Operational Climate Data Summary (OCDS) Analysis of Varying Climate Zones within the United States
Aaron Hurley
2024-04-15
Introduction
14th Weather Squadron Patch
The 14th Weather Squadron (14WS) is the US Air Force’s only climatological unit. They are tasked to “Collect, Protect, [and] Exploit” global weather data. They do this by ingesting weather observations from around the world and storing them for climatological analysis to support the DOD. They produce many kinds of products – including the Operational Climate Data Summary (OCDS). The OCDS is a product that is produced for nearly 4000 locations around the world. An example OCDS is provided in Appendix A (Download Appendix A - KIND_Climogram.pdf). Each product is currently comprised of the following monthly statistics calculated from the previous complete 10 years of data for that location:
- maximum (max) temperature [extreme],
- minimum (min) temperature [extreme],
- average max temperature,
- average min temperature,
- maximum precipitation (liquid and snow, where available) [extreme],
- average precipitation (liquid and snow, where available),
- monthly prevailing wind speed and direction [extreme],
- and max and min average relative humidity.
These monthly temperature, precipitation, wind, and humidity statistics are averaged at the 15th day of each month, whereas the maximum and minimum temperature are absolutes that are recorded. The OCDS is updated whenever necessary – typically on a monthly to quarterly basis, or when errors are discovered that slipped through the Quality Check process. The purpose of updating the OCDS is to recalculate each of the above metrics with the most recent data. By updating these metrics, current, relevant weather data is presented. These OCDSs are exceedingly useful for decision makers during the planning phase of exercises, training experiences, and operations as these events take place up to years in the future after planning.
Domain 1
Business Understanding
Recently, a request was sent to the 14WS to investigate the reality of producing a 10-day OCDS instead of monthly. Meaning, it would encompass a 10-day interval for averages instead of a month’s worth of data. This prompted several questions:
Are there periods of time across specific climates that can be identified as beneficial to use 10-day OCDS?
Does it matter whether we use 10 years, 20 years, or 30 years of data for operational purposes? That is, how much of a difference is there?
How are the stations changing with time? Do these statistical analyses match up with meteorological effects for the stations?
The purpose of this report is to explore the benefits and costs of performing such an analysis by taking a small subset of locations from the largest three climate regions within the United States. Benefits for this analysis include the ability to identify drastic changes in the climate that should be accounted for in relaying climate information. E.g., if Location A gets 4 inches of rain in one month, but they only average one rain event per month, then those 4 inches likely come all at once and it would stand out in an analysis of the data.
Costs to this analysis include the following:
The cost to rewrite the software to produce the shorter interval analysis,
The time spent by analysts to work on the OCDSs (approximately 20 hours per OCDS which would likely increase to approximately 25 hours per OCDS), and
The computing time by the servers (currently it takes approximately 1 hour to run an OCDS, but by creating 3x the number of intervals it would take approximately 3 hours to run).
Keeping these constraints in mind, the zones and specific locations are:
| Climate Zone | Location Name | Airport Identifier (ICAO) |
|---|---|---|
| Heavy Snow Winter | Robert’s Field, OR | KRDM |
| Heavy Snow Winter | Buffalo, NY | KBUF |
| Warm Summer, Cold Winter | Topeka, KS | KFOE |
| Warm Summer, Cold Winter | Madison, WI | KMSN |
| Warm Summer, Cold Winter | Tri-Cities Airport, TN | KTRI |
| Warm Summer, Cold Winter | Juneau, AK | PAJN |
| Arid | El Paso, TX | KELP |
| Arid | St. George, UT | KSGU |
By comparing these locations and seeing if there are commonalities in the assessment, there is the potential for some assumptions to be made that could make transitioning to a 10-day OCDS much simpler. If similarities are found in the 10-, 20-, and 30-year climatology data and the future OCDS data, then the similarities in the general trends can be used for other locations in the same climate zones, regardless of the varying topography and other physical features surrounding each location. As an example, if there are similar temperature fluctuations for all locations in a climate zone, we could make a generalization about this for other locations not modeled in this study within the same climate zone.
Additionally, we can use similarities to generalize how long the OCDS may remain valid for all locations within the climate zone.
The above locations were chosen due to varying topographical locations, varying geographical features, and availability of weather data. For example, for the Arid climate locations, St. George is in a valley surrounded by mountains, whereas El Paso has mountains only to the West. Additionally, El Paso is over 1000 feet higher in elevation than St. George. The availability of the weather data is important – some locations have a short amount of time that they have been sending weather data – e.g., 5 years. This would not be sufficient to determine climate trends (see note 1). Currently, the 14WS is using a minimum of 10 years of data to develop an OCDS for a site. However, for the scope of this project, we are looking at up to 30 years of daily weather data to determine which interval should be used for climate study.
About the Data
We will be looking at the following variables in our calculations:
variables Variable Description
1 Average Temperature The air temperature (Celsius)
2 Average Dewpoint Temperature The measure of moisture in the air at the station (Celsius)
3 Average Wind Direction The direction the wind comes from (degrees)
4 Average Wind Speed The 2-minute average speed of the wind (m/s)
5 Average Wind Gust Speed The 3-second maximum wind (m/s)
6 Average Cloud Ceiling The height of the lowest ceiling – a ceiling is 5/8 or more of sky coverage (ft above ground level)
7 Average Cloud Cover The amount of the sky that is covered by clouds (octants [eighths])
8 Average Visibility The distance you can see before 95% of light is scattered (meters)
9 Average Altimeter Setting The measure of the atmospheric pressure at the station (inHg)
10 Average Precipitation The average amount of precipitation (mm)It has already been shown by other climate studies that maximum and minimum temperatures are increasing. However, this project looks at a targeted application of this data, as previously explained. Thus, the monthly maximum and minimum temperatures are currently irrelevant.
The data was procured from the 14th Weather Squadron. Appendix B (Download Appendix B - Data Dictionary.htm) is the data dictionary for the datasets used. This includes a description of the data and bounds associated with it.
To summarize the data, there are 190 columns and 2,379,050 rows:
333,758 – KELP,
294,122 – KFOE,
413,290 – KBUF,
362,321 – KMSN,
333,637 – KSGU,
284,865 – KRDM,
318,127 – KTRI,
338,930 – PAJN.
Within these datasets, there are a total of 373,164,955 null values. These account for 88% of the entries from within the entire datset. The next section details the method we will use for eliminating these null values.
Planned Methodology
The planned methodology includes the following:
-
Download the complete weather observations for the eight desired locations.
-
Identify null values within the dataset.
-
Handle null values in the following methods:
-
Because we are looking at hourly observations, and because of the C1 (continuous with 1st derivative – see note 2 below) nature of the weather, one way to observe conditions is to consider that conditions exist until they change. This implies that we can continue using a previous value until a new value is measured, e.g., visibility. Visibility isn’t going to change from 7 miles to 10 feet back to 7 miles without something happening to explain it and an interim decrease in visibility is recorded.
-
Columns composed entirely of null values will be removed.
-
Remove columns that are not directly being used for analysis
-
For precipitation NULL values, replace them with 0s. (see note 3)
-
Visualize data and identify trends and outliers.
- Create models for each variable.
-
Use a variety of models to compare data for each climate zone.
-
Linear regression
-
Multivariate regression
-
Spline with varying degrees of freedom
-
Monthly – 11 knots + cubic natural spline (3) - 14 df
-
10-day – 36 knots + cubic natural spline (3) - 39 df
-
Daily – 366 knots + cubic natural spline (3) - 369 df
-
Identify times of year for each variable that are statistically different from the 10-, 20-, and 30-year data average.
-
Utilize summaries across 10-, 20-, and 30-year models to identify times when the models are and are not statistically significant.
-
Identify trend of data for each variable based on the 10-, 20-, and 30-year data.
- Perform ANOVA (Analysis of Variance) Tests on the spline models
- Compare ANOVA results and determine which subsets of data performed the best
- Rerun above analysis for different subsection of the data
- Split the datasets into 10-year intervals: 1994-2003, 2004-2013, and 2014-2023
- Generate spline regressions on each dataset in the above determined “best” model output
- Create a linear regression for each Julian Day
- Plot the slopes of the above regressions to determine how each variable is changing by Julian Day over each 10-year data period (see note 4)
The 14WS uses shorter periods of complete or incomplete data for other products, not OCDSs.
C1 is mathematical notation denoting the “smoothness” of a function. It means that given a function, you could take the 1st derivative of it at all points, but the resulting function may not be smooth. Weather is regarded as C1 smooth for most variables (not precipitation). This means that there are no disjointed variables, but there may be some “sharp” or “abrupt” curves that would not allow a 2nd derivative. An example would be that typically you wouldn’t see dense fog and low cloud cover – then 15 minutes later completely clear conditions, and 15 minutes after that back to dense fog and low cloud cover. That would be disjointed and not smooth. You would expect a gradual improvement or worsening of conditions with time – hence the C1 assumptions.
Imputing 0s in for NULL precipitation amounts does increase the risk of miscalculating the amount of precipitation received on any given day. However, this risk is limited in impact because it creates a minimum amount received. Thus, we will risk over estimating the precipitation amounts. This forms a conservative basis when looking at precipitation trends.
The initial desire was to use the code from a recent publication that shows promise in climate forecasting to run future forecasts for these sites. However, it was written in MATLAB and there are no simple ways to convert this within the project timeframe to R or Python. Therefore, we will omit this desired portion of the project, but instead use a statistical forecast to determine whether there is utility in either switching or continuing with the current OCDS process.
Notes:
Domain 2
Data Understanding
Data Summaries:
Robert’s Field, OR
summary(krdm_data)## PLATFORMID NETWORKTYPE OBSERVATIONTIME REPORTTYPECODE
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## Mean :44.25 Mean :-121.1 Mean : 6.445 Mean :1
## 3rd Qu.:44.25 3rd Qu.:-121.1 3rd Qu.:10.000 3rd Qu.:1
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## VISIBILITYTYPE VISIBILITYTYPEQC AIRTEMPERATURE AIRTEMPERATUREQC
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## 3rd Qu.: 7.00
## Max. :10.00
## NA's :49936
## CLOUDCOVERQC CLOUDCOVERLO CLOUDCOVERLOQC CLOUDBASEHEIGHT
## Min. :0.00 Min. :0.00 Min. :0.00 Min. : 0
## 1st Qu.:1.00 1st Qu.:4.00 1st Qu.:0.00 1st Qu.: 457
## Median :1.00 Median :7.00 Median :0.00 Median :1341
## Mean :1.08 Mean :5.92 Mean :0.19 Mean :1373
## 3rd Qu.:1.00 3rd Qu.:8.00 3rd Qu.:0.00 3rd Qu.:1981
## Max. :4.00 Max. :8.00 Max. :1.00 Max. :3658
## NA's :46078 NA's :284194 NA's :279696 NA's :239857
## CLOUDBASEHEIGHTQC CLOUDTYPELO CLOUDTYPELOQC CLOUDTYPEMID
## Min. :1 Min. :0.00 Min. :0.00 Min. :0.00
## 1st Qu.:1 1st Qu.:3.00 1st Qu.:0.00 1st Qu.:3.00
## Median :1 Median :5.00 Median :0.00 Median :5.00
## Mean :1 Mean :4.93 Mean :0.09 Mean :4.97
## 3rd Qu.:1 3rd Qu.:7.00 3rd Qu.:0.00 3rd Qu.:7.00
## Max. :1 Max. :9.00 Max. :1.00 Max. :9.00
## NA's :239857 NA's :284792 NA's :280294 NA's :284775
## CLOUDTYPEMIDQC CLOUDTYPEHI CLOUDTYPEHIQC SUNSHINE
## Min. :0.00 Mode:logical Min. :0 Mode:logical
## 1st Qu.:0.00 NA's:285230 1st Qu.:0 NA's:285230
## Median :0.00 Median :0
## Mean :0.09 Mean :0
## 3rd Qu.:0.00 3rd Qu.:0
## Max. :1.00 Max. :0
## NA's :280277 NA's :280732
## SURFACECODE SURFACECODEQC SOILTEMPERATURE SOILTEMPERATUREQC
## Min. :1 Mode:logical Mode:logical Mode:logical
## 1st Qu.:1 NA's:285230 NA's:285230 NA's:285230
## Median :1
## Mean :1
## 3rd Qu.:1
## Max. :1
## NA's :285227
## SOILDEPTH OBSERVATIONPERIODSOILT OBSERVATIONPERIODSOILTQC
## Mode:logical Mode:logical Mode:logical
## NA's:285230 NA's:285230 NA's:285230
##
##
##
##
##
## ALTIMETERSETTING ALTIMETERSETTINGQC STATIONPRESSURE STATIONPRESSUREQC
## Min. : 980.4 Min. :0.000 Mode:logical Min. :0
## 1st Qu.:1013.5 1st Qu.:1.000 NA's:285230 1st Qu.:0
## Median :1017.3 Median :1.000 Median :0
## Mean :1016.9 Mean :1.001 Mean :0
## 3rd Qu.:1021.0 3rd Qu.:1.000 3rd Qu.:0
## Max. :1041.3 Max. :5.000 Max. :0
## NA's :390 NA's :387 NA's :271019
## PRESSURETENDENCY PRESSURETENDENCYQC PRESSURE3HOURCHG PRESSURE3HOURCHGQC
## Min. :0.00 Min. :0.00 Min. :-6.40 Min. :0.00
## 1st Qu.:1.00 1st Qu.:1.00 1st Qu.: 0.30 1st Qu.:1.00
## Median :3.00 Median :1.00 Median : 0.70 Median :1.00
## Mean :4.13 Mean :0.85 Mean : 0.82 Mean :0.86
## 3rd Qu.:7.00 3rd Qu.:1.00 3rd Qu.: 1.30 3rd Qu.:1.00
## Max. :8.00 Max. :5.00 Max. :13.00 Max. :5.00
## NA's :223796 NA's :208978 NA's :223164 NA's :208591
## PRESSURE24HOURCHG PRESSURE24HOURCHGQC PRESSURETREND ISOBARICSURFACE
## Min. :0 Min. :0.00 Mode:logical Mode:logical
## 1st Qu.:0 1st Qu.:0.00 NA's:285230 NA's:285230
## Median :0 Median :0.00
## Mean :0 Mean :0.05
## 3rd Qu.:0 3rd Qu.:0.00
## Max. :0 Max. :1.00
## NA's :284494 NA's :270283
## ISOBARICSURFACEQC ISOBARICSURFACEHEIGHT ISOBARICSURFACEHEIGHTQC SEASURFACETEMP
## Mode:logical Mode:logical Mode:logical Mode:logical
## NA's:285230 NA's:285230 NA's:285230 NA's:285230
##
##
##
##
##
## SEASURFACETEMPQC REMARKSYN REMARKMET REMARKAWY
## Min. :5 Mode:logical Length:285230 Length:285230
## 1st Qu.:5 NA's:285230 Class :character Class :character
## Median :5 Mode :character Mode :character
## Mean :5
## 3rd Qu.:5
## Max. :5
## NA's :285227
## HORIZONTALDATUM VERTICALDATUM LIGHTNINGFREQUENCY
## Length:285230 Length:285230 Mode:logical
## Class :character Class :character NA's:285230
## Mode :character Mode :character
##
##
##
##
## RECEIPTDTG INSERTIONTIME BLKSTN
## Min. :20130500000000 Length:285230 Min. :726835
## 1st Qu.:20160100000000 Class :character 1st Qu.:726835
## Median :20180900000000 Mode :character Median :726835
## Mean :20182258996100 Mean :726847
## 3rd Qu.:20210400000000 3rd Qu.:726835
## Max. :20231200000000 Max. :726920
## NA's :182347 NA's :106113Buffalo, NY
summary(kbuf_data)## PLATFORMID NETWORKTYPE OBSERVATIONTIME REPORTTYPECODE
## Length:413352 Length:413352 Length:413352 Length:413352
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## LATITUDE LONGITUDE MONTH SECURITYID
## Min. : 1.00 Length:413352 Min. : 1.000 Min. : 1.000
## 1st Qu.:42.93 Class :character 1st Qu.: 3.000 1st Qu.: 1.000
## Median :42.93 Mode :character Median : 6.000 Median : 1.000
## Mean :42.93 Mean : 6.384 Mean : 1.001
## 3rd Qu.:42.94 3rd Qu.:10.000 3rd Qu.: 1.000
## Max. :42.94 Max. :12.000 Max. :215.000
## NA's :1 NA's :2 NA's :1
## DISTRIBUTIONCD STATIONMODE PLATFORMHEIGHT CALLLETTER
## Length:413352 Min. : 0.00 Min. :-1000.0 Length:413352
## Class :character 1st Qu.: 0.00 1st Qu.: 215.0 Class :character
## Mode :character Median : 0.00 Median : 218.0 Mode :character
## Mean : 0.22 Mean : 208.3
## 3rd Qu.: 0.00 3rd Qu.: 220.7
## Max. :182.00 Max. : 270.0
## NA's :254385 NA's :1
## VERSION WINDDIRECTION WINDDIRECTIONQC WINDCONDITIONS
## Length:413352 Min. : 10.0 Min. :0.000 Length:413352
## Class :character 1st Qu.:150.0 1st Qu.:1.000 Class :character
## Mode :character Median :220.0 Median :1.000 Mode :character
## Mean :202.2 Mean :0.996
## 3rd Qu.:260.0 3rd Qu.:1.000
## Max. :360.0 Max. :6.100
## NA's :29998 NA's :25421
## WINDCONDITIONSQC WINDSPEED WINDSPEEDQC STARTDIRECTION
## Min. :1.00 Min. : 0.000 Min. :0.000 Min. : 10.0
## 1st Qu.:1.00 1st Qu.: 2.600 1st Qu.:1.000 1st Qu.:185.0
## Median :1.00 Median : 4.100 Median :1.000 Median :220.0
## Mean :1.01 Mean : 4.566 Mean :1.003 Mean :214.3
## 3rd Qu.:1.00 3rd Qu.: 6.200 3rd Qu.:1.000 3rd Qu.:270.0
## Max. :5.00 Max. :36.100 Max. :5.000 Max. :360.0
## NA's :262276 NA's :5682 NA's :5896 NA's :413081
## ENDDIRECTION WINDGUSTSPEED WINDGUSTSPEEDQC WINDMEASUREMENTMODE
## Min. : 10 Min. : 0.0 Min. :0 Length:413352
## 1st Qu.:130 1st Qu.: 10.3 1st Qu.:1 Class :character
## Median :270 Median : 11.8 Median :1 Mode :character
## Mean :222 Mean : 12.3 Mean :1
## 3rd Qu.:310 3rd Qu.: 13.9 3rd Qu.:1
## Max. :360 Max. :900.0 Max. :4
## NA's :413081 NA's :346504 NA's :328946
## CLOUDCEILING CLOUDCEILINGQC CEILINGDETERMINATION CEILINGDETERMINATIONQC
## Min. : 0 Length:413352 Length:413352 Min. : 0.0
## 1st Qu.: 690 Class :character Class :character 1st Qu.: 0.0
## Median : 1950 Mode :character Mode :character Median : 0.0
## Mean : 8168 Mean : 2.2
## 3rd Qu.:22000 3rd Qu.: 0.0
## Max. :22000 Max. :48280.0
## NA's :57060 NA's :390841
## CLOUDCAVOK CLOUDCAVOKQC VISIBILITY VISIBILITYQC
## Length:413352 Length:413352 Min. : 0 Min. :1.000
## Class :character Class :character 1st Qu.: 11265 1st Qu.:1.000
## Mode :character Mode :character Median : 16093 Median :1.000
## Mean : 13622 Mean :1.005
## 3rd Qu.: 16093 3rd Qu.:1.000
## Max. :112654 Max. :4.000
## NA's :5418 NA's :5435
## VISIBILITYTYPE VISIBILITYTYPEQC AIRTEMPERATURE AIRTEMPERATUREQC
## Length:413352 Min. :-5.6 Min. :-81.100 Min. : 0.000
## Class :character 1st Qu.: 1.0 1st Qu.: 0.000 1st Qu.: 1.000
## Mode :character Median : 1.0 Median : 8.300 Median : 1.000
## Mean : 1.0 Mean : 8.607 Mean : 1.008
## 3rd Qu.: 1.0 3rd Qu.: 18.000 3rd Qu.: 1.000
## Max. : 4.0 Max. : 36.100 Max. :1024.000
## NA's :5625 NA's :8876 NA's :8870
## DEWPOINTTEMPERATURE DEWPOINTTEMPERATUREQC SEALEVELPRESSURE SEALEVELPRESSUREQC
## Min. :-27.800 Min. :0.000 Min. : 980.5 Min. :0.00
## 1st Qu.: -4.000 1st Qu.:1.000 1st Qu.:1011.4 1st Qu.:1.00
## Median : 3.300 Median :1.000 Median :1016.3 Median :1.00
## Mean : 3.746 Mean :1.006 Mean :1016.2 Mean :0.99
## 3rd Qu.: 12.200 3rd Qu.:1.000 3rd Qu.:1021.2 3rd Qu.:1.00
## Max. : 27.100 Max. :5.000 Max. :1046.0 Max. :5.00
## NA's :8991 NA's :8960 NA's :79351 NA's :75954
## OBSERVATIONPERIODPP1 OBSERVATIONPERIODPP1QC PRECIPAMOUNT1 PRECIPAMOUNT1QC
## Min. : 0.00 Min. :1.0 Min. : 0.00 Min. :1.0
## 1st Qu.: 1.00 1st Qu.:1.0 1st Qu.: 0.00 1st Qu.:1.0
## Median : 1.00 Median :1.0 Median : 0.00 Median :1.0
## Mean : 2.95 Mean :1.3 Mean : 0.88 Mean :1.3
## 3rd Qu.: 6.00 3rd Qu.:1.0 3rd Qu.: 0.50 3rd Qu.:1.0
## Max. :24.00 Max. :4.0 Max. :509.00 Max. :4.0
## NA's :281866 NA's :358881 NA's :281029 NA's :350564
## PRECIPCONDITION1 PRECIPCONDITION1QC OBSERVATIONPERIODPP2
## Min. :0.00 Min. :1.0 Min. : 0.0
## 1st Qu.:2.00 1st Qu.:1.0 1st Qu.: 3.0
## Median :2.00 Median :1.0 Median : 6.0
## Mean :2.23 Mean :1.2 Mean : 9.1
## 3rd Qu.:2.00 3rd Qu.:1.0 3rd Qu.:24.0
## Max. :3.00 Max. :4.0 Max. :24.0
## NA's :312472 NA's :351047 NA's :391683
## OBSERVATIONPERIODPP2QC PRECIPAMOUNT2 PRECIPAMOUNT2QC PRECIPCONDITION2
## Min. :1 Min. : 0.0 Min. :0.0 Min. :1.0
## 1st Qu.:1 1st Qu.: 0.0 1st Qu.:1.0 1st Qu.:2.0
## Median :1 Median : 0.5 Median :1.0 Median :2.0
## Mean :1 Mean : 2.9 Mean :0.9 Mean :2.5
## 3rd Qu.:1 3rd Qu.: 2.8 3rd Qu.:1.0 3rd Qu.:3.0
## Max. :4 Max. :600.0 Max. :4.0 Max. :3.0
## NA's :405670 NA's :391469 NA's :404100 NA's :400089
## PRECIPCONDITION2QC OBSERVATIONPERIODPP3 OBSERVATIONPERIODPP3QC
## Min. :1 Min. : 1.0 Min. :1
## 1st Qu.:1 1st Qu.:24.0 1st Qu.:1
## Median :1 Median :24.0 Median :1
## Mean :1 Mean :19.1 Mean :1
## 3rd Qu.:1 3rd Qu.:24.0 3rd Qu.:1
## Max. :4 Max. :24.0 Max. :1
## NA's :404102 NA's :411593 NA's :412608
## PRECIPAMOUNT3 PRECIPAMOUNT3QC PRECIPCONDITION3 PRECIPCONDITION3QC
## Min. : 0.0 Min. :1 Min. :1.0 Min. :1
## 1st Qu.: 0.5 1st Qu.:1 1st Qu.:3.0 1st Qu.:1
## Median : 2.5 Median :1 Median :3.0 Median :1
## Mean : 6.3 Mean :1 Mean :2.8 Mean :1
## 3rd Qu.: 7.9 3rd Qu.:1 3rd Qu.:3.0 3rd Qu.:1
## Max. :99.1 Max. :2 Max. :3.0 Max. :1
## NA's :411558 NA's :412336 NA's :412202 NA's :412333
## OBSERVATIONPERIODPP4 OBSERVATIONPERIODPP4QC PRECIPAMOUNT4 PRECIPAMOUNT4QC
## Min. : 1.0 Min. :1 Min. : 0.2 Min. :1
## 1st Qu.:24.0 1st Qu.:1 1st Qu.: 0.7 1st Qu.:1
## Median :24.0 Median :1 Median : 2.4 Median :1
## Mean :23.4 Mean :1 Mean : 6.8 Mean :1
## 3rd Qu.:24.0 3rd Qu.:1 3rd Qu.: 7.1 3rd Qu.:1
## Max. :24.0 Max. :1 Max. :46.7 Max. :2
## NA's :413316 NA's :413341 NA's :413316 NA's :413316
## PRECIPCONDITION4 PRECIPCONDITION4QC PRECIPHISTDUR PRECIPHISTDURQC
## Min. :3 Min. :1 Min. :0.0 Min. :0
## 1st Qu.:3 1st Qu.:1 1st Qu.:1.0 1st Qu.:0
## Median :3 Median :1 Median :2.0 Median :0
## Mean :3 Mean :1 Mean :1.7 Mean :0
## 3rd Qu.:3 3rd Qu.:1 3rd Qu.:2.0 3rd Qu.:0
## Max. :3 Max. :1 Max. :3.0 Max. :1
## NA's :413316 NA's :413316 NA's :412381 NA's :405549
## PRECIPHISTCHAR PRECIPHISTCHARQC PRECIPDISC PRECIPDISCQC
## Length:413352 Mode:logical Min. :0 Mode:logical
## Class :character NA's:413352 1st Qu.:1 NA's:413352
## Mode :character Median :1
## Mean :1
## 3rd Qu.:1
## Max. :5
## NA's :351951
## PRECIPBOGUS PRECIPBOGUSQC PRECIPAMOUNTSD PRECIPAMOUNTSDQC
## Min. : 0.0 Mode:logical Min. : 0.0 Min. :1
## 1st Qu.: 0.0 NA's:413352 1st Qu.: 3.0 1st Qu.:1
## Median : 0.0 Median : 8.0 Median :1
## Mean : 0.4 Mean : 11.4 Mean :1
## 3rd Qu.: 0.0 3rd Qu.: 15.0 3rd Qu.:1
## Max. :17.0 Max. :996.0 Max. :5
## NA's :394813 NA's :394974 NA's :403229
## PRECIPCONDITIONSD PRECIPCONDITIONSDQC DEPTHWTREQUIV DEPTHWTREQUIVQC
## Min. :0.0 Min. :0 Min. : 0.0 Min. :4
## 1st Qu.:0.0 1st Qu.:1 1st Qu.: 0.0 1st Qu.:4
## Median :3.0 Median :1 Median : 51.0 Median :4
## Mean :2.1 Mean :1 Mean :108.6 Mean :4
## 3rd Qu.:3.0 3rd Qu.:1 3rd Qu.:152.0 3rd Qu.:4
## Max. :3.0 Max. :1 Max. :740.0 Max. :4
## NA's :403470 NA's :403470 NA's :403147 NA's :412692
## DEPTHWECOND DEPTHWECONDQC HAILSIZE PRECIPAMOUNTSF1
## Mode:logical Mode:logical Min. :0.1 Min. : 0.0
## NA's:413352 NA's:413352 1st Qu.:0.1 1st Qu.: 0.0
## Median :0.1 Median : 0.0
## Mean :0.4 Mean : 0.7
## 3rd Qu.:0.6 3rd Qu.: 0.0
## Max. :1.0 Max. :47.0
## NA's :413345 NA's :409082
## PRECIPAMOUNTSF1QC PRECIPCONDITIONSF1 PRECIPCONDITIONSF1QC OBSERVATIONPERIODSF1
## Min. :0 Min. :0.0 Min. :0 Min. :1.0
## 1st Qu.:1 1st Qu.:0.0 1st Qu.:1 1st Qu.:1.0
## Median :1 Median :0.0 Median :1 Median :1.0
## Mean :1 Mean :0.6 Mean :1 Mean :1.1
## 3rd Qu.:1 3rd Qu.:0.0 3rd Qu.:1 3rd Qu.:1.0
## Max. :1 Max. :3.0 Max. :1 Max. :6.0
## NA's :409503 NA's :409505 NA's :409503 NA's :413071
## OBSERVATIONPERIODSF1QC PRECIPAMOUNTSF2 PRECIPAMOUNTSF2QC PRECIPCONDITIONSF2
## Min. :0 Min. : 0.0 Min. :1 Min. :0.0
## 1st Qu.:1 1st Qu.: 0.0 1st Qu.:1 1st Qu.:0.0
## Median :1 Median : 0.0 Median :1 Median :0.0
## Mean :1 Mean : 1.3 Mean :1 Mean :1.2
## 3rd Qu.:1 3rd Qu.: 1.0 3rd Qu.:1 3rd Qu.:3.0
## Max. :1 Max. :47.0 Max. :1 Max. :3.0
## NA's :413074 NA's :412559 NA's :412559 NA's :412559
## PRECIPCONDITIONSF2QC OBSERVATIONPERIODSF2 OBSERVATIONPERIODSF2QC
## Min. :1 Min. :1 Min. :0.0
## 1st Qu.:1 1st Qu.:1 1st Qu.:0.0
## Median :1 Median :1 Median :1.0
## Mean :1 Mean :1 Mean :0.6
## 3rd Qu.:1 3rd Qu.:1 3rd Qu.:1.0
## Max. :1 Max. :1 Max. :1.0
## NA's :412559 NA's :413347 NA's :413344
## PRECIPAMOUNTSF3 PRECIPAMOUNTSF3QC PRECIPCONDITIONSF3 PRECIPCONDITIONSF3QC
## Min. : 0 Min. :1 Min. :0.0 Min. :1
## 1st Qu.: 0 1st Qu.:1 1st Qu.:0.0 1st Qu.:1
## Median : 0 Median :1 Median :0.0 Median :1
## Mean : 1 Mean :1 Mean :0.9 Mean :1
## 3rd Qu.: 1 3rd Qu.:1 3rd Qu.:3.0 3rd Qu.:1
## Max. :14 Max. :1 Max. :3.0 Max. :1
## NA's :413122 NA's :413122 NA's :413122 NA's :413122
## OBSERVATIONPERIODSF3 OBSERVATIONPERIODSF3QC PRECIPAMOUNTSF4 PRECIPAMOUNTSF4QC
## Min. :1 Min. :1 Min. :0 Min. :1
## 1st Qu.:1 1st Qu.:1 1st Qu.:0 1st Qu.:1
## Median :1 Median :1 Median :0 Median :1
## Mean :1 Mean :1 Mean :1 Mean :1
## 3rd Qu.:1 3rd Qu.:1 3rd Qu.:1 3rd Qu.:1
## Max. :1 Max. :1 Max. :9 Max. :1
## NA's :413349 NA's :413349 NA's :413330 NA's :413330
## PRECIPCONDITIONSF4 PRECIPCONDITIONSF4QC OBSERVATIONPERIODSF4
## Min. :0.0 Min. :0 Min. :1
## 1st Qu.:0.0 1st Qu.:1 1st Qu.:1
## Median :0.0 Median :1 Median :1
## Mean :1.1 Mean :1 Mean :1
## 3rd Qu.:3.0 3rd Qu.:1 3rd Qu.:1
## Max. :3.0 Max. :1 Max. :1
## NA's :413330 NA's :413329 NA's :413347
## OBSERVATIONPERIODSF4QC PRESENTMANUAL1 PRESENTMANUAL1QC PRESENTMANUAL2
## Min. :1 Min. : 0.00 Min. :0.00 Min. : 0.00
## 1st Qu.:1 1st Qu.: 0.00 1st Qu.:1.00 1st Qu.: 0.00
## Median :1 Median :10.00 Median :1.00 Median : 0.00
## Mean :1 Mean :33.83 Mean :0.97 Mean : 8.67
## 3rd Qu.:1 3rd Qu.:71.00 3rd Qu.:1.00 3rd Qu.:10.00
## Max. :1 Max. :99.00 Max. :5.00 Max. :97.00
## NA's :413348 NA's :256381 NA's :251364 NA's :315660
## PRESENTMANUAL2QC PRESENTMANUAL3 PRESENTMANUAL3QC PRESENTMANUAL4
## Min. :0.00 Min. : 0.0 Min. :0 Min. : 0.0
## 1st Qu.:1.00 1st Qu.: 0.0 1st Qu.:1 1st Qu.: 0.0
## Median :1.00 Median : 0.0 Median :1 Median : 0.0
## Mean :0.98 Mean : 0.8 Mean :1 Mean : 6.9
## 3rd Qu.:1.00 3rd Qu.: 0.0 3rd Qu.:1 3rd Qu.:10.0
## Max. :4.00 Max. :90.0 Max. :1 Max. :61.0
## NA's :280631 NA's :338986 NA's :295832 NA's :413093
## PRESENTMANUAL4QC PRESENTMANUAL5 PRESENTMANUAL5QC PRESENTMANUAL6
## Min. :1 Min. : 0.0 Min. :1 Min. :0.0
## 1st Qu.:1 1st Qu.: 0.0 1st Qu.:1 1st Qu.:0.2
## Median :1 Median : 0.5 Median :1 Median :0.5
## Mean :1 Mean :15.3 Mean :1 Mean :0.5
## 3rd Qu.:1 3rd Qu.: 7.8 3rd Qu.:1 3rd Qu.:0.8
## Max. :1 Max. :81.0 Max. :1 Max. :1.0
## NA's :304877 NA's :413346 NA's :304929 NA's :413350
## PRESENTMANUAL6QC PRESENTMANUAL7 PRESENTMANUAL7QC PRESENTAUTOMATED1
## Min. :1 Mode:logical Min. :1 Min. : 4.0
## 1st Qu.:1 NA's:413352 1st Qu.:1 1st Qu.:61.0
## Median :1 Median :1 Median :63.0
## Mean :1 Mean :1 Mean :60.7
## 3rd Qu.:1 3rd Qu.:1 3rd Qu.:71.0
## Max. :1 Max. :1 Max. :96.0
## NA's :304930 NA's :304930 NA's :375711
## PRESENTAUTOMATED1QC PRESENTAUTOMATED2 PRESENTAUTOMATED2QC PRESENTAUTOMATED3
## Min. :0.0 Min. : 4.0 Min. :0.0 Min. :10
## 1st Qu.:0.0 1st Qu.:10.0 1st Qu.:1.0 1st Qu.:10
## Median :1.0 Median :10.0 Median :1.0 Median :29
## Mean :0.8 Mean :14.4 Mean :1.1 Mean :31
## 3rd Qu.:1.0 3rd Qu.:10.0 3rd Qu.:1.0 3rd Qu.:54
## Max. :4.0 Max. :95.0 Max. :4.0 Max. :68
## NA's :363033 NA's :398406 NA's :398391 NA's :412971
## PRESENTAUTOMATED3QC PASTMANUAL1 PASTMANUAL1QC WXPASTPERIOD1
## Min. :1 Min. :0.0 Min. :0.0 Min. :1
## 1st Qu.:1 1st Qu.:6.0 1st Qu.:0.0 1st Qu.:6
## Median :4 Median :8.0 Median :0.0 Median :6
## Mean :3 Mean :6.5 Mean :0.2 Mean :6
## 3rd Qu.:4 3rd Qu.:8.0 3rd Qu.:0.0 3rd Qu.:6
## Max. :4 Max. :9.0 Max. :1.0 Max. :6
## NA's :412971 NA's :412119 NA's :406196 NA's :412120
## WXPASTPERIOD1QC PASTMANUAL2 PASTMANUAL2QC WXPASTPERIOD2
## Min. :1 Min. :0.0 Min. :0.0 Min. :1
## 1st Qu.:1 1st Qu.:1.0 1st Qu.:0.0 1st Qu.:6
## Median :1 Median :2.0 Median :0.0 Median :6
## Mean :1 Mean :3.3 Mean :0.4 Mean :6
## 3rd Qu.:1 3rd Qu.:6.0 3rd Qu.:1.0 3rd Qu.:6
## Max. :1 Max. :9.0 Max. :1.0 Max. :6
## NA's :412120 NA's :412119 NA's :409898 NA's :412120
## WXPASTPERIOD2QC PASTAUTOMATED1 PASTAUTOMATED1QC WXPASTAUTOPERIOD1
## Min. :1 Mode:logical Min. :0 Mode:logical
## 1st Qu.:1 NA's:413352 1st Qu.:0 NA's:413352
## Median :1 Median :0
## Mean :1 Mean :0
## 3rd Qu.:1 3rd Qu.:0
## Max. :1 Max. :0
## NA's :412120 NA's :407429
## WXPASTAUTOPERIOD1QC PASTAUTOMATED2 PASTAUTOMATED2QC WXPASTAUTOPERIOD2
## Mode:logical Mode:logical Min. :0 Mode:logical
## NA's:413352 NA's:413352 1st Qu.:0 NA's:413352
## Median :0
## Mean :0
## 3rd Qu.:0
## Max. :0
## NA's :411131
## WXPASTAUTOPERIOD2QC RUNWAYENDBEARING RUNWAYDESIGNATOR RUNWAYVISUALRANGE
## Mode:logical Min. : 5.0 Length:413352 Min. : 30
## NA's:413352 1st Qu.:23.0 Class :character 1st Qu.: 853
## Median :23.0 Mode :character Median :1372
## Mean :22.5 Mean :1246
## 3rd Qu.:23.0 3rd Qu.:1676
## Max. :32.0 Max. :6000
## NA's :401854 NA's :401855
## CLOUDCOVER CLOUDCOVERQC CLOUDCOVERLO CLOUDCOVERLOQC
## Min. : 0.00 Min. :0.00 Min. :0.0 Min. :0.0
## 1st Qu.: 4.00 1st Qu.:1.00 1st Qu.:0.0 1st Qu.:0.0
## Median : 7.00 Median :1.00 Median :0.0 Median :0.0
## Mean : 5.64 Mean :1.18 Mean :1.5 Mean :0.3
## 3rd Qu.: 8.00 3rd Qu.:1.00 3rd Qu.:2.0 3rd Qu.:1.0
## Max. :10.00 Max. :4.00 Max. :9.0 Max. :1.0
## NA's :100387 NA's :88673 NA's :406627 NA's :391002
## CLOUDBASEHEIGHT CLOUDBASEHEIGHTQC CLOUDTYPELO CLOUDTYPELOQC
## Min. : 0 Min. :0.00 Min. :0.0 Min. :0.0
## 1st Qu.: 450 1st Qu.:1.00 1st Qu.:0.0 1st Qu.:0.0
## Median : 823 Median :1.00 Median :4.0 Median :1.0
## Mean : 1389 Mean :0.99 Mean :2.9 Mean :0.7
## 3rd Qu.: 1524 3rd Qu.:1.00 3rd Qu.:5.0 3rd Qu.:1.0
## Max. :10668 Max. :1.00 Max. :9.0 Max. :1.0
## NA's :250918 NA's :248868 NA's :378424 NA's :362799
## CLOUDTYPEMID CLOUDTYPEMIDQC CLOUDTYPEHI CLOUDTYPEHIQC
## Min. :0.0 Min. :0.0 Min. :0.0 Min. :0.0
## 1st Qu.:0.0 1st Qu.:0.0 1st Qu.:0.0 1st Qu.:0.0
## Median :0.0 Median :1.0 Median :0.0 Median :1.0
## Mean :2.8 Mean :0.6 Mean :2.5 Mean :0.6
## 3rd Qu.:7.0 3rd Qu.:1.0 3rd Qu.:7.0 3rd Qu.:1.0
## Max. :9.0 Max. :1.0 Max. :9.0 Max. :1.0
## NA's :385880 NA's :370255 NA's :390349 NA's :374724
## SUNSHINE SURFACECODE SURFACECODEQC SOILTEMPERATURE
## Mode:logical Min. :1 Mode:logical Mode:logical
## NA's:413352 1st Qu.:1 NA's:413352 NA's:413352
## Median :1
## Mean :1
## 3rd Qu.:1
## Max. :1
## NA's :413349
## SOILTEMPERATUREQC SOILDEPTH OBSERVATIONPERIODSOILT
## Mode:logical Min. :1023 Min. :1
## NA's:413352 1st Qu.:1023 1st Qu.:1
## Median :1023 Median :1
## Mean :1023 Mean :1
## 3rd Qu.:1023 3rd Qu.:1
## Max. :1023 Max. :1
## NA's :413351 NA's :413351
## OBSERVATIONPERIODSOILTQC ALTIMETERSETTING ALTIMETERSETTINGQC STATIONPRESSURE
## Min. :997.6 Min. : 1 Min. :0.00 Min. : 954.0
## 1st Qu.:997.6 1st Qu.:1010 1st Qu.:1.00 1st Qu.: 984.9
## Median :997.6 Median :1015 Median :1.00 Median : 989.6
## Mean :997.6 Mean :1015 Mean :0.99 Mean : 989.4
## 3rd Qu.:997.6 3rd Qu.:1020 3rd Qu.:1.00 3rd Qu.: 994.4
## Max. :997.6 Max. :1044 Max. :5.00 Max. :1018.2
## NA's :413351 NA's :68695 NA's :64328 NA's :275021
## STATIONPRESSUREQC PRESSURETENDENCY PRESSURETENDENCYQC PRESSURE3HOURCHG
## Min. :0.00 Min. :0.00 Min. :0.00 Min. :-10.80
## 1st Qu.:1.00 1st Qu.:2.00 1st Qu.:1.00 1st Qu.: 0.20
## Median :1.00 Median :5.00 Median :1.00 Median : 0.70
## Mean :1.05 Mean :4.38 Mean :0.89 Mean : 0.77
## 3rd Qu.:1.00 3rd Qu.:7.00 3rd Qu.:1.00 3rd Qu.: 1.40
## Max. :5.00 Max. :8.00 Max. :5.00 Max. : 50.00
## NA's :258403 NA's :286367 NA's :269469 NA's :281170
## PRESSURE3HOURCHGQC PRESSURE24HOURCHG PRESSURE24HOURCHGQC PRESSURETREND
## Min. :0.0 Min. :0 Min. :0 Mode:logical
## 1st Qu.:1.0 1st Qu.:0 1st Qu.:0 NA's:413352
## Median :1.0 Median :0 Median :0
## Mean :0.9 Mean :0 Mean :0
## 3rd Qu.:1.0 3rd Qu.:0 3rd Qu.:0
## Max. :5.0 Max. :0 Max. :1
## NA's :265382 NA's :412488 NA's :391547
## ISOBARICSURFACE ISOBARICSURFACEQC ISOBARICSURFACEHEIGHT
## Min. :1.0 Mode:logical Min. : 888
## 1st Qu.:1.0 NA's:413352 1st Qu.: 945
## Median :1.0 Median :1002
## Mean :1.3 Mean :1002
## 3rd Qu.:1.5 3rd Qu.:1059
## Max. :2.0 Max. :1116
## NA's :413349 NA's :413350
## ISOBARICSURFACEHEIGHTQC SEASURFACETEMP SEASURFACETEMPQC REMARKSYN
## Mode:logical Mode:logical Min. :5 Length:413352
## NA's:413352 NA's:413352 1st Qu.:5 Class :character
## Median :5 Mode :character
## Mean :5
## 3rd Qu.:5
## Max. :5
## NA's :413349
## REMARKMET REMARKAWY HORIZONTALDATUM VERTICALDATUM
## Length:413352 Length:413352 Length:413352 Length:413352
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## LIGHTNINGFREQUENCY RECEIPTDTG INSERTIONTIME
## Min. :725280 Min. :20130500000000 Length:413352
## 1st Qu.:725280 1st Qu.:20151200000000 Class :character
## Median :725280 Median :20180900000000 Mode :character
## Mean :725280 Mean :20182352548700
## 3rd Qu.:725280 3rd Qu.:20210500000000
## Max. :725280 Max. :20231200000000
## NA's :413351 NA's :262060
## BLKSTN
## Min. :725280
## 1st Qu.:725280
## Median :725280
## Mean :725280
## 3rd Qu.:725280
## Max. :725280
## NA's :158969Topeka, KS
summary(kfoe_data)## PLATFORMID NETWORKTYPE OBSERVATIONTIME REPORTTYPECODE
## Length:294162 Length:294162 Length:294162 Length:294162
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## LATITUDE LONGITUDE MONTH SECURITYID
## Min. :38.94 Min. :-95.67 Min. : 1.000 Min. :1
## 1st Qu.:38.95 1st Qu.:-95.67 1st Qu.: 3.000 1st Qu.:1
## Median :38.95 Median :-95.66 Median : 6.000 Median :1
## Mean :38.95 Mean :-95.66 Mean : 6.447 Mean :1
## 3rd Qu.:38.95 3rd Qu.:-95.65 3rd Qu.: 9.000 3rd Qu.:1
## Max. :38.95 Max. :-95.65 Max. :12.000 Max. :1
## NA's :3 NA's :3 NA's :3 NA's :3
## DISTRIBUTIONCD STATIONMODE PLATFORMHEIGHT CALLLETTER
## Length:294162 Min. :0.00 Min. :328.6 Length:294162
## Class :character 1st Qu.:0.00 1st Qu.:328.6 Class :character
## Mode :character Median :0.00 Median :329.0 Mode :character
## Mean :0.03 Mean :329.7
## 3rd Qu.:0.00 3rd Qu.:329.0
## Max. :1.00 Max. :337.0
## NA's :177319 NA's :3
## VERSION WINDDIRECTION WINDDIRECTIONQC WINDCONDITIONS
## Length:294162 Min. : 9.0 Min. :0.000 Length:294162
## Class :character 1st Qu.:120.0 1st Qu.:1.000 Class :character
## Mode :character Median :180.0 Median :1.000 Mode :character
## Mean :188.7 Mean :0.993
## 3rd Qu.:290.0 3rd Qu.:1.000
## Max. :360.0 Max. :2.000
## NA's :33542 NA's :29513
## WINDCONDITIONSQC WINDSPEED WINDSPEEDQC STARTDIRECTION
## Min. :1.00 Min. : 0.000 Min. :0.000 Min. : 10.0
## 1st Qu.:1.00 1st Qu.: 2.600 1st Qu.:1.000 1st Qu.:120.0
## Median :1.00 Median : 4.100 Median :1.000 Median :200.0
## Mean :1.02 Mean : 4.311 Mean :1.007 Mean :204.3
## 3rd Qu.:1.00 3rd Qu.: 6.100 3rd Qu.:1.000 3rd Qu.:310.0
## Max. :4.00 Max. :62.200 Max. :4.000 Max. :360.0
## NA's :179284 NA's :2532 NA's :2709 NA's :293760
## ENDDIRECTION WINDGUSTSPEED WINDGUSTSPEEDQC WINDMEASUREMENTMODE
## Min. : 10.0 Min. : 6.20 Min. :0.00 Min. :1
## 1st Qu.: 70.0 1st Qu.:10.30 1st Qu.:1.00 1st Qu.:4
## Median :170.0 Median :11.80 Median :1.00 Median :4
## Mean :169.9 Mean :12.19 Mean :0.79 Mean :4
## 3rd Qu.:250.0 3rd Qu.:13.90 3rd Qu.:1.00 3rd Qu.:4
## Max. :360.0 Max. :39.10 Max. :2.00 Max. :4
## NA's :293772 NA's :246957 NA's :234105 NA's :181704
## CLOUDCEILING CLOUDCEILINGQC CEILINGDETERMINATION CEILINGDETERMINATIONQC
## Min. : 0 Min. :0.000 Length:294162 Min. :0.0
## 1st Qu.: 975 1st Qu.:1.000 Class :character 1st Qu.:0.0
## Median :22000 Median :1.000 Mode :character Median :0.0
## Mean :13806 Mean :1.105 Mean :0.1
## 3rd Qu.:22000 3rd Qu.:1.000 3rd Qu.:0.0
## Max. :22000 Max. :4.000 Max. :1.0
## NA's :3838 NA's :3789 NA's :276797
## CLOUDCAVOK CLOUDCAVOKQC VISIBILITY VISIBILITYQC
## Length:294162 Min. :1 Min. : 0 Min. :1.000
## Class :character 1st Qu.:1 1st Qu.: 16093 1st Qu.:1.000
## Mode :character Median :1 Median : 16093 Median :1.000
## Mean :1 Mean : 14219 Mean :1.002
## 3rd Qu.:1 3rd Qu.: 16093 3rd Qu.:1.000
## Max. :1 Max. :160000 Max. :5.000
## NA's :180069 NA's :1438 NA's :1437
## VISIBILITYTYPE VISIBILITYTYPEQC AIRTEMPERATURE AIRTEMPERATUREQC
## Length:294162 Min. :1 Min. :-29.00 Min. :0.000
## Class :character 1st Qu.:1 1st Qu.: 3.30 1st Qu.:1.000
## Mode :character Median :1 Median : 13.90 Median :1.000
## Mean :1 Mean : 12.63 Mean :1.004
## 3rd Qu.:1 3rd Qu.: 22.00 3rd Qu.:1.000
## Max. :4 Max. : 44.00 Max. :5.000
## NA's :879 NA's :1466 NA's :1443
## DEWPOINTTEMPERATURE DEWPOINTTEMPERATUREQC SEALEVELPRESSURE SEALEVELPRESSUREQC
## Min. :-31.000 Min. :0.000 Min. : 968.7 Min. :0.00
## 1st Qu.: -1.700 1st Qu.:1.000 1st Qu.:1011.4 1st Qu.:1.00
## Median : 8.000 Median :1.000 Median :1015.9 Median :1.00
## Mean : 6.943 Mean :1.004 Mean :1016.3 Mean :0.99
## 3rd Qu.: 17.000 3rd Qu.:1.000 3rd Qu.:1020.9 3rd Qu.:1.00
## Max. : 29.000 Max. :5.000 Max. :1073.9 Max. :5.00
## NA's :1748 NA's :1713 NA's :74091 NA's :71413
## OBSERVATIONPERIODPP1 OBSERVATIONPERIODPP1QC PRECIPAMOUNT1 PRECIPAMOUNT1QC
## Min. : 1.00 Min. :1.00 Min. : 0.00 Min. :0.00
## 1st Qu.: 1.00 1st Qu.:1.00 1st Qu.: 0.00 1st Qu.:1.00
## Median : 1.00 Median :1.00 Median : 0.20 Median :1.00
## Mean : 2.59 Mean :1.43 Mean : 1.48 Mean :1.39
## 3rd Qu.: 3.00 3rd Qu.:1.00 3rd Qu.: 1.00 3rd Qu.:1.00
## Max. :24.00 Max. :4.00 Max. :725.10 Max. :4.00
## NA's :249614 NA's :272994 NA's :250144 NA's :270610
## PRECIPCONDITION1 PRECIPCONDITION1QC OBSERVATIONPERIODPP2
## Min. :1.00 Min. :1.00 Min. : 1.00
## 1st Qu.:2.00 1st Qu.:1.00 1st Qu.: 3.00
## Median :2.00 Median :1.00 Median : 3.00
## Mean :2.38 Mean :1.36 Mean : 6.04
## 3rd Qu.:3.00 3rd Qu.:1.00 3rd Qu.: 6.00
## Max. :3.00 Max. :4.00 Max. :24.00
## NA's :264772 NA's :270713 NA's :287624
## OBSERVATIONPERIODPP2QC PRECIPAMOUNT2 PRECIPAMOUNT2QC PRECIPCONDITION2
## Min. :1 Min. : 0.00 Min. :0.00 Min. :1.00
## 1st Qu.:1 1st Qu.: 0.00 1st Qu.:1.00 1st Qu.:2.00
## Median :1 Median : 0.80 Median :1.00 Median :3.00
## Mean :1 Mean : 4.14 Mean :0.86 Mean :2.54
## 3rd Qu.:1 3rd Qu.: 4.30 3rd Qu.:1.00 3rd Qu.:3.00
## Max. :1 Max. :123.70 Max. :1.00 Max. :3.00
## NA's :291745 NA's :287657 NA's :291395 NA's :290433
## PRECIPCONDITION2QC OBSERVATIONPERIODPP3 OBSERVATIONPERIODPP3QC
## Min. :1 Min. : 1.00 Min. :1
## 1st Qu.:1 1st Qu.:24.00 1st Qu.:1
## Median :1 Median :24.00 Median :1
## Mean :1 Mean :20.72 Mean :1
## 3rd Qu.:1 3rd Qu.:24.00 3rd Qu.:1
## Max. :1 Max. :24.00 Max. :1
## NA's :291370 NA's :293523 NA's :293897
## PRECIPAMOUNT3 PRECIPAMOUNT3QC PRECIPCONDITION3 PRECIPCONDITION3QC
## Min. : 0.00 Min. :0.00 Min. :1.0 Min. :1
## 1st Qu.: 0.80 1st Qu.:1.00 1st Qu.:3.0 1st Qu.:1
## Median : 4.30 Median :1.00 Median :3.0 Median :1
## Mean : 10.76 Mean :0.99 Mean :2.8 Mean :1
## 3rd Qu.: 14.43 3rd Qu.:1.00 3rd Qu.:3.0 3rd Qu.:1
## Max. :170.90 Max. :1.00 Max. :3.0 Max. :1
## NA's :293542 NA's :293865 NA's :293817 NA's :293848
## OBSERVATIONPERIODPP4 OBSERVATIONPERIODPP4QC PRECIPAMOUNT4 PRECIPAMOUNT4QC
## Mode:logical Mode:logical Mode:logical Mode:logical
## NA's:294162 NA's:294162 NA's:294162 NA's:294162
##
##
##
##
##
## PRECIPCONDITION4 PRECIPCONDITION4QC PRECIPHISTDUR PRECIPHISTDURQC
## Mode:logical Mode:logical Mode:logical Min. :0
## NA's:294162 NA's:294162 NA's:294162 1st Qu.:0
## Median :0
## Mean :0
## 3rd Qu.:0
## Max. :0
## NA's :291575
## PRECIPHISTCHAR PRECIPHISTCHARQC PRECIPDISC PRECIPDISCQC
## Mode:logical Mode:logical Min. :0.00 Mode:logical
## NA's:294162 NA's:294162 1st Qu.:1.00 NA's:294162
## Median :1.00
## Mean :0.89
## 3rd Qu.:1.00
## Max. :3.00
## NA's :259349
## PRECIPBOGUS PRECIPBOGUSQC PRECIPAMOUNTSD PRECIPAMOUNTSDQC
## Min. : 0.00 Mode:logical Min. : 0.00 Mode:logical
## 1st Qu.: 0.00 NA's:294162 1st Qu.: 0.00 NA's:294162
## Median : 0.00 Median : 1.00
## Mean : 0.15 Mean : 4.14
## 3rd Qu.: 0.00 3rd Qu.: 5.00
## Max. :15.00 Max. :490.00
## NA's :284681 NA's :293645
## PRECIPCONDITIONSD PRECIPCONDITIONSDQC DEPTHWTREQUIV DEPTHWTREQUIVQC
## Mode:logical Mode:logical Mode:logical Mode:logical
## NA's:294162 NA's:294162 NA's:294162 NA's:294162
##
##
##
##
##
## DEPTHWECOND DEPTHWECONDQC HAILSIZE PRECIPAMOUNTSF1
## Mode:logical Mode:logical Min. :0.1 Mode:logical
## NA's:294162 NA's:294162 1st Qu.:0.1 NA's:294162
## Median :0.1
## Mean :0.1
## 3rd Qu.:0.1
## Max. :0.1
## NA's :294161
## PRECIPAMOUNTSF1QC PRECIPCONDITIONSF1 PRECIPCONDITIONSF1QC OBSERVATIONPERIODSF1
## Mode:logical Mode:logical Mode:logical Mode:logical
## NA's:294162 NA's:294162 NA's:294162 NA's:294162
##
##
##
##
##
## OBSERVATIONPERIODSF1QC PRECIPAMOUNTSF2 PRECIPAMOUNTSF2QC PRECIPCONDITIONSF2
## Mode:logical Mode:logical Mode:logical Mode:logical
## NA's:294162 NA's:294162 NA's:294162 NA's:294162
##
##
##
##
##
## PRECIPCONDITIONSF2QC OBSERVATIONPERIODSF2 OBSERVATIONPERIODSF2QC
## Mode:logical Mode:logical Mode:logical
## NA's:294162 NA's:294162 NA's:294162
##
##
##
##
##
## PRECIPAMOUNTSF3 PRECIPAMOUNTSF3QC PRECIPCONDITIONSF3 PRECIPCONDITIONSF3QC
## Mode:logical Mode:logical Mode:logical Mode:logical
## NA's:294162 NA's:294162 NA's:294162 NA's:294162
##
##
##
##
##
## OBSERVATIONPERIODSF3 OBSERVATIONPERIODSF3QC PRECIPAMOUNTSF4 PRECIPAMOUNTSF4QC
## Mode:logical Mode:logical Mode:logical Mode:logical
## NA's:294162 NA's:294162 NA's:294162 NA's:294162
##
##
##
##
##
## PRECIPCONDITIONSF4 PRECIPCONDITIONSF4QC OBSERVATIONPERIODSF4
## Mode:logical Mode:logical Mode:logical
## NA's:294162 NA's:294162 NA's:294162
##
##
##
##
##
## OBSERVATIONPERIODSF4QC PRESENTMANUAL1 PRESENTMANUAL1QC PRESENTMANUAL2
## Mode:logical Min. : 0.0 Min. :0.00 Min. : 0.00
## NA's:294162 1st Qu.: 0.0 1st Qu.:1.00 1st Qu.: 0.00
## Median : 0.0 Median :1.00 Median : 0.00
## Mean :13.9 Mean :0.96 Mean : 4.15
## 3rd Qu.:10.0 3rd Qu.:1.00 3rd Qu.: 0.00
## Max. :99.0 Max. :4.00 Max. :90.00
## NA's :217716 NA's :214749 NA's :241033
## PRESENTMANUAL2QC PRESENTMANUAL3 PRESENTMANUAL3QC PRESENTMANUAL4
## Min. :0.00 Min. : 0.00 Min. :0 Mode:logical
## 1st Qu.:1.00 1st Qu.: 0.00 1st Qu.:1 NA's:294162
## Median :1.00 Median : 0.00 Median :1
## Mean :0.99 Mean : 0.37 Mean :1
## 3rd Qu.:1.00 3rd Qu.: 0.00 3rd Qu.:1
## Max. :1.00 Max. :90.00 Max. :1
## NA's :223903 NA's :246001 NA's :228002
## PRESENTMANUAL4QC PRESENTMANUAL5 PRESENTMANUAL5QC PRESENTMANUAL6
## Min. :1 Mode:logical Min. :1 Mode:logical
## 1st Qu.:1 NA's:294162 1st Qu.:1 NA's:294162
## Median :1 Median :1
## Mean :1 Mean :1
## 3rd Qu.:1 3rd Qu.:1
## Max. :1 Max. :1
## NA's :232912 NA's :232912
## PRESENTMANUAL6QC PRESENTMANUAL7 PRESENTMANUAL7QC PRESENTAUTOMATED1
## Min. :1 Mode:logical Min. :1 Min. : 4.0
## 1st Qu.:1 NA's:294162 1st Qu.:1 1st Qu.:10.0
## Median :1 Median :1 Median :40.0
## Mean :1 Mean :1 Mean :40.5
## 3rd Qu.:1 3rd Qu.:1 3rd Qu.:62.0
## Max. :1 Max. :1 Max. :96.0
## NA's :232912 NA's :232912 NA's :261552
## PRESENTAUTOMATED1QC PRESENTAUTOMATED2 PRESENTAUTOMATED2QC PRESENTAUTOMATED3
## Min. :0.00 Min. : 4.00 Min. :1 Min. :10.00
## 1st Qu.:1.00 1st Qu.:10.00 1st Qu.:1 1st Qu.:10.00
## Median :1.00 Median :10.00 Median :1 Median :10.00
## Mean :0.81 Mean :16.53 Mean :1 Mean :10.35
## 3rd Qu.:1.00 3rd Qu.:10.00 3rd Qu.:1 3rd Qu.:10.00
## Max. :5.00 Max. :95.00 Max. :5 Max. :30.00
## NA's :253583 NA's :284284 NA's :284282 NA's :293601
## PRESENTAUTOMATED3QC PASTMANUAL1 PASTMANUAL1QC WXPASTPERIOD1
## Min. :1.00 Mode:logical Min. :0 Mode:logical
## 1st Qu.:1.00 NA's:294162 1st Qu.:0 NA's:294162
## Median :1.00 Median :0
## Mean :1.03 Mean :0
## 3rd Qu.:1.00 3rd Qu.:0
## Max. :4.00 Max. :0
## NA's :293601 NA's :291195
## WXPASTPERIOD1QC PASTMANUAL2 PASTMANUAL2QC WXPASTPERIOD2 WXPASTPERIOD2QC
## Mode:logical Mode:logical Min. :0 Mode:logical Mode:logical
## NA's:294162 NA's:294162 1st Qu.:0 NA's:294162 NA's:294162
## Median :0
## Mean :0
## 3rd Qu.:0
## Max. :0
## NA's :293287
## PASTAUTOMATED1 PASTAUTOMATED1QC WXPASTAUTOPERIOD1 WXPASTAUTOPERIOD1QC
## Mode:logical Min. :0 Mode:logical Mode:logical
## NA's:294162 1st Qu.:0 NA's:294162 NA's:294162
## Median :0
## Mean :0
## 3rd Qu.:0
## Max. :0
## NA's :291195
## PASTAUTOMATED2 PASTAUTOMATED2QC WXPASTAUTOPERIOD2 WXPASTAUTOPERIOD2QC
## Mode:logical Min. :0 Mode:logical Mode:logical
## NA's:294162 1st Qu.:0 NA's:294162 NA's:294162
## Median :0
## Mean :0
## 3rd Qu.:0
## Max. :0
## NA's :293287
## RUNWAYENDBEARING RUNWAYDESIGNATOR RUNWAYVISUALRANGE CLOUDCOVER
## Min. :31 Length:294162 Min. : 91 Min. : 0.00
## 1st Qu.:31 Class :character 1st Qu.: 732 1st Qu.: 0.00
## Median :31 Mode :character Median :1067 Median : 0.00
## Mean :31 Mean :1121 Mean : 2.55
## 3rd Qu.:31 3rd Qu.:1524 3rd Qu.: 7.00
## Max. :31 Max. :1829 Max. :10.00
## NA's :292644 NA's :292642 NA's :49495
## CLOUDCOVERQC CLOUDCOVERLO CLOUDCOVERLOQC CLOUDBASEHEIGHT
## Min. :0.00 Min. :0 Min. :0 Min. : 0
## 1st Qu.:1.00 1st Qu.:0 1st Qu.:0 1st Qu.: 244
## Median :1.00 Median :0 Median :0 Median : 579
## Mean :1.07 Mean :0 Mean :0 Mean : 948
## 3rd Qu.:1.00 3rd Qu.:0 3rd Qu.:0 3rd Qu.:1372
## Max. :4.00 Max. :0 Max. :1 Max. :7620
## NA's :43855 NA's :294161 NA's :288431 NA's :239481
## CLOUDBASEHEIGHTQC CLOUDTYPELO CLOUDTYPELOQC CLOUDTYPEMID
## Min. :1 Min. :7 Min. :0 Min. :1
## 1st Qu.:1 1st Qu.:7 1st Qu.:0 1st Qu.:1
## Median :1 Median :7 Median :0 Median :1
## Mean :1 Mean :7 Mean :0 Mean :1
## 3rd Qu.:1 3rd Qu.:7 3rd Qu.:0 3rd Qu.:1
## Max. :1 Max. :7 Max. :1 Max. :1
## NA's :239481 NA's :294161 NA's :288431 NA's :294161
## CLOUDTYPEMIDQC CLOUDTYPEHI CLOUDTYPEHIQC SUNSHINE
## Min. :0 Min. :0 Min. :0 Mode:logical
## 1st Qu.:0 1st Qu.:0 1st Qu.:0 NA's:294162
## Median :0 Median :0 Median :0
## Mean :0 Mean :0 Mean :0
## 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0
## Max. :1 Max. :0 Max. :1
## NA's :288431 NA's :294161 NA's :288431
## SURFACECODE SURFACECODEQC SOILTEMPERATURE SOILTEMPERATUREQC
## Min. :1 Mode:logical Mode:logical Mode:logical
## 1st Qu.:1 NA's:294162 NA's:294162 NA's:294162
## Median :1
## Mean :1
## 3rd Qu.:1
## Max. :1
## NA's :294160
## SOILDEPTH OBSERVATIONPERIODSOILT OBSERVATIONPERIODSOILTQC
## Mode:logical Mode:logical Mode:logical
## NA's:294162 NA's:294162 NA's:294162
##
##
##
##
##
## ALTIMETERSETTING ALTIMETERSETTINGQC STATIONPRESSURE STATIONPRESSUREQC
## Min. : 942.1 Min. :0.000 Mode:logical Min. :0
## 1st Qu.:1011.8 1st Qu.:1.000 NA's:294162 1st Qu.:0
## Median :1015.9 Median :1.000 Median :0
## Mean :1016.2 Mean :1.002 Mean :0
## 3rd Qu.:1020.7 3rd Qu.:1.000 3rd Qu.:0
## Max. :1085.7 Max. :5.000 Max. :0
## NA's :1020 NA's :999 NA's :278370
## PRESSURETENDENCY PRESSURETENDENCYQC PRESSURE3HOURCHG PRESSURE3HOURCHGQC
## Min. :0.0 Min. :0.00 Min. :-7.30 Min. :0.00
## 1st Qu.:2.0 1st Qu.:1.00 1st Qu.: 0.30 1st Qu.:1.00
## Median :4.0 Median :1.00 Median : 0.80 Median :1.00
## Mean :4.3 Mean :0.78 Mean : 0.89 Mean :0.78
## 3rd Qu.:6.0 3rd Qu.:1.00 3rd Qu.: 1.40 3rd Qu.:1.00
## Max. :8.0 Max. :5.00 Max. :30.80 Max. :5.00
## NA's :240783 NA's :225042 NA's :239619 NA's :224260
## PRESSURE24HOURCHG PRESSURE24HOURCHGQC PRESSURETREND ISOBARICSURFACE
## Min. :0 Min. :0.00 Mode:logical Mode:logical
## 1st Qu.:0 1st Qu.:0.00 NA's:294162 NA's:294162
## Median :0 Median :0.00
## Mean :0 Mean :0.05
## 3rd Qu.:0 3rd Qu.:0.00
## Max. :0 Max. :1.00
## NA's :293348 NA's :277556
## ISOBARICSURFACEQC ISOBARICSURFACEHEIGHT ISOBARICSURFACEHEIGHTQC SEASURFACETEMP
## Mode:logical Mode:logical Mode:logical Mode:logical
## NA's:294162 NA's:294162 NA's:294162 NA's:294162
##
##
##
##
##
## SEASURFACETEMPQC REMARKSYN REMARKMET REMARKAWY
## Min. :5 Mode:logical Length:294162 Length:294162
## 1st Qu.:5 NA's:294162 Class :character Class :character
## Median :5 Mode :character Mode :character
## Mean :5
## 3rd Qu.:5
## Max. :5
## NA's :294160
## HORIZONTALDATUM VERTICALDATUM LIGHTNINGFREQUENCY
## Length:294162 Length:294162 Mode:logical
## Class :character Class :character NA's:294162
## Mode :character Mode :character
##
##
##
##
## RECEIPTDTG INSERTIONTIME BLKSTN
## Min. :20130500000000 Length:294162 Min. :724565
## 1st Qu.:20160100000000 Class :character 1st Qu.:724565
## Median :20180800000000 Mode :character Median :724565
## Mean :20182024898100 Mean :724565
## 3rd Qu.:20210300000000 3rd Qu.:724565
## Max. :20231200000000 Max. :724565
## NA's :180069 NA's :116849Madison, WI
summary(kmsn_data)## PLATFORMID NETWORKTYPE OBSERVATIONTIME REPORTTYPECODE
## Length:362604 Length:362604 Length:362604 Length:362604
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## LATITUDE LONGITUDE MONTH SECURITYID
## Min. :43.13 Min. :-89.35 Min. : 1.000 Length:362604
## 1st Qu.:43.13 1st Qu.:-89.35 1st Qu.: 3.000 Class :character
## Median :43.13 Median :-89.34 Median : 6.000 Mode :character
## Mean :43.13 Mean :-89.34 Mean : 6.467
## 3rd Qu.:43.14 3rd Qu.:-89.33 3rd Qu.:10.000
## Max. :43.14 Max. :-89.33 Max. :12.000
## NA's :2 NA's :2 NA's :2
## DISTRIBUTIONCD STATIONMODE PLATFORMHEIGHT CALLLETTER
## Length:362604 Min. :0.00 Min. :261.0 Length:362604
## Class :character 1st Qu.:0.00 1st Qu.:261.0 Class :character
## Mode :character Median :0.00 Median :264.0 Mode :character
## Mean :0.11 Mean :265.2
## 3rd Qu.:0.00 3rd Qu.:270.4
## Max. :1.00 Max. :270.4
## NA's :224898 NA's :2
## VERSION WINDDIRECTION WINDDIRECTIONQC WINDCONDITIONS
## Length:362604 Min. : 10.0 Min. :0.00 Length:362604
## Class :character 1st Qu.:120.0 1st Qu.:1.00 Class :character
## Mode :character Median :200.0 Median :1.00 Mode :character
## Mean :200.1 Mean :0.98
## 3rd Qu.:300.0 3rd Qu.:1.00
## Max. :360.0 Max. :4.00
## NA's :77023 NA's :64798
## WINDCONDITIONSQC WINDSPEED WINDSPEEDQC STARTDIRECTION
## Min. :1.00 Min. : 0.000 Min. :0.000 Min. : 10.0
## 1st Qu.:1.00 1st Qu.: 2.100 1st Qu.:1.000 1st Qu.:200.0
## Median :1.00 Median : 3.100 Median :1.000 Median :220.0
## Mean :1.03 Mean : 3.344 Mean :1.011 Mean :222.4
## 3rd Qu.:1.00 3rd Qu.: 4.600 3rd Qu.:1.000 3rd Qu.:250.0
## Max. :5.00 Max. :50.900 Max. :5.000 Max. :360.0
## NA's :226659 NA's :1244 NA's :2009 NA's :359791
## ENDDIRECTION WINDGUSTSPEED WINDGUSTSPEEDQC WINDMEASUREMENTMODE
## Min. : 10.0 Min. : 0.0 Min. :0.00 Min. :1
## 1st Qu.:270.0 1st Qu.: 8.7 1st Qu.:0.00 1st Qu.:4
## Median :290.0 Median : 9.8 Median :1.00 Median :4
## Mean :281.4 Mean :10.3 Mean :0.86 Mean :4
## 3rd Qu.:310.0 3rd Qu.:11.3 3rd Qu.:1.00 3rd Qu.:4
## Max. :360.0 Max. :42.0 Max. :4.00 Max. :4
## NA's :359791 NA's :316685 NA's :300048 NA's :228490
## CLOUDCEILING CLOUDCEILINGQC CEILINGDETERMINATION CEILINGDETERMINATIONQC
## Min. : 0 Min. :0.000 Length:362604 Min. :0.0
## 1st Qu.: 750 1st Qu.:1.000 Class :character 1st Qu.:0.0
## Median : 4500 Median :1.000 Mode :character Median :0.0
## Mean :10847 Mean :1.174 Mean :0.1
## 3rd Qu.:22000 3rd Qu.:1.000 3rd Qu.:0.0
## Max. :22000 Max. :4.000 Max. :5.0
## NA's :25645 NA's :23392 NA's :341854
## CLOUDCAVOK CLOUDCAVOKQC VISIBILITY VISIBILITYQC
## Length:362604 Min. :1 Min. : 0 Min. :1.000
## Class :character 1st Qu.:1 1st Qu.: 9656 1st Qu.:1.000
## Mode :character Median :1 Median :16093 Median :1.000
## Mean :1 Mean :12856 Mean :1.003
## 3rd Qu.:1 3rd Qu.:16093 3rd Qu.:1.000
## Max. :1 Max. :50000 Max. :4.000
## NA's :227891 NA's :177 NA's :176
## VISIBILITYTYPE VISIBILITYTYPEQC AIRTEMPERATURE AIRTEMPERATUREQC
## Length:362604 Min. :1 Min. :-32.800 Min. :1.000
## Class :character 1st Qu.:1 1st Qu.: -0.600 1st Qu.:1.000
## Mode :character Median :1 Median : 8.900 Median :1.000
## Mean :1 Mean : 8.423 Mean :1.003
## 3rd Qu.:1 3rd Qu.: 18.300 3rd Qu.:1.000
## Max. :4 Max. : 60.000 Max. :5.000
## NA's :547 NA's :10904 NA's :10903
## DEWPOINTTEMPERATURE DEWPOINTTEMPERATUREQC SEALEVELPRESSURE SEALEVELPRESSUREQC
## Min. :-37.800 Min. :1.000 Min. : 976 Min. :0.00
## 1st Qu.: -4.000 1st Qu.:1.000 1st Qu.:1012 1st Qu.:1.00
## Median : 3.300 Median :1.000 Median :1016 Median :1.00
## Mean : 3.487 Mean :1.003 Mean :1016 Mean :0.99
## 3rd Qu.: 13.000 3rd Qu.:1.000 3rd Qu.:1021 3rd Qu.:1.00
## Max. : 27.200 Max. :5.000 Max. :1048 Max. :5.00
## NA's :10938 NA's :10937 NA's :61445 NA's :57767
## OBSERVATIONPERIODPP1 OBSERVATIONPERIODPP1QC PRECIPAMOUNT1 PRECIPAMOUNT1QC
## Min. : 1.00 Min. :1.0 Min. : 0.00 Min. :1.0
## 1st Qu.: 1.00 1st Qu.:1.0 1st Qu.: 0.00 1st Qu.:1.0
## Median : 1.00 Median :1.0 Median : 0.00 Median :1.0
## Mean : 3.25 Mean :1.3 Mean : 1.11 Mean :1.3
## 3rd Qu.: 6.00 3rd Qu.:1.0 3rd Qu.: 0.50 3rd Qu.:1.0
## Max. :24.00 Max. :4.0 Max. :93.20 Max. :4.0
## NA's :277798 NA's :326553 NA's :277111 NA's :321460
## PRECIPCONDITION1 PRECIPCONDITION1QC OBSERVATIONPERIODPP2
## Min. :1.00 Min. :1.0 Min. : 1
## 1st Qu.:2.00 1st Qu.:1.0 1st Qu.: 3
## Median :2.00 Median :1.0 Median : 6
## Mean :2.28 Mean :1.3 Mean :10
## 3rd Qu.:3.00 3rd Qu.:1.0 3rd Qu.:24
## Max. :3.00 Max. :4.0 Max. :24
## NA's :300206 NA's :321794 NA's :348101
## OBSERVATIONPERIODPP2QC PRECIPAMOUNT2 PRECIPAMOUNT2QC PRECIPCONDITION2
## Min. :1 Min. : 0.0 Min. :0.0 Min. :2.0
## 1st Qu.:1 1st Qu.: 0.0 1st Qu.:1.0 1st Qu.:2.0
## Median :1 Median : 0.5 Median :1.0 Median :2.0
## Mean :1 Mean : 3.7 Mean :0.9 Mean :2.5
## 3rd Qu.:1 3rd Qu.: 3.6 3rd Qu.:1.0 3rd Qu.:3.0
## Max. :1 Max. :112.0 Max. :2.0 Max. :3.0
## NA's :357610 NA's :348172 NA's :356786 NA's :354513
## PRECIPCONDITION2QC OBSERVATIONPERIODPP3 OBSERVATIONPERIODPP3QC
## Min. :1 Min. : 1.0 Min. :1
## 1st Qu.:1 1st Qu.:24.0 1st Qu.:1
## Median :1 Median :24.0 Median :1
## Mean :1 Mean :19.5 Mean :1
## 3rd Qu.:1 3rd Qu.:24.0 3rd Qu.:1
## Max. :1 Max. :24.0 Max. :1
## NA's :356789 NA's :361525 NA's :362158
## PRECIPAMOUNT3 PRECIPAMOUNT3QC PRECIPCONDITION3 PRECIPCONDITION3QC
## Min. : 0.0 Min. :1 Min. :0.0 Min. :1
## 1st Qu.: 0.7 1st Qu.:1 1st Qu.:3.0 1st Qu.:1
## Median : 2.8 Median :1 Median :3.0 Median :1
## Mean : 7.3 Mean :1 Mean :2.9 Mean :1
## 3rd Qu.: 8.7 3rd Qu.:1 3rd Qu.:3.0 3rd Qu.:1
## Max. :112.0 Max. :2 Max. :3.0 Max. :1
## NA's :361524 NA's :361996 NA's :361955 NA's :361996
## OBSERVATIONPERIODPP4 OBSERVATIONPERIODPP4QC PRECIPAMOUNT4 PRECIPAMOUNT4QC
## Min. :24 Min. :1 Min. : 0.0 Min. :1
## 1st Qu.:24 1st Qu.:1 1st Qu.: 0.8 1st Qu.:1
## Median :24 Median :1 Median : 2.1 Median :1
## Mean :24 Mean :1 Mean : 5.6 Mean :1
## 3rd Qu.:24 3rd Qu.:1 3rd Qu.: 4.4 3rd Qu.:1
## Max. :24 Max. :1 Max. :51.5 Max. :1
## NA's :362578 NA's :362601 NA's :362578 NA's :362578
## PRECIPCONDITION4 PRECIPCONDITION4QC PRECIPHISTDUR PRECIPHISTDURQC
## Min. :2 Min. :1 Min. :0.0 Min. :0
## 1st Qu.:3 1st Qu.:1 1st Qu.:1.0 1st Qu.:0
## Median :3 Median :1 Median :2.0 Median :0
## Mean :3 Mean :1 Mean :1.7 Mean :0
## 3rd Qu.:3 3rd Qu.:1 3rd Qu.:2.0 3rd Qu.:0
## Max. :3 Max. :1 Max. :3.0 Max. :0
## NA's :362578 NA's :362578 NA's :361687 NA's :357402
## PRECIPHISTCHAR PRECIPHISTCHARQC PRECIPDISC PRECIPDISCQC
## Length:362604 Mode:logical Min. :0.00 Mode:logical
## Class :character NA's:362604 1st Qu.:1.00 NA's:362604
## Mode :character Median :1.00
## Mean :1.01
## 3rd Qu.:1.00
## Max. :5.00
## NA's :305512
## PRECIPBOGUS PRECIPBOGUSQC PRECIPAMOUNTSD PRECIPAMOUNTSDQC
## Min. : 0.0 Mode:logical Min. : 0.0 Min. :1
## 1st Qu.: 0.0 NA's:362604 1st Qu.: 5.0 1st Qu.:1
## Median : 0.0 Median : 10.0 Median :1
## Mean : 0.3 Mean : 12.6 Mean :1
## 3rd Qu.: 0.0 3rd Qu.: 18.0 3rd Qu.:1
## Max. :13.0 Max. :996.0 Max. :1
## NA's :345610 NA's :349150 NA's :358019
## PRECIPCONDITIONSD PRECIPCONDITIONSDQC DEPTHWTREQUIV DEPTHWTREQUIVQC
## Min. :1 Min. :0 Min. : 5.0 Min. :4
## 1st Qu.:3 1st Qu.:1 1st Qu.: 50.0 1st Qu.:4
## Median :3 Median :1 Median : 80.0 Median :4
## Mean :3 Mean :1 Mean :107.6 Mean :4
## 3rd Qu.:3 3rd Qu.:1 3rd Qu.:152.0 3rd Qu.:4
## Max. :3 Max. :1 Max. :410.0 Max. :4
## NA's :358098 NA's :358097 NA's :357941 NA's :361967
## DEPTHWECOND DEPTHWECONDQC HAILSIZE PRECIPAMOUNTSF1
## Mode:logical Mode:logical Min. : 0.1 Min. : 0.0
## NA's:362604 NA's:362604 1st Qu.: 0.1 1st Qu.: 1.0
## Median : 0.1 Median : 2.0
## Mean : 3.6 Mean : 3.5
## 3rd Qu.: 6.3 3rd Qu.: 5.0
## Max. :12.7 Max. :19.0
## NA's :362595 NA's :362486
## PRECIPAMOUNTSF1QC PRECIPCONDITIONSF1 PRECIPCONDITIONSF1QC OBSERVATIONPERIODSF1
## Min. :0 Mode:logical Min. :0 Min. :1
## 1st Qu.:0 NA's:362604 1st Qu.:0 1st Qu.:1
## Median :0 Median :0 Median :1
## Mean :0 Mean :0 Mean :1
## 3rd Qu.:0 3rd Qu.:0 3rd Qu.:1
## Max. :0 Max. :0 Max. :6
## NA's :362601 NA's :362601 NA's :362501
## OBSERVATIONPERIODSF1QC PRECIPAMOUNTSF2 PRECIPAMOUNTSF2QC PRECIPCONDITIONSF2
## Min. :1 Mode:logical Mode:logical Mode:logical
## 1st Qu.:1 NA's:362604 NA's:362604 NA's:362604
## Median :1
## Mean :1
## 3rd Qu.:1
## Max. :1
## NA's :362502
## PRECIPCONDITIONSF2QC OBSERVATIONPERIODSF2 OBSERVATIONPERIODSF2QC
## Mode:logical Mode:logical Mode:logical
## NA's:362604 NA's:362604 NA's:362604
##
##
##
##
##
## PRECIPAMOUNTSF3 PRECIPAMOUNTSF3QC PRECIPCONDITIONSF3 PRECIPCONDITIONSF3QC
## Mode:logical Mode:logical Mode:logical Mode:logical
## NA's:362604 NA's:362604 NA's:362604 NA's:362604
##
##
##
##
##
## OBSERVATIONPERIODSF3 OBSERVATIONPERIODSF3QC PRECIPAMOUNTSF4 PRECIPAMOUNTSF4QC
## Mode:logical Mode:logical Mode:logical Mode:logical
## NA's:362604 NA's:362604 NA's:362604 NA's:362604
##
##
##
##
##
## PRECIPCONDITIONSF4 PRECIPCONDITIONSF4QC OBSERVATIONPERIODSF4
## Mode:logical Mode:logical Mode:logical
## NA's:362604 NA's:362604 NA's:362604
##
##
##
##
##
## OBSERVATIONPERIODSF4QC PRESENTMANUAL1 PRESENTMANUAL1QC PRESENTMANUAL2
## Mode:logical Min. : 0.00 Min. :0.00 Min. : 0.00
## NA's:362604 1st Qu.: 0.00 1st Qu.:1.00 1st Qu.: 0.00
## Median :10.00 Median :1.00 Median : 0.00
## Mean :24.74 Mean :0.97 Mean : 8.34
## 3rd Qu.:61.00 3rd Qu.:1.00 3rd Qu.:10.00
## Max. :99.00 Max. :5.00 Max. :97.00
## NA's :226282 NA's :221765 NA's :276262
## PRESENTMANUAL2QC PRESENTMANUAL3 PRESENTMANUAL3QC PRESENTMANUAL4
## Min. :0.00 Min. : 0.00 Min. :0 Min. : 0.0
## 1st Qu.:1.00 1st Qu.: 0.00 1st Qu.:1 1st Qu.: 0.0
## Median :1.00 Median : 0.00 Median :1 Median : 0.0
## Mean :0.99 Mean : 1.01 Mean :1 Mean : 9.7
## 3rd Qu.:1.00 3rd Qu.: 0.00 3rd Qu.:1 3rd Qu.:10.0
## Max. :4.00 Max. :90.00 Max. :1 Max. :61.0
## NA's :246295 NA's :293078 NA's :257503 NA's :362309
## PRESENTMANUAL4QC PRESENTMANUAL5 PRESENTMANUAL5QC PRESENTMANUAL6
## Min. :1 Min. :53 Min. :1 Min. : 0.0
## 1st Qu.:1 1st Qu.:62 1st Qu.:1 1st Qu.: 0.0
## Median :1 Median :71 Median :1 Median : 0.0
## Mean :1 Mean :65 Mean :1 Mean :12.7
## 3rd Qu.:1 3rd Qu.:71 3rd Qu.:1 3rd Qu.:19.0
## Max. :1 Max. :71 Max. :1 Max. :38.0
## NA's :265345 NA's :362601 NA's :265410 NA's :362601
## PRESENTMANUAL6QC PRESENTMANUAL7 PRESENTMANUAL7QC PRESENTAUTOMATED1
## Min. :1 Min. :0 Min. :1 Min. : 4.0
## 1st Qu.:1 1st Qu.:0 1st Qu.:1 1st Qu.:10.0
## Median :1 Median :0 Median :1 Median :61.0
## Mean :1 Mean :0 Mean :1 Mean :47.8
## 3rd Qu.:1 3rd Qu.:0 3rd Qu.:1 3rd Qu.:71.0
## Max. :1 Max. :0 Max. :1 Max. :96.0
## NA's :265410 NA's :362601 NA's :265410 NA's :329873
## PRESENTAUTOMATED1QC PRESENTAUTOMATED2 PRESENTAUTOMATED2QC PRESENTAUTOMATED3
## Min. :0.0 Min. : 4.0 Min. :1.0 Min. :10.0
## 1st Qu.:1.0 1st Qu.:10.0 1st Qu.:1.0 1st Qu.:10.0
## Median :1.0 Median :10.0 Median :1.0 Median :10.0
## Mean :0.8 Mean :14.2 Mean :1.1 Mean :20.5
## 3rd Qu.:1.0 3rd Qu.:10.0 3rd Qu.:1.0 3rd Qu.:10.0
## Max. :5.0 Max. :95.0 Max. :4.0 Max. :68.0
## NA's :319410 NA's :350408 NA's :350408 NA's :362093
## PRESENTAUTOMATED3QC PASTMANUAL1 PASTMANUAL1QC WXPASTPERIOD1
## Min. :1.0 Min. :0.0 Min. :0.0 Min. :1
## 1st Qu.:1.0 1st Qu.:2.0 1st Qu.:0.0 1st Qu.:6
## Median :1.0 Median :7.0 Median :0.0 Median :6
## Mean :2.4 Mean :5.8 Mean :0.2 Mean :6
## 3rd Qu.:4.0 3rd Qu.:8.0 3rd Qu.:0.0 3rd Qu.:6
## Max. :4.0 Max. :9.0 Max. :1.0 Max. :6
## NA's :362093 NA's :361279 NA's :356489 NA's :361279
## WXPASTPERIOD1QC PASTMANUAL2 PASTMANUAL2QC WXPASTPERIOD2
## Min. :1 Min. :0 Min. :0.0 Min. :1
## 1st Qu.:1 1st Qu.:1 1st Qu.:0.0 1st Qu.:6
## Median :1 Median :2 Median :0.0 Median :6
## Mean :1 Mean :3 Mean :0.5 Mean :6
## 3rd Qu.:1 3rd Qu.:5 3rd Qu.:1.0 3rd Qu.:6
## Max. :1 Max. :9 Max. :1.0 Max. :6
## NA's :361279 NA's :361279 NA's :359879 NA's :361279
## WXPASTPERIOD2QC PASTAUTOMATED1 PASTAUTOMATED1QC WXPASTAUTOPERIOD1
## Min. :1 Mode:logical Min. :0 Mode:logical
## 1st Qu.:1 NA's:362604 1st Qu.:0 NA's:362604
## Median :1 Median :0
## Mean :1 Mean :0
## 3rd Qu.:1 3rd Qu.:0
## Max. :1 Max. :0
## NA's :361279 NA's :357814
## WXPASTAUTOPERIOD1QC PASTAUTOMATED2 PASTAUTOMATED2QC WXPASTAUTOPERIOD2
## Mode:logical Mode:logical Min. :0 Mode:logical
## NA's:362604 NA's:362604 1st Qu.:0 NA's:362604
## Median :0
## Mean :0
## 3rd Qu.:0
## Max. :0
## NA's :361204
## WXPASTAUTOPERIOD2QC RUNWAYENDBEARING RUNWAYDESIGNATOR RUNWAYVISUALRANGE
## Mode:logical Min. :18 Length:362604 Min. : 0
## NA's:362604 1st Qu.:36 Class :character 1st Qu.: 792
## Median :36 Mode :character Median :1372
## Mean :36 Mean :1247
## 3rd Qu.:36 3rd Qu.:1829
## Max. :36 Max. :1829
## NA's :359624 NA's :359623
## CLOUDCOVER CLOUDCOVERQC CLOUDCOVERLO CLOUDCOVERLOQC
## Min. : 0.00 Min. :0.00 Min. :0 Min. :0.0
## 1st Qu.: 0.00 1st Qu.:1.00 1st Qu.:0 1st Qu.:0.0
## Median : 4.00 Median :1.00 Median :0 Median :1.0
## Mean : 4.06 Mean :1.17 Mean :1 Mean :0.5
## 3rd Qu.: 8.00 3rd Qu.:1.00 3rd Qu.:0 3rd Qu.:1.0
## Max. :10.00 Max. :4.00 Max. :9 Max. :1.0
## NA's :82642 NA's :72917 NA's :350468 NA's :338753
## CLOUDBASEHEIGHT CLOUDBASEHEIGHTQC CLOUDTYPELO CLOUDTYPELOQC
## Min. : 0 Min. :0.00 Min. :0.0 Min. :0.0
## 1st Qu.: 396 1st Qu.:1.00 1st Qu.:0.0 1st Qu.:0.0
## Median : 853 Median :1.00 Median :0.0 Median :1.0
## Mean :1638 Mean :0.99 Mean :1.1 Mean :0.6
## 3rd Qu.:1829 3rd Qu.:1.00 3rd Qu.:0.0 3rd Qu.:1.0
## Max. :9144 Max. :1.00 Max. :9.0 Max. :1.0
## NA's :244081 NA's :243279 NA's :346933 NA's :335218
## CLOUDTYPEMID CLOUDTYPEMIDQC CLOUDTYPEHI CLOUDTYPEHIQC
## Min. :0 Min. :0.0 Min. :0.0 Min. :0.0
## 1st Qu.:0 1st Qu.:0.0 1st Qu.:0.0 1st Qu.:0.0
## Median :0 Median :1.0 Median :0.0 Median :1.0
## Mean :1 Mean :0.5 Mean :0.7 Mean :0.5
## 3rd Qu.:0 3rd Qu.:1.0 3rd Qu.:0.0 3rd Qu.:1.0
## Max. :9 Max. :1.0 Max. :9.0 Max. :1.0
## NA's :348805 NA's :337090 NA's :349432 NA's :337717
## SUNSHINE SURFACECODE SURFACECODEQC SOILTEMPERATURE
## Mode:logical Min. :1 Mode:logical Mode:logical
## NA's:362604 1st Qu.:1 NA's:362604 NA's:362604
## Median :1
## Mean :1
## 3rd Qu.:1
## Max. :1
## NA's :362602
## SOILTEMPERATUREQC SOILDEPTH OBSERVATIONPERIODSOILT
## Mode:logical Mode:logical Mode:logical
## NA's:362604 NA's:362604 NA's:362604
##
##
##
##
##
## OBSERVATIONPERIODSOILTQC ALTIMETERSETTING ALTIMETERSETTINGQC STATIONPRESSURE
## Mode:logical Min. : 976 Min. :0 Min. : 946.2
## NA's:362604 1st Qu.:1011 1st Qu.:1 1st Qu.: 979.9
## Median :1016 Median :1 Median : 984.8
## Mean :1015 Mean :1 Mean : 984.4
## 3rd Qu.:1020 3rd Qu.:1 3rd Qu.: 989.1
## Max. :1045 Max. :5 Max. :1012.6
## NA's :34300 NA's :32093 NA's :267618
## STATIONPRESSUREQC PRESSURETENDENCY PRESSURETENDENCYQC PRESSURE3HOURCHG
## Min. :0.00 Min. :0.00 Min. :0.00 Min. :-8.10
## 1st Qu.:1.00 1st Qu.:2.00 1st Qu.:1.00 1st Qu.: 0.20
## Median :1.00 Median :4.00 Median :1.00 Median : 0.70
## Mean :0.85 Mean :4.37 Mean :0.86 Mean : 0.81
## 3rd Qu.:1.00 3rd Qu.:7.00 3rd Qu.:1.00 3rd Qu.: 1.40
## Max. :5.00 Max. :8.00 Max. :5.00 Max. :13.20
## NA's :250736 NA's :262688 NA's :245553 NA's :260149
## PRESSURE3HOURCHGQC PRESSURE24HOURCHG PRESSURE24HOURCHGQC PRESSURETREND
## Min. :0.00 Min. :0 Min. :0 Mode:logical
## 1st Qu.:1.00 1st Qu.:0 1st Qu.:0 NA's:362604
## Median :1.00 Median :0 Median :0
## Mean :0.87 Mean :0 Mean :0
## 3rd Qu.:1.00 3rd Qu.:0 3rd Qu.:0
## Max. :5.00 Max. :0 Max. :1
## NA's :243352 NA's :361794 NA's :342773
## ISOBARICSURFACE ISOBARICSURFACEQC ISOBARICSURFACEHEIGHT
## Mode:logical Mode:logical Mode:logical
## NA's:362604 NA's:362604 NA's:362604
##
##
##
##
##
## ISOBARICSURFACEHEIGHTQC SEASURFACETEMP SEASURFACETEMPQC REMARKSYN
## Mode:logical Mode:logical Min. :5 Length:362604
## NA's:362604 NA's:362604 1st Qu.:5 Class :character
## Median :5 Mode :character
## Mean :5
## 3rd Qu.:5
## Max. :5
## NA's :362602
## REMARKMET REMARKAWY HORIZONTALDATUM VERTICALDATUM
## Length:362604 Length:362604 Length:362604 Length:362604
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## LIGHTNINGFREQUENCY RECEIPTDTG INSERTIONTIME
## Mode:logical Min. :20130500000000 Length:362604
## NA's:362604 1st Qu.:20160100000000 Class :character
## Median :20180800000000 Mode :character
## Mean :20182136775300
## 3rd Qu.:20210400000000
## Max. :20231200000000
## NA's :227886
## BLKSTN
## Min. :726410
## 1st Qu.:726410
## Median :726410
## Mean :726410
## 3rd Qu.:726410
## Max. :726410
## NA's :137710Tri-cities Airport, TN
summary(ktri_data)## PLATFORMID NETWORKTYPE OBSERVATIONTIME REPORTTYPECODE
## Length:318155 Length:318155 Length:318155 Length:318155
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## LATITUDE LONGITUDE MONTH SECURITYID
## Min. :36.47 Min. :-82.41 Min. : 1.000 Min. :1
## 1st Qu.:36.47 1st Qu.:-82.41 1st Qu.: 3.000 1st Qu.:1
## Median :36.48 Median :-82.40 Median : 7.000 Median :1
## Mean :36.48 Mean :-82.40 Mean : 6.487 Mean :1
## 3rd Qu.:36.48 3rd Qu.:-82.40 3rd Qu.: 9.000 3rd Qu.:1
## Max. :36.48 Max. :-82.38 Max. :12.000 Max. :1
##
## DISTRIBUTIONCD STATIONMODE PLATFORMHEIGHT CALLLETTER
## Length:318155 Min. :0.00 Min. :-1000.0 Length:318155
## Class :character 1st Qu.:0.00 1st Qu.: 463.0 Class :character
## Mode :character Median :0.00 Median : 463.0 Mode :character
## Mean :0.03 Mean : 464.2
## 3rd Qu.:0.00 3rd Qu.: 463.0
## Max. :1.00 Max. : 475.0
## NA's :203807
## VERSION WINDDIRECTION WINDDIRECTIONQC WINDCONDITIONS
## Min. : 0.0 Min. : 10.0 Min. :0.00 Length:318155
## 1st Qu.: 0.0 1st Qu.:150.0 1st Qu.:1.00 Class :character
## Median :182.0 Median :240.0 Median :1.00 Mode :character
## Mean :116.6 Mean :212.1 Mean :0.96
## 3rd Qu.:182.0 3rd Qu.:280.0 3rd Qu.:1.00
## Max. :182.0 Max. :360.0 Max. :4.00
## NA's :157154 NA's :127614
## WINDCONDITIONSQC WINDSPEED WINDSPEEDQC STARTDIRECTION
## Min. :1.0 Min. : 0.000 Min. :0.000 Min. : 10.0
## 1st Qu.:1.0 1st Qu.: 0.000 1st Qu.:1.000 1st Qu.:170.0
## Median :1.0 Median : 1.500 Median :1.000 Median :220.0
## Mean :1.1 Mean : 1.845 Mean :1.038 Mean :209.7
## 3rd Qu.:1.0 3rd Qu.: 3.100 3rd Qu.:1.000 3rd Qu.:260.0
## Max. :4.0 Max. :19.600 Max. :4.000 Max. :360.0
## NA's :202804 NA's :248 NA's :1083 NA's :317679
## ENDDIRECTION WINDGUSTSPEED WINDGUSTSPEEDQC WINDMEASUREMENTMODE
## Min. : 10.0 Min. : 6.10 Min. :0.00 Min. :1
## 1st Qu.:160.0 1st Qu.: 9.20 1st Qu.:0.00 1st Qu.:4
## Median :270.0 Median :10.30 Median :1.00 Median :4
## Mean :231.4 Mean :10.82 Mean :0.51 Mean :4
## 3rd Qu.:310.0 3rd Qu.:11.80 3rd Qu.:1.00 3rd Qu.:4
## Max. :360.0 Max. :29.30 Max. :5.00 Max. :4
## NA's :317679 NA's :301825 NA's :286413 NA's :206930
## CLOUDCEILING CLOUDCEILINGQC CEILINGDETERMINATION CEILINGDETERMINATIONQC
## Min. : 0 Min. :0.000 Length:318155 Min. :0.00
## 1st Qu.: 1080 1st Qu.:1.000 Class :character 1st Qu.:0.00
## Median : 4800 Median :1.000 Mode :character Median :0.00
## Mean :10772 Mean :1.248 Mean :0.12
## 3rd Qu.:22000 3rd Qu.:1.000 3rd Qu.:0.00
## Max. :22000 Max. :4.000 Max. :1.00
## NA's :5621 NA's :5514 NA's :300166
## CLOUDCAVOK CLOUDCAVOKQC VISIBILITY VISIBILITYQC
## Length:318155 Min. :1 Min. : 0 Min. :1.000
## Class :character 1st Qu.:1 1st Qu.: 9656 1st Qu.:1.000
## Mode :character Median :1 Median : 16093 Median :1.000
## Mean :1 Mean : 13013 Mean :1.007
## 3rd Qu.:1 3rd Qu.: 16093 3rd Qu.:1.000
## Max. :1 Max. :128747 Max. :4.000
## NA's :206795 NA's :307 NA's :337
## VISIBILITYTYPE VISIBILITYTYPEQC AIRTEMPERATURE AIRTEMPERATUREQC
## Length:318155 Min. :1.000 Min. :-25.00 Min. :1.000
## Class :character 1st Qu.:1.000 1st Qu.: 6.00 1st Qu.:1.000
## Mode :character Median :1.000 Median : 14.00 Median :1.000
## Mean :1.002 Mean : 13.28 Mean :1.009
## 3rd Qu.:1.000 3rd Qu.: 21.00 3rd Qu.:1.000
## Max. :4.000 Max. : 39.00 Max. :5.000
## NA's :172 NA's :1862 NA's :1857
## DEWPOINTTEMPERATURE DEWPOINTTEMPERATUREQC SEALEVELPRESSURE SEALEVELPRESSUREQC
## Min. :-88.000 Min. :1.000 Min. : 986.3 Min. :0.00
## 1st Qu.: 1.000 1st Qu.:1.000 1st Qu.:1013.9 1st Qu.:1.00
## Median : 10.000 Median :1.000 Median :1017.5 Median :1.00
## Mean : 8.338 Mean :1.009 Mean :1017.6 Mean :0.99
## 3rd Qu.: 16.700 3rd Qu.:1.000 3rd Qu.:1021.4 3rd Qu.:1.00
## Max. : 26.000 Max. :5.000 Max. :1045.1 Max. :5.00
## NA's :1994 NA's :1975 NA's :59034 NA's :56003
## OBSERVATIONPERIODPP1 OBSERVATIONPERIODPP1QC PRECIPAMOUNT1 PRECIPAMOUNT1QC
## Min. : 1.00 Min. :1.00 Min. : 0.00 Min. :0.00
## 1st Qu.: 1.00 1st Qu.:1.00 1st Qu.: 0.00 1st Qu.:1.00
## Median : 1.00 Median :1.00 Median : 0.20 Median :1.00
## Mean : 2.87 Mean :1.43 Mean : 1.18 Mean :1.38
## 3rd Qu.: 6.00 3rd Qu.:1.00 3rd Qu.: 1.00 3rd Qu.:1.00
## Max. :24.00 Max. :4.00 Max. :762.80 Max. :4.00
## NA's :258517 NA's :292668 NA's :260034 NA's :289320
## PRECIPCONDITION1 PRECIPCONDITION1QC OBSERVATIONPERIODPP2
## Min. :1.00 Min. :1.00 Min. : 1.0
## 1st Qu.:2.00 1st Qu.:1.00 1st Qu.: 3.0
## Median :2.00 Median :1.00 Median : 3.0
## Mean :2.37 Mean :1.36 Mean : 6.4
## 3rd Qu.:3.00 3rd Qu.:1.00 3rd Qu.: 6.0
## Max. :3.00 Max. :4.00 Max. :24.0
## NA's :280603 NA's :289582 NA's :307763
## OBSERVATIONPERIODPP2QC PRECIPAMOUNT2 PRECIPAMOUNT2QC PRECIPCONDITION2
## Min. :1 Min. : 0.00 Min. :0.00 Min. :1.00
## 1st Qu.:1 1st Qu.: 0.00 1st Qu.:1.00 1st Qu.:2.00
## Median :1 Median : 0.80 Median :1.00 Median :3.00
## Mean :1 Mean : 3.43 Mean :0.86 Mean :2.55
## 3rd Qu.:1 3rd Qu.: 4.00 3rd Qu.:1.00 3rd Qu.:3.00
## Max. :1 Max. :254.00 Max. :2.00 Max. :3.00
## NA's :314260 NA's :307792 NA's :313623 NA's :311913
## PRECIPCONDITION2QC OBSERVATIONPERIODPP3 OBSERVATIONPERIODPP3QC
## Min. :1 Min. : 1.0 Min. :1
## 1st Qu.:1 1st Qu.:24.0 1st Qu.:1
## Median :1 Median :24.0 Median :1
## Mean :1 Mean :19.6 Mean :1
## 3rd Qu.:1 3rd Qu.:24.0 3rd Qu.:1
## Max. :1 Max. :24.0 Max. :1
## NA's :313622 NA's :317212 NA's :317750
## PRECIPAMOUNT3 PRECIPAMOUNT3QC PRECIPCONDITION3 PRECIPCONDITION3QC
## Min. : 0.0 Min. :0 Min. :1.0 Min. :1
## 1st Qu.: 0.8 1st Qu.:1 1st Qu.:3.0 1st Qu.:1
## Median : 3.8 Median :1 Median :3.0 Median :1
## Mean : 8.3 Mean :1 Mean :2.9 Mean :1
## 3rd Qu.:11.6 3rd Qu.:1 3rd Qu.:3.0 3rd Qu.:1
## Max. :67.5 Max. :1 Max. :3.0 Max. :1
## NA's :317214 NA's :317680 NA's :317612 NA's :317679
## OBSERVATIONPERIODPP4 OBSERVATIONPERIODPP4QC PRECIPAMOUNT4 PRECIPAMOUNT4QC
## Min. :24 Min. :1 Min. : 3.5 Min. :1
## 1st Qu.:24 1st Qu.:1 1st Qu.:10.8 1st Qu.:1
## Median :24 Median :1 Median :13.8 Median :1
## Mean :24 Mean :1 Mean :20.3 Mean :1
## 3rd Qu.:24 3rd Qu.:1 3rd Qu.:22.0 3rd Qu.:1
## Max. :24 Max. :1 Max. :56.3 Max. :1
## NA's :318149 NA's :318151 NA's :318149 NA's :318149
## PRECIPCONDITION4 PRECIPCONDITION4QC PRECIPHISTDUR PRECIPHISTDURQC
## Min. :3 Min. :1 Mode:logical Min. :0
## 1st Qu.:3 1st Qu.:1 NA's:318155 1st Qu.:0
## Median :3 Median :1 Median :0
## Mean :3 Mean :1 Mean :0
## 3rd Qu.:3 3rd Qu.:1 3rd Qu.:0
## Max. :3 Max. :1 Max. :0
## NA's :318149 NA's :318149 NA's :314727
## PRECIPHISTCHAR PRECIPHISTCHARQC PRECIPDISC PRECIPDISCQC
## Mode:logical Mode:logical Min. :0.00 Mode:logical
## NA's:318155 NA's:318155 1st Qu.:0.00 NA's:318155
## Median :0.00
## Mean :0.06
## 3rd Qu.:0.00
## Max. :3.00
## NA's :266624
## PRECIPBOGUS PRECIPBOGUSQC PRECIPAMOUNTSD PRECIPAMOUNTSDQC
## Min. : 0.00 Mode:logical Min. : 0.0 Min. :1
## 1st Qu.: 0.00 NA's:318155 1st Qu.: 2.0 1st Qu.:1
## Median : 0.00 Median : 5.0 Median :1
## Mean : 0.37 Mean : 4.9 Mean :1
## 3rd Qu.: 0.00 3rd Qu.: 6.0 3rd Qu.:1
## Max. :15.00 Max. :33.0 Max. :4
## NA's :300890 NA's :316984 NA's :317900
## PRECIPCONDITIONSD PRECIPCONDITIONSDQC DEPTHWTREQUIV DEPTHWTREQUIVQC
## Min. :3 Min. :1 Min. : 25.0 Min. :4
## 1st Qu.:3 1st Qu.:1 1st Qu.: 25.0 1st Qu.:4
## Median :3 Median :1 Median : 51.0 Median :4
## Mean :3 Mean :1 Mean : 74.5 Mean :4
## 3rd Qu.:3 3rd Qu.:1 3rd Qu.:102.0 3rd Qu.:4
## Max. :3 Max. :1 Max. :406.0 Max. :4
## NA's :317931 NA's :317931 NA's :317897 NA's :318131
## DEPTHWECOND DEPTHWECONDQC HAILSIZE PRECIPAMOUNTSF1
## Mode:logical Mode:logical Min. :0.0 Min. :1
## NA's:318155 NA's:318155 1st Qu.:0.1 1st Qu.:1
## Median :6.3 Median :1
## Mean :4.0 Mean :1
## 3rd Qu.:6.3 3rd Qu.:1
## Max. :6.3 Max. :1
## NA's :318147 NA's :318154
## PRECIPAMOUNTSF1QC PRECIPCONDITIONSF1 PRECIPCONDITIONSF1QC OBSERVATIONPERIODSF1
## Min. :1 Min. :3 Min. :1 Min. :1
## 1st Qu.:1 1st Qu.:3 1st Qu.:1 1st Qu.:1
## Median :1 Median :3 Median :1 Median :1
## Mean :1 Mean :3 Mean :1 Mean :1
## 3rd Qu.:1 3rd Qu.:3 3rd Qu.:1 3rd Qu.:1
## Max. :1 Max. :3 Max. :1 Max. :1
## NA's :318154 NA's :318154 NA's :318154 NA's :318123
## OBSERVATIONPERIODSF1QC PRECIPAMOUNTSF2 PRECIPAMOUNTSF2QC PRECIPCONDITIONSF2
## Min. :1 Mode:logical Mode:logical Mode:logical
## 1st Qu.:1 NA's:318155 NA's:318155 NA's:318155
## Median :1
## Mean :1
## 3rd Qu.:1
## Max. :1
## NA's :318123
## PRECIPCONDITIONSF2QC OBSERVATIONPERIODSF2 OBSERVATIONPERIODSF2QC
## Mode:logical Min. :1 Min. :1
## NA's:318155 1st Qu.:1 1st Qu.:1
## Median :1 Median :1
## Mean :1 Mean :1
## 3rd Qu.:1 3rd Qu.:1
## Max. :1 Max. :1
## NA's :318154 NA's :318154
## PRECIPAMOUNTSF3 PRECIPAMOUNTSF3QC PRECIPCONDITIONSF3 PRECIPCONDITIONSF3QC
## Mode:logical Mode:logical Mode:logical Mode:logical
## NA's:318155 NA's:318155 NA's:318155 NA's:318155
##
##
##
##
##
## OBSERVATIONPERIODSF3 OBSERVATIONPERIODSF3QC PRECIPAMOUNTSF4 PRECIPAMOUNTSF4QC
## Mode:logical Mode:logical Mode:logical Mode:logical
## NA's:318155 NA's:318155 NA's:318155 NA's:318155
##
##
##
##
##
## PRECIPCONDITIONSF4 PRECIPCONDITIONSF4QC OBSERVATIONPERIODSF4
## Mode:logical Mode:logical Mode:logical
## NA's:318155 NA's:318155 NA's:318155
##
##
##
##
##
## OBSERVATIONPERIODSF4QC PRESENTMANUAL1 PRESENTMANUAL1QC PRESENTMANUAL2
## Mode:logical Min. : 0.00 Min. :0.00 Min. : 0.0
## NA's:318155 1st Qu.: 0.00 1st Qu.:1.00 1st Qu.: 0.0
## Median :10.00 Median :1.00 Median : 0.0
## Mean :20.38 Mean :0.97 Mean : 5.1
## 3rd Qu.:45.00 3rd Qu.:1.00 3rd Qu.: 0.0
## Max. :99.00 Max. :4.00 Max. :90.0
## NA's :188449 NA's :184070 NA's :237805
## PRESENTMANUAL2QC PRESENTMANUAL3 PRESENTMANUAL3QC PRESENTMANUAL4
## Min. :0.00 Min. : 0.00 Min. :1 Min. : 5.0
## 1st Qu.:1.00 1st Qu.: 0.00 1st Qu.:1 1st Qu.:10.0
## Median :1.00 Median : 0.00 Median :1 Median :18.0
## Mean :0.99 Mean : 0.42 Mean :1 Mean :24.6
## 3rd Qu.:1.00 3rd Qu.: 0.00 3rd Qu.:1 3rd Qu.:45.0
## Max. :4.00 Max. :90.00 Max. :2 Max. :45.0
## NA's :203534 NA's :248482 NA's :210435 NA's :318150
## PRESENTMANUAL4QC PRESENTMANUAL5 PRESENTMANUAL5QC PRESENTMANUAL6
## Min. :1 Mode:logical Min. :1 Mode:logical
## 1st Qu.:1 NA's:318155 1st Qu.:1 NA's:318155
## Median :1 Median :1
## Mean :1 Mean :1
## 3rd Qu.:1 3rd Qu.:1
## Max. :1 Max. :1
## NA's :217076 NA's :217076
## PRESENTMANUAL6QC PRESENTMANUAL7 PRESENTMANUAL7QC PRESENTAUTOMATED1
## Min. :1 Mode:logical Min. :1 Min. : 4.00
## 1st Qu.:1 NA's:318155 1st Qu.:1 1st Qu.:10.00
## Median :1 Median :1 Median :61.00
## Mean :1 Mean :1 Mean :42.45
## 3rd Qu.:1 3rd Qu.:1 3rd Qu.:62.00
## Max. :1 Max. :1 Max. :96.00
## NA's :217076 NA's :217076 NA's :291129
## PRESENTAUTOMATED1QC PRESENTAUTOMATED2 PRESENTAUTOMATED2QC PRESENTAUTOMATED3
## Min. :0.00 Min. : 4.00 Min. :1.00 Min. : 4
## 1st Qu.:1.00 1st Qu.:10.00 1st Qu.:1.00 1st Qu.:10
## Median :1.00 Median :10.00 Median :1.00 Median :10
## Mean :0.77 Mean :10.91 Mean :1.02 Mean :14
## 3rd Qu.:1.00 3rd Qu.:10.00 3rd Qu.:1.00 3rd Qu.:10
## Max. :4.00 Max. :95.00 Max. :4.00 Max. :33
## NA's :282133 NA's :310178 NA's :310178 NA's :318125
## PRESENTAUTOMATED3QC PASTMANUAL1 PASTMANUAL1QC WXPASTPERIOD1
## Min. :1.0 Mode:logical Min. :0 Mode:logical
## 1st Qu.:1.0 NA's:318155 1st Qu.:0 NA's:318155
## Median :4.0 Median :0
## Mean :3.1 Mean :0
## 3rd Qu.:4.0 3rd Qu.:0
## Max. :4.0 Max. :0
## NA's :318125 NA's :313772
## WXPASTPERIOD1QC PASTMANUAL2 PASTMANUAL2QC WXPASTPERIOD2 WXPASTPERIOD2QC
## Mode:logical Mode:logical Min. :0 Mode:logical Mode:logical
## NA's:318155 NA's:318155 1st Qu.:0 NA's:318155 NA's:318155
## Median :0
## Mean :0
## 3rd Qu.:0
## Max. :0
## NA's :317147
## PASTAUTOMATED1 PASTAUTOMATED1QC WXPASTAUTOPERIOD1 WXPASTAUTOPERIOD1QC
## Mode:logical Min. :0 Mode:logical Mode:logical
## NA's:318155 1st Qu.:0 NA's:318155 NA's:318155
## Median :0
## Mean :0
## 3rd Qu.:0
## Max. :0
## NA's :313772
## PASTAUTOMATED2 PASTAUTOMATED2QC WXPASTAUTOPERIOD2 WXPASTAUTOPERIOD2QC
## Mode:logical Min. :0 Mode:logical Mode:logical
## NA's:318155 1st Qu.:0 NA's:318155 NA's:318155
## Median :0
## Mean :0
## 3rd Qu.:0
## Max. :0
## NA's :317147
## RUNWAYENDBEARING RUNWAYDESIGNATOR RUNWAYVISUALRANGE CLOUDCOVER
## Min. :13 Length:318155 Min. : 122.0 Min. : 0.00
## 1st Qu.:23 Class :character 1st Qu.: 305.0 1st Qu.: 2.00
## Median :23 Mode :character Median : 732.0 Median : 4.00
## Mean :23 Mean : 963.7 Mean : 4.44
## 3rd Qu.:23 3rd Qu.:1829.0 3rd Qu.: 8.00
## Max. :24 Max. :1900.0 Max. :10.00
## NA's :317353 NA's :317352 NA's :73468
## CLOUDCOVERQC CLOUDCOVERLO CLOUDCOVERLOQC CLOUDBASEHEIGHT
## Min. :0.00 Min. :0 Min. :0 Min. : 0
## 1st Qu.:1.00 1st Qu.:0 1st Qu.:0 1st Qu.: 549
## Median :1.00 Median :0 Median :0 Median :1280
## Mean :1.26 Mean :0 Mean :0 Mean :2029
## 3rd Qu.:1.00 3rd Qu.:0 3rd Qu.:0 3rd Qu.:2134
## Max. :4.00 Max. :0 Max. :1 Max. :8534
## NA's :63942 NA's :318154 NA's :307403 NA's :222245
## CLOUDBASEHEIGHTQC CLOUDTYPELO CLOUDTYPELOQC CLOUDTYPEMID
## Min. :1 Min. :0.00 Min. :0.00 Min. :0.00
## 1st Qu.:1 1st Qu.:0.00 1st Qu.:0.00 1st Qu.:0.00
## Median :1 Median :4.00 Median :0.00 Median :2.00
## Mean :1 Mean :2.93 Mean :0.27 Mean :3.48
## 3rd Qu.:1 3rd Qu.:5.00 3rd Qu.:1.00 3rd Qu.:7.00
## Max. :1 Max. :9.00 Max. :1.00 Max. :8.00
## NA's :222245 NA's :314260 NA's :303509 NA's :314935
## CLOUDTYPEMIDQC CLOUDTYPEHI CLOUDTYPEHIQC SUNSHINE
## Min. :0.00 Min. :0.0 Min. :0.0 Mode:logical
## 1st Qu.:0.00 1st Qu.:0.0 1st Qu.:0.0 NA's:318155
## Median :0.00 Median :1.0 Median :0.0
## Mean :0.23 Mean :2.5 Mean :0.2
## 3rd Qu.:0.00 3rd Qu.:7.0 3rd Qu.:0.0
## Max. :1.00 Max. :9.0 Max. :1.0
## NA's :304184 NA's :315539 NA's :304788
## SURFACECODE SURFACECODEQC SOILTEMPERATURE SOILTEMPERATUREQC
## Min. :1 Mode:logical Mode:logical Mode:logical
## 1st Qu.:1 NA's:318155 NA's:318155 NA's:318155
## Median :1
## Mean :1
## 3rd Qu.:1
## Max. :1
## NA's :318153
## SOILDEPTH OBSERVATIONPERIODSOILT OBSERVATIONPERIODSOILTQC
## Mode:logical Mode:logical Mode:logical
## NA's:318155 NA's:318155 NA's:318155
##
##
##
##
##
## ALTIMETERSETTING ALTIMETERSETTINGQC STATIONPRESSURE STATIONPRESSUREQC
## Min. : 987.8 Min. :1.000 Mode:logical Min. :0
## 1st Qu.:1014.9 1st Qu.:1.000 NA's:318155 1st Qu.:0
## Median :1018.3 Median :1.000 Median :0
## Mean :1018.2 Mean :1.002 Mean :0
## 3rd Qu.:1022.0 3rd Qu.:1.000 3rd Qu.:0
## Max. :1042.7 Max. :5.000 Max. :0
## NA's :98 NA's :93 NA's :301938
## PRESSURETENDENCY PRESSURETENDENCYQC PRESSURE3HOURCHG PRESSURE3HOURCHGQC
## Min. :0.00 Min. :0.00 Min. :-7.70 Min. :0.00
## 1st Qu.:2.00 1st Qu.:1.00 1st Qu.: 0.30 1st Qu.:1.00
## Median :3.00 Median :1.00 Median : 0.70 Median :1.00
## Mean :4.21 Mean :0.81 Mean : 0.83 Mean :0.82
## 3rd Qu.:6.00 3rd Qu.:1.00 3rd Qu.: 1.40 3rd Qu.:1.00
## Max. :8.00 Max. :4.00 Max. :11.20 Max. :4.00
## NA's :252279 NA's :236117 NA's :249617 NA's :233876
## PRESSURE24HOURCHG PRESSURE24HOURCHGQC PRESSURETREND ISOBARICSURFACE
## Min. :0 Min. :0.00 Mode:logical Mode:logical
## 1st Qu.:0 1st Qu.:0.00 NA's:318155 NA's:318155
## Median :0 Median :0.00
## Mean :0 Mean :0.05
## 3rd Qu.:0 3rd Qu.:0.00
## Max. :0 Max. :1.00
## NA's :317316 NA's :301099
## ISOBARICSURFACEQC ISOBARICSURFACEHEIGHT ISOBARICSURFACEHEIGHTQC SEASURFACETEMP
## Mode:logical Mode:logical Mode:logical Mode:logical
## NA's:318155 NA's:318155 NA's:318155 NA's:318155
##
##
##
##
##
## SEASURFACETEMPQC REMARKSYN REMARKMET REMARKAWY
## Min. :5 Mode:logical Length:318155 Length:318155
## 1st Qu.:5 NA's:318155 Class :character Class :character
## Median :5 Mode :character Mode :character
## Mean :5
## 3rd Qu.:5
## Max. :5
## NA's :318153
## HORIZONTALDATUM VERTICALDATUM LIGHTNINGFREQUENCY
## Length:318155 Length:318155 Mode:logical
## Class :character Class :character NA's:318155
## Mode :character Mode :character
##
##
##
##
## RECEIPTDTG INSERTIONTIME BLKSTN
## Min. :20130500000000 Length:318155 Min. :703610
## 1st Qu.:20151200000000 Class :character 1st Qu.:723183
## Median :20180700000000 Mode :character Median :723183
## Mean :20181634982000 Mean :723206
## 3rd Qu.:20210400000000 3rd Qu.:723183
## Max. :20231200000000 Max. :723350
## NA's :206795 NA's :114348Juneau, AK
summary(pajn_data)## PLATFORMID NETWORKTYPE OBSERVATIONTIME REPORTTYPECODE
## Length:338952 Length:338952 Length:338952 Length:338952
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## LATITUDE LONGITUDE MONTH SECURITYID
## Min. :58.35 Min. :-134.6 Min. : 1.000 Min. :1
## 1st Qu.:58.35 1st Qu.:-134.6 1st Qu.: 3.000 1st Qu.:1
## Median :58.35 Median :-134.6 Median : 7.000 Median :1
## Mean :58.36 Mean :-134.6 Mean : 6.521 Mean :1
## 3rd Qu.:58.37 3rd Qu.:-134.6 3rd Qu.:10.000 3rd Qu.:1
## Max. :58.37 Max. :-134.6 Max. :12.000 Max. :1
## NA's :3 NA's :3 NA's :3 NA's :3
## DISTRIBUTIONCD STATIONMODE PLATFORMHEIGHT CALLLETTER
## Length:338952 Min. :0.00 Min. :6.400 Length:338952
## Class :character 1st Qu.:0.00 1st Qu.:6.400 Class :character
## Mode :character Median :0.00 Median :7.000 Mode :character
## Mean :0.16 Mean :6.803
## 3rd Qu.:0.00 3rd Qu.:7.000
## Max. :1.00 Max. :7.000
## NA's :212050 NA's :3
## VERSION WINDDIRECTION WINDDIRECTIONQC WINDCONDITIONS
## Min. : 0.0 Min. : 2.0 Min. :0.00 Length:338952
## 1st Qu.: 0.0 1st Qu.: 80.0 1st Qu.:1.00 Class :character
## Median :182.0 Median :110.0 Median :1.00 Mode :character
## Mean :113.9 Mean :134.5 Mean :0.98
## 3rd Qu.:182.0 3rd Qu.:140.0 3rd Qu.:1.00
## Max. :182.0 Max. :360.0 Max. :4.00
## NA's :3 NA's :102930 NA's :87370
## WINDCONDITIONSQC WINDSPEED WINDSPEEDQC STARTDIRECTION
## Min. :1.00 Min. : 0.000 Min. :0.000 Min. : 10.0
## 1st Qu.:1.00 1st Qu.: 0.000 1st Qu.:1.000 1st Qu.: 50.0
## Median :1.00 Median : 2.600 Median :1.000 Median : 70.0
## Mean :1.05 Mean : 3.201 Mean :1.018 Mean :121.5
## 3rd Qu.:1.00 3rd Qu.: 5.100 3rd Qu.:1.000 3rd Qu.:220.0
## Max. :5.00 Max. :28.200 Max. :5.000 Max. :360.0
## NA's :210054 NA's :524 NA's :851 NA's :338625
## ENDDIRECTION WINDGUSTSPEED WINDGUSTSPEEDQC WINDMEASUREMENTMODE
## Min. : 20.0 Min. : 0.00 Min. :0.00 Min. :1
## 1st Qu.:110.0 1st Qu.:10.30 1st Qu.:0.00 1st Qu.:4
## Median :140.0 Median :12.30 Median :1.00 Median :4
## Mean :164.1 Mean :12.43 Mean :0.77 Mean :4
## 3rd Qu.:205.0 3rd Qu.:14.40 3rd Qu.:1.00 3rd Qu.:4
## Max. :360.0 Max. :31.90 Max. :4.00 Max. :4
## NA's :338625 NA's :310591 NA's :293547 NA's :212127
## CLOUDCEILING CLOUDCEILINGQC CEILINGDETERMINATION CEILINGDETERMINATIONQC
## Min. : 0 Min. :0.0 Length:338952 Min. :0.0
## 1st Qu.: 884 1st Qu.:1.0 Class :character 1st Qu.:0.0
## Median : 1350 Median :1.0 Mode :character Median :0.0
## Mean : 5499 Mean :1.2 Mean :0.1
## 3rd Qu.: 3658 3rd Qu.:1.0 3rd Qu.:0.0
## Max. :22000 Max. :4.0 Max. :5.0
## NA's :29769 NA's :27522 NA's :318998
## CLOUDCAVOK CLOUDCAVOKQC VISIBILITY VISIBILITYQC
## Length:338952 Min. :1 Min. : 0 Min. :1.000
## Class :character 1st Qu.:1 1st Qu.: 11265 1st Qu.:1.000
## Mode :character Median :1 Median : 16093 Median :1.000
## Mean :1 Mean : 14655 Mean :1.003
## 3rd Qu.:1 3rd Qu.: 16093 3rd Qu.:1.000
## Max. :1 Max. :160000 Max. :4.000
## NA's :212055 NA's :497 NA's :497
## VISIBILITYTYPE VISIBILITYTYPEQC AIRTEMPERATURE AIRTEMPERATUREQC
## Length:338952 Min. :1 Min. :-23.000 Min. :1.000
## Class :character 1st Qu.:1 1st Qu.: 1.000 1st Qu.:1.000
## Mode :character Median :1 Median : 5.000 Median :1.000
## Mean :1 Mean : 5.482 Mean :1.004
## 3rd Qu.:1 3rd Qu.: 11.000 3rd Qu.:1.000
## Max. :4 Max. : 29.400 Max. :5.000
## NA's :445 NA's :2324 NA's :2322
## DEWPOINTTEMPERATURE DEWPOINTTEMPERATUREQC SEALEVELPRESSURE SEALEVELPRESSUREQC
## Min. :-34.00 Min. :1.000 Min. : 960 Min. :0.00
## 1st Qu.: -1.00 1st Qu.:1.000 1st Qu.:1006 1st Qu.:1.00
## Median : 3.00 Median :1.000 Median :1013 Median :1.00
## Mean : 2.57 Mean :1.004 Mean :1012 Mean :0.99
## 3rd Qu.: 8.00 3rd Qu.:1.000 3rd Qu.:1019 3rd Qu.:1.00
## Max. : 17.00 Max. :5.000 Max. :1055 Max. :5.00
## NA's :2456 NA's :2452 NA's :79571 NA's :76870
## OBSERVATIONPERIODPP1 OBSERVATIONPERIODPP1QC PRECIPAMOUNT1 PRECIPAMOUNT1QC
## Min. : 1.00 Min. :1.00 Min. : 0.00 Min. :0.00
## 1st Qu.: 1.00 1st Qu.:1.00 1st Qu.: 0.00 1st Qu.:1.00
## Median : 1.00 Median :1.00 Median : 0.20 Median :1.00
## Mean : 2.59 Mean :1.28 Mean : 0.74 Mean :1.27
## 3rd Qu.: 6.00 3rd Qu.:1.00 3rd Qu.: 0.70 3rd Qu.:1.00
## Max. :24.00 Max. :4.00 Max. :263.00 Max. :4.00
## NA's :200862 NA's :274168 NA's :200426 NA's :264933
## PRECIPCONDITION1 PRECIPCONDITION1QC OBSERVATIONPERIODPP2
## Min. :1.00 Min. :1.00 Min. : 1.00
## 1st Qu.:2.00 1st Qu.:1.00 1st Qu.: 3.00
## Median :2.00 Median :1.00 Median : 6.00
## Mean :2.36 Mean :1.23 Mean : 7.88
## 3rd Qu.:3.00 3rd Qu.:1.00 3rd Qu.: 6.00
## Max. :3.00 Max. :4.00 Max. :24.00
## NA's :241816 NA's :266081 NA's :306276
## OBSERVATIONPERIODPP2QC PRECIPAMOUNT2 PRECIPAMOUNT2QC PRECIPCONDITION2
## Min. :1 Min. : 0.00 Min. :0.0 Min. :0.0
## 1st Qu.:1 1st Qu.: 0.20 1st Qu.:1.0 1st Qu.:2.0
## Median :1 Median : 1.00 Median :1.0 Median :3.0
## Mean :1 Mean : 2.91 Mean :0.9 Mean :2.6
## 3rd Qu.:1 3rd Qu.: 3.30 3rd Qu.:1.0 3rd Qu.:3.0
## Max. :4 Max. :227.40 Max. :4.0 Max. :3.0
## NA's :325060 NA's :306346 NA's :322935 NA's :319397
## PRECIPCONDITION2QC OBSERVATIONPERIODPP3 OBSERVATIONPERIODPP3QC
## Min. :1 Min. : 1.0 Min. :1
## 1st Qu.:1 1st Qu.:24.0 1st Qu.:1
## Median :1 Median :24.0 Median :1
## Mean :1 Mean :20.4 Mean :1
## 3rd Qu.:1 3rd Qu.:24.0 3rd Qu.:1
## Max. :4 Max. :24.0 Max. :1
## NA's :322931 NA's :335749 NA's :337508
## PRECIPAMOUNT3 PRECIPAMOUNT3QC PRECIPCONDITION3 PRECIPCONDITION3QC
## Min. : 0.0 Min. :0 Min. :1.0 Min. :1
## 1st Qu.: 1.5 1st Qu.:1 1st Qu.:3.0 1st Qu.:1
## Median : 4.6 Median :1 Median :3.0 Median :1
## Mean : 7.7 Mean :1 Mean :2.9 Mean :1
## 3rd Qu.: 10.9 3rd Qu.:1 3rd Qu.:3.0 3rd Qu.:1
## Max. :129.0 Max. :2 Max. :3.0 Max. :1
## NA's :335760 NA's :337176 NA's :337026 NA's :337168
## OBSERVATIONPERIODPP4 OBSERVATIONPERIODPP4QC PRECIPAMOUNT4 PRECIPAMOUNT4QC
## Min. :24 Min. :1 Min. : 0.2 Min. :1
## 1st Qu.:24 1st Qu.:1 1st Qu.: 2.2 1st Qu.:1
## Median :24 Median :1 Median : 5.0 Median :1
## Mean :24 Mean :1 Mean : 8.7 Mean :1
## 3rd Qu.:24 3rd Qu.:1 3rd Qu.:12.9 3rd Qu.:1
## Max. :24 Max. :1 Max. :35.8 Max. :1
## NA's :338907 NA's :338938 NA's :338907 NA's :338907
## PRECIPCONDITION4 PRECIPCONDITION4QC PRECIPHISTDUR PRECIPHISTDURQC
## Min. :3 Min. :1 Mode:logical Min. :0
## 1st Qu.:3 1st Qu.:1 NA's:338952 1st Qu.:0
## Median :3 Median :1 Median :0
## Mean :3 Mean :1 Mean :0
## 3rd Qu.:3 3rd Qu.:1 3rd Qu.:0
## Max. :3 Max. :1 Max. :0
## NA's :338907 NA's :338907 NA's :329825
## PRECIPHISTCHAR PRECIPHISTCHARQC PRECIPDISC PRECIPDISCQC
## Mode:logical Mode:logical Min. :0.00 Mode:logical
## NA's:338952 NA's:338952 1st Qu.:1.00 NA's:338952
## Median :1.00
## Mean :0.91
## 3rd Qu.:1.00
## Max. :4.00
## NA's :288021
## PRECIPBOGUS PRECIPBOGUSQC PRECIPAMOUNTSD PRECIPAMOUNTSDQC
## Min. :0.0 Mode:logical Min. : 0.0 Min. :1
## 1st Qu.:0.0 NA's:338952 1st Qu.: 5.0 1st Qu.:1
## Median :0.0 Median : 13.0 Median :1
## Mean :0.3 Mean : 49.3 Mean :1
## 3rd Qu.:0.0 3rd Qu.: 20.0 3rd Qu.:1
## Max. :7.0 Max. :996.0 Max. :4
## NA's :324454 NA's :325733 NA's :334179
## PRECIPCONDITIONSD PRECIPCONDITIONSDQC DEPTHWTREQUIV DEPTHWTREQUIVQC
## Min. :1.0 Min. :1 Min. : 0.0 Min. :4
## 1st Qu.:3.0 1st Qu.:1 1st Qu.: 51.0 1st Qu.:4
## Median :3.0 Median :1 Median :127.0 Median :4
## Mean :2.8 Mean :1 Mean :138.7 Mean :4
## 3rd Qu.:3.0 3rd Qu.:1 3rd Qu.:180.0 3rd Qu.:4
## Max. :3.0 Max. :1 Max. :690.0 Max. :4
## NA's :333954 NA's :334413 NA's :334217 NA's :338441
## DEPTHWECOND DEPTHWECONDQC HAILSIZE PRECIPAMOUNTSF1 PRECIPAMOUNTSF1QC
## Mode:logical Mode:logical Mode:logical Mode:logical Mode:logical
## NA's:338952 NA's:338952 NA's:338952 NA's:338952 NA's:338952
##
##
##
##
##
## PRECIPCONDITIONSF1 PRECIPCONDITIONSF1QC OBSERVATIONPERIODSF1
## Mode:logical Mode:logical Min. :1
## NA's:338952 NA's:338952 1st Qu.:1
## Median :1
## Mean :1
## 3rd Qu.:1
## Max. :1
## NA's :338701
## OBSERVATIONPERIODSF1QC PRECIPAMOUNTSF2 PRECIPAMOUNTSF2QC PRECIPCONDITIONSF2
## Min. :1 Mode:logical Mode:logical Mode:logical
## 1st Qu.:1 NA's:338952 NA's:338952 NA's:338952
## Median :1
## Mean :1
## 3rd Qu.:1
## Max. :1
## NA's :338701
## PRECIPCONDITIONSF2QC OBSERVATIONPERIODSF2 OBSERVATIONPERIODSF2QC
## Mode:logical Mode:logical Mode:logical
## NA's:338952 NA's:338952 NA's:338952
##
##
##
##
##
## PRECIPAMOUNTSF3 PRECIPAMOUNTSF3QC PRECIPCONDITIONSF3 PRECIPCONDITIONSF3QC
## Mode:logical Mode:logical Mode:logical Mode:logical
## NA's:338952 NA's:338952 NA's:338952 NA's:338952
##
##
##
##
##
## OBSERVATIONPERIODSF3 OBSERVATIONPERIODSF3QC PRECIPAMOUNTSF4 PRECIPAMOUNTSF4QC
## Mode:logical Mode:logical Mode:logical Mode:logical
## NA's:338952 NA's:338952 NA's:338952 NA's:338952
##
##
##
##
##
## PRECIPCONDITIONSF4 PRECIPCONDITIONSF4QC OBSERVATIONPERIODSF4
## Mode:logical Mode:logical Mode:logical
## NA's:338952 NA's:338952 NA's:338952
##
##
##
##
##
## OBSERVATIONPERIODSF4QC PRESENTMANUAL1 PRESENTMANUAL1QC PRESENTMANUAL2
## Mode:logical Min. : 0.00 Min. :0.00 Min. : 0.00
## NA's:338952 1st Qu.: 0.00 1st Qu.:1.00 1st Qu.: 0.00
## Median :61.00 Median :1.00 Median : 0.00
## Mean :36.87 Mean :0.95 Mean : 5.77
## 3rd Qu.:61.00 3rd Qu.:1.00 3rd Qu.:10.00
## Max. :97.00 Max. :4.00 Max. :89.00
## NA's :189418 NA's :182264 NA's :253836
## PRESENTMANUAL2QC PRESENTMANUAL3 PRESENTMANUAL3QC PRESENTMANUAL4
## Min. :0.00 Min. : 0.00 Min. :0 Min. : 0.0
## 1st Qu.:1.00 1st Qu.: 0.00 1st Qu.:1 1st Qu.: 0.0
## Median :1.00 Median : 0.00 Median :1 Median : 0.0
## Mean :0.98 Mean : 0.56 Mean :1 Mean : 4.1
## 3rd Qu.:1.00 3rd Qu.: 0.00 3rd Qu.:1 3rd Qu.:10.0
## Max. :4.00 Max. :87.00 Max. :4 Max. :66.0
## NA's :218954 NA's :271903 NA's :233075 NA's :338582
## PRESENTMANUAL4QC PRESENTMANUAL5 PRESENTMANUAL5QC PRESENTMANUAL6
## Min. :1 Min. : 0.0 Min. :1 Min. : 0.0
## 1st Qu.:1 1st Qu.: 0.0 1st Qu.:1 1st Qu.: 0.0
## Median :1 Median : 0.0 Median :1 Median : 0.0
## Mean :1 Mean :15.5 Mean :1 Mean : 1.6
## 3rd Qu.:1 3rd Qu.:10.0 3rd Qu.:1 3rd Qu.: 0.0
## Max. :4 Max. :71.0 Max. :4 Max. :10.0
## NA's :240209 NA's :338928 NA's :240261 NA's :338933
## PRESENTMANUAL6QC PRESENTMANUAL7 PRESENTMANUAL7QC PRESENTAUTOMATED1
## Min. :1 Min. :0 Min. :1 Min. : 4.00
## 1st Qu.:1 1st Qu.:0 1st Qu.:1 1st Qu.:61.00
## Median :1 Median :0 Median :1 Median :61.00
## Mean :1 Mean :0 Mean :1 Mean :56.85
## 3rd Qu.:1 3rd Qu.:0 3rd Qu.:1 3rd Qu.:61.00
## Max. :1 Max. :0 Max. :1 Max. :92.00
## NA's :240265 NA's :338947 NA's :240267 NA's :288353
## PRESENTAUTOMATED1QC PRESENTAUTOMATED2 PRESENTAUTOMATED2QC PRESENTAUTOMATED3
## Min. :0.00 Min. :10.0 Min. :0 Min. :10.0
## 1st Qu.:1.00 1st Qu.:10.0 1st Qu.:1 1st Qu.:10.0
## Median :1.00 Median :10.0 Median :1 Median :31.0
## Mean :0.84 Mean :11.3 Mean :1 Mean :32.3
## 3rd Qu.:1.00 3rd Qu.:10.0 3rd Qu.:1 3rd Qu.:54.0
## Max. :5.00 Max. :72.0 Max. :5 Max. :67.0
## NA's :277663 NA's :321077 NA's :321069 NA's :338759
## PRESENTAUTOMATED3QC PASTMANUAL1 PASTMANUAL1QC WXPASTPERIOD1
## Min. :1.0 Mode:logical Min. :0 Mode:logical
## 1st Qu.:1.0 NA's:338952 1st Qu.:0 NA's:338952
## Median :1.0 Median :0
## Mean :2.5 Mean :0
## 3rd Qu.:4.0 3rd Qu.:0
## Max. :4.0 Max. :0
## NA's :338759 NA's :330958
## WXPASTPERIOD1QC PASTMANUAL2 PASTMANUAL2QC WXPASTPERIOD2 WXPASTPERIOD2QC
## Mode:logical Mode:logical Min. :0 Mode:logical Mode:logical
## NA's:338952 NA's:338952 1st Qu.:0 NA's:338952 NA's:338952
## Median :0
## Mean :0
## 3rd Qu.:0
## Max. :0
## NA's :336525
## PASTAUTOMATED1 PASTAUTOMATED1QC WXPASTAUTOPERIOD1 WXPASTAUTOPERIOD1QC
## Mode:logical Min. :0 Mode:logical Mode:logical
## NA's:338952 1st Qu.:0 NA's:338952 NA's:338952
## Median :0
## Mean :0
## 3rd Qu.:0
## Max. :0
## NA's :330958
## PASTAUTOMATED2 PASTAUTOMATED2QC WXPASTAUTOPERIOD2 WXPASTAUTOPERIOD2QC
## Mode:logical Min. :0 Mode:logical Mode:logical
## NA's:338952 1st Qu.:0 NA's:338952 NA's:338952
## Median :0
## Mean :0
## 3rd Qu.:0
## Max. :0
## NA's :336525
## RUNWAYENDBEARING RUNWAYDESIGNATOR RUNWAYVISUALRANGE CLOUDCOVER
## Min. : 8.0 Length:338952 Min. : 30 Min. : 0.00
## 1st Qu.: 8.0 Class :character 1st Qu.: 488 1st Qu.: 6.00
## Median :26.0 Mode :character Median : 914 Median : 8.00
## Mean :18.3 Mean :1020 Mean : 6.41
## 3rd Qu.:26.0 3rd Qu.:1524 3rd Qu.: 8.00
## Max. :27.0 Max. :1829 Max. :10.00
## NA's :335642 NA's :335642 NA's :92291
## CLOUDCOVERQC CLOUDCOVERLO CLOUDCOVERLOQC CLOUDBASEHEIGHT
## Min. :0.0 Min. :0.0 Min. :0.0 Min. : 0.0
## 1st Qu.:1.0 1st Qu.:0.0 1st Qu.:0.0 1st Qu.: 150.0
## Median :1.0 Median :0.0 Median :0.0 Median : 366.0
## Mean :1.1 Mean :0.8 Mean :0.1 Mean : 885.3
## 3rd Qu.:1.0 3rd Qu.:0.0 3rd Qu.:0.0 3rd Qu.: 1128.0
## Max. :4.0 Max. :9.0 Max. :1.0 Max. :10668.0
## NA's :79582 NA's :337425 NA's :324082 NA's :205022
## CLOUDBASEHEIGHTQC CLOUDTYPELO CLOUDTYPELOQC CLOUDTYPEMID
## Min. :0.00 Min. :0.0 Min. :0.0 Min. :0.0
## 1st Qu.:1.00 1st Qu.:0.0 1st Qu.:0.0 1st Qu.:0.0
## Median :1.00 Median :0.0 Median :0.0 Median :0.0
## Mean :0.99 Mean :1.7 Mean :0.1 Mean :0.7
## 3rd Qu.:1.00 3rd Qu.:5.0 3rd Qu.:0.0 3rd Qu.:0.0
## Max. :1.00 Max. :8.0 Max. :1.0 Max. :9.0
## NA's :204090 NA's :336727 NA's :323384 NA's :337219
## CLOUDTYPEMIDQC CLOUDTYPEHI CLOUDTYPEHIQC SUNSHINE
## Min. :0.0 Min. :0.0 Min. :0.0 Mode:logical
## 1st Qu.:0.0 1st Qu.:0.0 1st Qu.:0.0 NA's:338952
## Median :0.0 Median :0.0 Median :0.0
## Mean :0.1 Mean :0.4 Mean :0.1
## 3rd Qu.:0.0 3rd Qu.:0.0 3rd Qu.:0.0
## Max. :1.0 Max. :9.0 Max. :1.0
## NA's :323876 NA's :337385 NA's :324042
## SURFACECODE SURFACECODEQC SOILTEMPERATURE SOILTEMPERATUREQC
## Min. :1 Mode:logical Mode:logical Mode:logical
## 1st Qu.:1 NA's:338952 NA's:338952 NA's:338952
## Median :1
## Mean :1
## 3rd Qu.:1
## Max. :1
## NA's :338950
## SOILDEPTH OBSERVATIONPERIODSOILT OBSERVATIONPERIODSOILTQC
## Mode:logical Mode:logical Mode:logical
## NA's:338952 NA's:338952 NA's:338952
##
##
##
##
##
## ALTIMETERSETTING ALTIMETERSETTINGQC STATIONPRESSURE STATIONPRESSUREQC
## Min. : 960 Min. :0.000 Min. : 959.2 Min. :0.00
## 1st Qu.:1005 1st Qu.:1.000 1st Qu.:1004.8 1st Qu.:1.00
## Median :1013 Median :1.000 Median :1012.2 Median :1.00
## Mean :1012 Mean :0.995 Mean :1011.0 Mean :0.77
## 3rd Qu.:1019 3rd Qu.:1.000 3rd Qu.:1018.0 3rd Qu.:1.00
## Max. :1084 Max. :5.000 Max. :1045.0 Max. :5.00
## NA's :33334 NA's :31151 NA's :287933 NA's :271974
## PRESSURETENDENCY PRESSURETENDENCYQC PRESSURE3HOURCHG PRESSURE3HOURCHGQC
## Min. :0.00 Min. :0.00 Min. :-10.20 Min. :0.00
## 1st Qu.:2.00 1st Qu.:1.00 1st Qu.: 0.20 1st Qu.:1.00
## Median :3.00 Median :1.00 Median : 0.60 Median :1.00
## Mean :4.37 Mean :0.86 Mean : 0.79 Mean :0.87
## 3rd Qu.:7.00 3rd Qu.:1.00 3rd Qu.: 1.40 3rd Qu.:1.00
## Max. :8.00 Max. :5.00 Max. : 40.60 Max. :5.00
## NA's :248516 NA's :232420 NA's :244280 NA's :228862
## PRESSURE24HOURCHG PRESSURE24HOURCHGQC PRESSURETREND ISOBARICSURFACE
## Min. :0.0 Min. :0 Mode:logical Min. :4
## 1st Qu.:0.0 1st Qu.:0 NA's:338952 1st Qu.:4
## Median :0.0 Median :0 Median :4
## Mean :0.0 Mean :0 Mean :4
## 3rd Qu.:0.0 3rd Qu.:0 3rd Qu.:4
## Max. :0.3 Max. :1 Max. :4
## NA's :338094 NA's :320084 NA's :338951
## ISOBARICSURFACEQC ISOBARICSURFACEHEIGHT ISOBARICSURFACEHEIGHTQC SEASURFACETEMP
## Mode:logical Min. :2944 Mode:logical Mode:logical
## NA's:338952 1st Qu.:2944 NA's:338952 NA's:338952
## Median :2944
## Mean :2944
## 3rd Qu.:2944
## Max. :2944
## NA's :338951
## SEASURFACETEMPQC REMARKSYN REMARKMET REMARKAWY
## Min. :5 Length:338952 Length:338952 Length:338952
## 1st Qu.:5 Class :character Class :character Class :character
## Median :5 Mode :character Mode :character Mode :character
## Mean :5
## 3rd Qu.:5
## Max. :5
## NA's :338950
## HORIZONTALDATUM VERTICALDATUM LIGHTNINGFREQUENCY
## Length:338952 Length:338952 Mode:logical
## Class :character Class :character NA's:338952
## Mode :character Mode :character
##
##
##
##
## RECEIPTDTG INSERTIONTIME BLKSTN
## Min. :20130500000000 Length:338952 Min. :703810
## 1st Qu.:20151200000000 Class :character 1st Qu.:703810
## Median :20181000000000 Mode :character Median :703810
## Mean :20182663424500 Mean :703810
## 3rd Qu.:20210500000000 3rd Qu.:703810
## Max. :20231200000000 Max. :703810
## NA's :212050 NA's :126908El Paso, TX
summary(kelp_data)## PLATFORMID NETWORKTYPE OBSERVATIONTIME REPORTTYPECODE
## Length:333824 Length:333824 Length:333824 Length:333824
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## LATITUDE LONGITUDE MONTH SECURITYID
## Min. :31.79 Min. :-106.4 Min. : 1.000 Min. :1
## 1st Qu.:31.80 1st Qu.:-106.4 1st Qu.: 3.000 1st Qu.:1
## Median :31.80 Median :-106.4 Median : 7.000 Median :1
## Mean :31.80 Mean :-106.4 Mean : 6.509 Mean :1
## 3rd Qu.:31.81 3rd Qu.:-106.4 3rd Qu.:10.000 3rd Qu.:1
## Max. :31.81 Max. :-106.4 Max. :12.000 Max. :1
##
## DISTRIBUTIONCD STATIONMODE PLATFORMHEIGHT CALLLETTER
## Length:333824 Min. :0.00 Min. :1194 Length:333824
## Class :character 1st Qu.:0.00 1st Qu.:1194 Class :character
## Mode :character Median :0.00 Median :1194 Mode :character
## Mean :0.26 Mean :1198
## 3rd Qu.:1.00 3rd Qu.:1206
## Max. :1.00 Max. :1206
## NA's :203629
## VERSION WINDDIRECTION WINDDIRECTIONQC WINDCONDITIONS
## Length:333824 Min. : 10.0 Min. :0.00 Length:333824
## Class :character 1st Qu.:100.0 1st Qu.:1.00 Class :character
## Mode :character Median :180.0 Median :1.00 Mode :character
## Mean :183.7 Mean :0.99
## 3rd Qu.:260.0 3rd Qu.:1.00
## Max. :360.0 Max. :4.00
## NA's :55389 NA's :47220
## WINDCONDITIONSQC WINDSPEED WINDSPEEDQC STARTDIRECTION
## Min. :1.00 Min. : 0.000 Min. :0.000 Min. : 10.0
## 1st Qu.:1.00 1st Qu.: 2.100 1st Qu.:1.000 1st Qu.:120.0
## Median :1.00 Median : 3.100 Median :1.000 Median :200.0
## Mean :1.02 Mean : 3.837 Mean :1.007 Mean :184.3
## 3rd Qu.:1.00 3rd Qu.: 5.100 3rd Qu.:1.000 3rd Qu.:240.0
## Max. :5.00 Max. :27.300 Max. :5.000 Max. :360.0
## NA's :205785 NA's :1325 NA's :2156 NA's :332776
## ENDDIRECTION WINDGUSTSPEED WINDGUSTSPEEDQC WINDMEASUREMENTMODE
## Min. : 10.0 Min. : 0.00 Min. :0.00 Min. :1
## 1st Qu.:170.0 1st Qu.: 9.30 1st Qu.:0.00 1st Qu.:4
## Median :260.0 Median :11.30 Median :1.00 Median :4
## Mean :230.8 Mean :12.01 Mean :0.96 Mean :4
## 3rd Qu.:300.0 3rd Qu.:13.90 3rd Qu.:1.00 3rd Qu.:4
## Max. :360.0 Max. :37.60 Max. :4.00 Max. :4
## NA's :332777 NA's :287522 NA's :271958 NA's :206652
## CLOUDCEILING CLOUDCEILINGQC CEILINGDETERMINATION CEILINGDETERMINATIONQC
## Min. : 0 Min. :0.00 Length:333824 Min. :0.00
## 1st Qu.: 7620 1st Qu.:1.00 Class :character 1st Qu.:0.00
## Median :22000 Median :1.00 Mode :character Median :0.00
## Mean :17270 Mean :1.39 Mean :0.01
## 3rd Qu.:22000 3rd Qu.:1.00 3rd Qu.:0.00
## Max. :22000 Max. :4.00 Max. :5.00
## NA's :40062 NA's :35622 NA's :315743
## CLOUDCAVOK CLOUDCAVOKQC VISIBILITY VISIBILITYQC
## Length:333824 Min. :1 Min. : 0 Min. :1
## Class :character 1st Qu.:1 1st Qu.: 16093 1st Qu.:1
## Mode :character Median :1 Median : 16093 Median :1
## Mean :1 Mean : 17297 Mean :1
## 3rd Qu.:1 3rd Qu.: 16093 3rd Qu.:1
## Max. :1 Max. :120700 Max. :4
## NA's :206565 NA's :209 NA's :207
## VISIBILITYTYPE VISIBILITYTYPEQC AIRTEMPERATURE AIRTEMPERATUREQC
## Length:333824 Min. :1 Min. :-17.00 Min. :0.000
## Class :character 1st Qu.:1 1st Qu.: 12.00 1st Qu.:1.000
## Mode :character Median :1 Median : 20.00 Median :1.000
## Mean :1 Mean : 19.19 Mean :1.001
## 3rd Qu.:1 3rd Qu.: 26.70 3rd Qu.:1.000
## Max. :4 Max. : 45.00 Max. :5.000
## NA's :563 NA's :338 NA's :332
## DEWPOINTTEMPERATURE DEWPOINTTEMPERATUREQC SEALEVELPRESSURE SEALEVELPRESSUREQC
## Min. :-29.40 Min. :0.000 Min. : 987.4 Min. :0.000
## 1st Qu.: -6.00 1st Qu.:1.000 1st Qu.:1008.0 1st Qu.:1.000
## Median : 1.00 Median :1.000 Median :1011.5 Median :1.000
## Mean : 1.45 Mean :1.002 Mean :1012.3 Mean :0.999
## 3rd Qu.: 9.00 3rd Qu.:1.000 3rd Qu.:1015.9 3rd Qu.:1.000
## Max. : 23.00 Max. :5.000 Max. :1038.7 Max. :5.000
## NA's :413 NA's :369 NA's :12623 NA's :12088
## OBSERVATIONPERIODPP1 OBSERVATIONPERIODPP1QC PRECIPAMOUNT1 PRECIPAMOUNT1QC
## Min. : 1.00 Min. :1.0 Min. : 0.00 Min. :0.0
## 1st Qu.: 1.00 1st Qu.:1.0 1st Qu.: 0.00 1st Qu.:1.0
## Median : 6.00 Median :1.0 Median : 0.00 Median :1.0
## Mean : 4.82 Mean :1.7 Mean : 0.76 Mean :1.6
## 3rd Qu.: 6.00 3rd Qu.:3.0 3rd Qu.: 0.20 3rd Qu.:3.0
## Max. :24.00 Max. :4.0 Max. :66.50 Max. :4.0
## NA's :299985 NA's :321156 NA's :296543 NA's :319520
## PRECIPCONDITION1 PRECIPCONDITION1QC OBSERVATIONPERIODPP2
## Min. :1.00 Min. :1.0 Min. : 1.0
## 1st Qu.:2.00 1st Qu.:1.0 1st Qu.: 3.0
## Median :2.00 Median :1.0 Median : 6.0
## Mean :2.16 Mean :1.6 Mean :11.9
## 3rd Qu.:2.00 3rd Qu.:3.0 3rd Qu.:24.0
## Max. :3.00 Max. :4.0 Max. :24.0
## NA's :308095 NA's :319599 NA's :329579
## OBSERVATIONPERIODPP2QC PRECIPAMOUNT2 PRECIPAMOUNT2QC PRECIPCONDITION2
## Min. :1 Min. : 0.0 Min. :0.0 Min. :0.0
## 1st Qu.:1 1st Qu.: 0.0 1st Qu.:1.0 1st Qu.:2.0
## Median :1 Median : 0.5 Median :1.0 Median :2.0
## Mean :1 Mean : 2.8 Mean :0.9 Mean :2.5
## 3rd Qu.:1 3rd Qu.: 2.5 3rd Qu.:1.0 3rd Qu.:3.0
## Max. :1 Max. :298.1 Max. :4.0 Max. :3.0
## NA's :332354 NA's :329627 NA's :332113 NA's :331581
## PRECIPCONDITION2QC OBSERVATIONPERIODPP3 OBSERVATIONPERIODPP3QC
## Min. :1 Min. : 1.0 Min. :1
## 1st Qu.:1 1st Qu.:24.0 1st Qu.:1
## Median :1 Median :24.0 Median :1
## Mean :1 Mean :19.6 Mean :1
## 3rd Qu.:1 3rd Qu.:24.0 3rd Qu.:1
## Max. :1 Max. :24.0 Max. :1
## NA's :332113 NA's :333604 NA's :333727
## PRECIPAMOUNT3 PRECIPAMOUNT3QC PRECIPCONDITION3 PRECIPCONDITION3QC
## Min. : 0.0 Min. :1 Min. :2.0 Min. :1
## 1st Qu.: 0.7 1st Qu.:1 1st Qu.:3.0 1st Qu.:1
## Median : 2.4 Median :1 Median :3.0 Median :1
## Mean : 6.2 Mean :1 Mean :2.9 Mean :1
## 3rd Qu.: 7.2 3rd Qu.:1 3rd Qu.:3.0 3rd Qu.:1
## Max. :49.7 Max. :1 Max. :3.0 Max. :1
## NA's :333604 NA's :333711 NA's :333693 NA's :333711
## OBSERVATIONPERIODPP4 OBSERVATIONPERIODPP4QC PRECIPAMOUNT4 PRECIPAMOUNT4QC
## Mode:logical Mode:logical Mode:logical Mode:logical
## NA's:333824 NA's:333824 NA's:333824 NA's:333824
##
##
##
##
##
## PRECIPCONDITION4 PRECIPCONDITION4QC PRECIPHISTDUR PRECIPHISTDURQC
## Mode:logical Mode:logical Min. :0.0 Min. :0
## NA's:333824 NA's:333824 1st Qu.:1.0 1st Qu.:0
## Median :1.0 Median :0
## Mean :1.3 Mean :0
## 3rd Qu.:2.0 3rd Qu.:0
## Max. :3.0 Max. :0
## NA's :333662 NA's :332351
## PRECIPHISTCHAR PRECIPHISTCHARQC PRECIPDISC PRECIPDISCQC
## Length:333824 Mode:logical Min. :0.00 Mode:logical
## Class :character NA's:333824 1st Qu.:1.00 NA's:333824
## Mode :character Median :1.00
## Mean :1.15
## 3rd Qu.:1.00
## Max. :5.00
## NA's :285242
## PRECIPBOGUS PRECIPBOGUSQC PRECIPAMOUNTSD PRECIPAMOUNTSDQC
## Min. : 0.0 Mode:logical Min. : 0.0 Min. :1
## 1st Qu.: 0.0 NA's:333824 1st Qu.: 0.0 1st Qu.:1
## Median : 0.0 Median : 3.0 Median :1
## Mean : 0.1 Mean : 4.9 Mean :1
## 3rd Qu.: 0.0 3rd Qu.: 5.0 3rd Qu.:1
## Max. :17.0 Max. :996.0 Max. :1
## NA's :319424 NA's :333268 NA's :333787
## PRECIPCONDITIONSD PRECIPCONDITIONSDQC DEPTHWTREQUIV DEPTHWTREQUIVQC
## Min. :1.0 Min. :1 Min. : 0.0 Mode:logical
## 1st Qu.:3.0 1st Qu.:1 1st Qu.: 25.0 NA's:333824
## Median :3.0 Median :1 Median : 30.0
## Mean :2.8 Mean :1 Mean : 47.3
## 3rd Qu.:3.0 3rd Qu.:1 3rd Qu.: 51.0
## Max. :3.0 Max. :1 Max. :152.0
## NA's :333790 NA's :333791 NA's :333787
## DEPTHWECOND DEPTHWECONDQC HAILSIZE PRECIPAMOUNTSF1 PRECIPAMOUNTSF1QC
## Mode:logical Mode:logical Mode:logical Mode:logical Mode:logical
## NA's:333824 NA's:333824 NA's:333824 NA's:333824 NA's:333824
##
##
##
##
##
## PRECIPCONDITIONSF1 PRECIPCONDITIONSF1QC OBSERVATIONPERIODSF1
## Mode:logical Mode:logical Min. :1
## NA's:333824 NA's:333824 1st Qu.:1
## Median :1
## Mean :1
## 3rd Qu.:1
## Max. :1
## NA's :333814
## OBSERVATIONPERIODSF1QC PRECIPAMOUNTSF2 PRECIPAMOUNTSF2QC PRECIPCONDITIONSF2
## Min. :1 Mode:logical Mode:logical Mode:logical
## 1st Qu.:1 NA's:333824 NA's:333824 NA's:333824
## Median :1
## Mean :1
## 3rd Qu.:1
## Max. :1
## NA's :333814
## PRECIPCONDITIONSF2QC OBSERVATIONPERIODSF2 OBSERVATIONPERIODSF2QC
## Mode:logical Mode:logical Mode:logical
## NA's:333824 NA's:333824 NA's:333824
##
##
##
##
##
## PRECIPAMOUNTSF3 PRECIPAMOUNTSF3QC PRECIPCONDITIONSF3 PRECIPCONDITIONSF3QC
## Mode:logical Mode:logical Mode:logical Mode:logical
## NA's:333824 NA's:333824 NA's:333824 NA's:333824
##
##
##
##
##
## OBSERVATIONPERIODSF3 OBSERVATIONPERIODSF3QC PRECIPAMOUNTSF4 PRECIPAMOUNTSF4QC
## Mode:logical Mode:logical Mode:logical Mode:logical
## NA's:333824 NA's:333824 NA's:333824 NA's:333824
##
##
##
##
##
## PRECIPCONDITIONSF4 PRECIPCONDITIONSF4QC OBSERVATIONPERIODSF4
## Mode:logical Mode:logical Mode:logical
## NA's:333824 NA's:333824 NA's:333824
##
##
##
##
##
## OBSERVATIONPERIODSF4QC PRESENTMANUAL1 PRESENTMANUAL1QC PRESENTMANUAL2
## Mode:logical Min. : 0.0 Min. :0.00 Min. : 0.00
## NA's:333824 1st Qu.: 0.0 1st Qu.:1.00 1st Qu.: 0.00
## Median : 0.0 Median :1.00 Median : 0.00
## Mean : 6.4 Mean :0.99 Mean : 1.96
## 3rd Qu.: 0.0 3rd Qu.:1.00 3rd Qu.: 0.00
## Max. :99.0 Max. :5.00 Max. :99.00
## NA's :252811 NA's :252117 NA's :274388
## PRESENTMANUAL2QC PRESENTMANUAL3 PRESENTMANUAL3QC PRESENTMANUAL4
## Min. :0 Min. : 0.00 Min. :1 Min. : 0.0
## 1st Qu.:1 1st Qu.: 0.00 1st Qu.:1 1st Qu.: 0.0
## Median :1 Median : 0.00 Median :1 Median : 0.0
## Mean :1 Mean : 0.09 Mean :1 Mean : 4.7
## 3rd Qu.:1 3rd Qu.: 0.00 3rd Qu.:1 3rd Qu.: 0.0
## Max. :1 Max. :89.00 Max. :1 Max. :90.0
## NA's :257997 NA's :275911 NA's :258859 NA's :333805
## PRESENTMANUAL4QC PRESENTMANUAL5 PRESENTMANUAL5QC PRESENTMANUAL6
## Min. :1 Min. :18 Min. :1 Mode:logical
## 1st Qu.:1 1st Qu.:18 1st Qu.:1 NA's:333824
## Median :1 Median :18 Median :1
## Mean :1 Mean :18 Mean :1
## 3rd Qu.:1 3rd Qu.:18 3rd Qu.:1
## Max. :1 Max. :18 Max. :1
## NA's :264138 NA's :333823 NA's :264140
## PRESENTMANUAL6QC PRESENTMANUAL7 PRESENTMANUAL7QC PRESENTAUTOMATED1
## Min. :1 Mode:logical Min. :1 Min. : 4
## 1st Qu.:1 NA's:333824 1st Qu.:1 1st Qu.:61
## Median :1 Median :1 Median :61
## Mean :1 Mean :1 Mean :66
## 3rd Qu.:1 3rd Qu.:1 3rd Qu.:91
## Max. :1 Max. :1 Max. :96
## NA's :264140 NA's :264140 NA's :328742
## PRESENTAUTOMATED1QC PRESENTAUTOMATED2 PRESENTAUTOMATED2QC PRESENTAUTOMATED3
## Min. :0.0 Min. : 4.0 Min. :0.0 Min. :18.0
## 1st Qu.:0.0 1st Qu.:10.0 1st Qu.:1.0 1st Qu.:18.0
## Median :0.0 Median :10.0 Median :1.0 Median :54.0
## Mean :0.3 Mean :16.6 Mean :1.2 Mean :37.9
## 3rd Qu.:1.0 3rd Qu.:10.0 3rd Qu.:1.0 3rd Qu.:54.0
## Max. :5.0 Max. :95.0 Max. :4.0 Max. :64.0
## NA's :318700 NA's :333271 NA's :333268 NA's :333809
## PRESENTAUTOMATED3QC PASTMANUAL1 PASTMANUAL1QC WXPASTPERIOD1
## Min. :1.0 Min. :0.0 Min. :0.0 Min. :1.0
## 1st Qu.:1.0 1st Qu.:2.0 1st Qu.:0.0 1st Qu.:6.0
## Median :4.0 Median :8.0 Median :0.0 Median :6.0
## Mean :2.8 Mean :5.8 Mean :0.2 Mean :5.9
## 3rd Qu.:4.0 3rd Qu.:8.0 3rd Qu.:0.0 3rd Qu.:6.0
## Max. :4.0 Max. :9.0 Max. :1.0 Max. :6.0
## NA's :333809 NA's :333589 NA's :332774 NA's :333589
## WXPASTPERIOD1QC PASTMANUAL2 PASTMANUAL2QC WXPASTPERIOD2
## Min. :1 Min. :0.0 Min. :0.0 Min. :1.0
## 1st Qu.:1 1st Qu.:1.0 1st Qu.:0.0 1st Qu.:6.0
## Median :1 Median :2.0 Median :1.0 Median :6.0
## Mean :1 Mean :2.6 Mean :0.7 Mean :5.9
## 3rd Qu.:1 3rd Qu.:2.0 3rd Qu.:1.0 3rd Qu.:6.0
## Max. :1 Max. :8.0 Max. :1.0 Max. :6.0
## NA's :333589 NA's :333589 NA's :333501 NA's :333589
## WXPASTPERIOD2QC PASTAUTOMATED1 PASTAUTOMATED1QC WXPASTAUTOPERIOD1
## Min. :1 Mode:logical Min. :0 Mode:logical
## 1st Qu.:1 NA's:333824 1st Qu.:0 NA's:333824
## Median :1 Median :0
## Mean :1 Mean :0
## 3rd Qu.:1 3rd Qu.:0
## Max. :1 Max. :0
## NA's :333589 NA's :333009
## WXPASTAUTOPERIOD1QC PASTAUTOMATED2 PASTAUTOMATED2QC WXPASTAUTOPERIOD2
## Mode:logical Mode:logical Min. :0 Mode:logical
## NA's:333824 NA's:333824 1st Qu.:0 NA's:333824
## Median :0
## Mean :0
## 3rd Qu.:0
## Max. :0
## NA's :333736
## WXPASTAUTOPERIOD2QC RUNWAYENDBEARING RUNWAYDESIGNATOR RUNWAYVISUALRANGE
## Mode:logical Min. :22 Length:333824 Min. : 201.0
## NA's:333824 1st Qu.:22 Class :character 1st Qu.: 302.0
## Median :22 Mode :character Median : 352.0
## Mean :22 Mean : 708.1
## 3rd Qu.:22 3rd Qu.: 805.0
## Max. :22 Max. :2213.0
## NA's :333798 NA's :333798
## CLOUDCOVER CLOUDCOVERQC CLOUDCOVERLO CLOUDCOVERLOQC
## Min. : 0 Min. :0.00 Min. :0.0 Min. :0.00
## 1st Qu.: 0 1st Qu.:1.00 1st Qu.:0.0 1st Qu.:0.00
## Median : 2 Median :1.00 Median :0.0 Median :1.00
## Mean : 3 Mean :1.44 Mean :0.2 Mean :0.57
## 3rd Qu.: 4 3rd Qu.:1.00 3rd Qu.:0.0 3rd Qu.:1.00
## Max. :10 Max. :4.00 Max. :9.0 Max. :1.00
## NA's :42511 NA's :37433 NA's :319937 NA's :309356
## CLOUDBASEHEIGHT CLOUDBASEHEIGHTQC CLOUDTYPELO CLOUDTYPELOQC
## Min. : 0 Min. :0.00 Min. :0.0 Min. :0.00
## 1st Qu.: 1829 1st Qu.:1.00 1st Qu.:0.0 1st Qu.:0.00
## Median : 3048 Median :1.00 Median :0.0 Median :1.00
## Mean : 3590 Mean :0.98 Mean :0.2 Mean :0.59
## 3rd Qu.: 5182 3rd Qu.:1.00 3rd Qu.:0.0 3rd Qu.:1.00
## Max. :10668 Max. :1.00 Max. :9.0 Max. :1.00
## NA's :244121 NA's :242059 NA's :318424 NA's :307843
## CLOUDTYPEMID CLOUDTYPEMIDQC CLOUDTYPEHI CLOUDTYPEHIQC
## Min. :0.0 Min. :0.00 Min. :0.0 Min. :0.00
## 1st Qu.:0.0 1st Qu.:0.00 1st Qu.:0.0 1st Qu.:0.00
## Median :0.0 Median :1.00 Median :0.0 Median :1.00
## Mean :0.6 Mean :0.59 Mean :0.8 Mean :0.59
## 3rd Qu.:0.0 3rd Qu.:1.00 3rd Qu.:0.0 3rd Qu.:1.00
## Max. :8.0 Max. :1.00 Max. :9.0 Max. :1.00
## NA's :318635 NA's :308054 NA's :318592 NA's :308011
## SUNSHINE SURFACECODE SURFACECODEQC SOILTEMPERATURE
## Mode:logical Min. :1 Mode:logical Mode:logical
## NA's:333824 1st Qu.:1 NA's:333824 NA's:333824
## Median :1
## Mean :1
## 3rd Qu.:1
## Max. :1
## NA's :333821
## SOILTEMPERATUREQC SOILDEPTH OBSERVATIONPERIODSOILT
## Mode:logical Mode:logical Mode:logical
## NA's:333824 NA's:333824 NA's:333824
##
##
##
##
##
## OBSERVATIONPERIODSOILTQC ALTIMETERSETTING ALTIMETERSETTINGQC STATIONPRESSURE
## Mode:logical Min. : 986.8 Min. :0.00 Min. :861.1
## NA's:333824 1st Qu.:1014.6 1st Qu.:1.00 1st Qu.:878.7
## Median :1017.6 Median :1.00 Median :881.4
## Mean :1017.7 Mean :0.98 Mean :881.6
## 3rd Qu.:1020.7 3rd Qu.:1.00 3rd Qu.:884.4
## Max. :1039.6 Max. :5.00 Max. :901.3
## NA's :63805 NA's :59434 NA's :228536
## STATIONPRESSUREQC PRESSURETENDENCY PRESSURETENDENCYQC PRESSURE3HOURCHG
## Min. :0.00 Min. :0.00 Min. :0.00 Min. :-5.50
## 1st Qu.:1.00 1st Qu.:1.00 1st Qu.:1.00 1st Qu.: 0.20
## Median :1.00 Median :3.00 Median :1.00 Median : 0.70
## Mean :0.89 Mean :4.14 Mean :0.91 Mean : 0.75
## 3rd Qu.:1.00 3rd Qu.:6.00 3rd Qu.:1.00 3rd Qu.: 1.40
## Max. :5.00 Max. :8.00 Max. :5.00 Max. :11.00
## NA's :214913 NA's :207496 NA's :193486 NA's :202823
## PRESSURE3HOURCHGQC PRESSURE24HOURCHG PRESSURE24HOURCHGQC PRESSURETREND
## Min. :0.00 Min. :0 Min. :0.00 Mode:logical
## 1st Qu.:1.00 1st Qu.:0 1st Qu.:0.00 NA's:333824
## Median :1.00 Median :0 Median :0.00
## Mean :0.92 Mean :0 Mean :0.03
## 3rd Qu.:1.00 3rd Qu.:0 3rd Qu.:0.00
## Max. :5.00 Max. :0 Max. :1.00
## NA's :189536 NA's :333175 NA's :315196
## ISOBARICSURFACE ISOBARICSURFACEQC ISOBARICSURFACEHEIGHT
## Mode:logical Mode:logical Mode:logical
## NA's:333824 NA's:333824 NA's:333824
##
##
##
##
##
## ISOBARICSURFACEHEIGHTQC SEASURFACETEMP SEASURFACETEMPQC REMARKSYN
## Mode:logical Mode:logical Min. :5 Length:333824
## NA's:333824 NA's:333824 1st Qu.:5 Class :character
## Median :5 Mode :character
## Mean :5
## 3rd Qu.:5
## Max. :5
## NA's :333821
## REMARKMET REMARKAWY HORIZONTALDATUM VERTICALDATUM
## Length:333824 Length:333824 Length:333824 Length:333824
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## LIGHTNINGFREQUENCY RECEIPTDTG INSERTIONTIME
## Mode:logical Min. :20130500000000 Length:333824
## NA's:333824 1st Qu.:20151200000000 Class :character
## Median :20180800000000 Mode :character
## Mean :20182138943500
## 3rd Qu.:20210500000000
## Max. :20231200000000
## NA's :206560
## BLKSTN
## Min. :722700
## 1st Qu.:722700
## Median :722700
## Mean :722700
## 3rd Qu.:722700
## Max. :722700
## NA's :130195St George, UT
summary(ksgu_data)## PLATFORMID NETWORKTYPE OBSERVATIONTIME REPORTTYPECODE
## Length:484391 Length:484391 Length:484391 Length:484391
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## LATITUDE LONGITUDE MONTH SECURITYID
## Min. :37.07 Min. : -114 Length:484391 Min. :1
## 1st Qu.:37.08 1st Qu.: -114 Class :character 1st Qu.:1
## Median :37.08 Median : -114 Mode :character Median :1
## Mean :37.09 Mean : 41745673 Mean :1
## 3rd Qu.:37.09 3rd Qu.: -114 3rd Qu.:1
## Max. :42.70 Max. :20221200000000 Max. :1
## NA's :3 NA's :2 NA's :2
## DISTRIBUTIONCD STATIONMODE PLATFORMHEIGHT CALLLETTER
## Length:484391 Min. :0 Min. :896.0 Length:484391
## Class :character 1st Qu.:0 1st Qu.:896.0 Class :character
## Mode :character Median :0 Median :896.0 Mode :character
## Mean :0 Mean :896.1
## 3rd Qu.:0 3rd Qu.:896.4
## Max. :1 Max. :896.4
## NA's :324434 NA's :4
## VERSION WINDDIRECTION WINDDIRECTIONQC WINDCONDITIONS
## Length:484391 Min. : 10.0 Min. :0.00 Length:484391
## Class :character 1st Qu.: 90.0 1st Qu.:1.00 Class :character
## Mode :character Median :180.0 Median :1.00 Mode :character
## Mean :176.3 Mean :0.97
## 3rd Qu.:260.0 3rd Qu.:1.00
## Max. :360.0 Max. :1.00
## NA's :137503 NA's :117192
## WINDCONDITIONSQC WINDSPEED WINDSPEEDQC STARTDIRECTION
## Min. :1 Min. : 0.000 Min. :0.000 Min. : 10.0
## 1st Qu.:1 1st Qu.: 0.000 1st Qu.:1.000 1st Qu.:180.0
## Median :1 Median : 2.100 Median :1.000 Median :230.0
## Mean :1 Mean : 2.728 Mean :1.013 Mean :212.6
## 3rd Qu.:1 3rd Qu.: 3.600 3rd Qu.:1.000 3rd Qu.:260.0
## Max. :4 Max. :40.700 Max. :4.000 Max. :360.0
## NA's :322045 NA's :1011 NA's :840 NA's :482986
## ENDDIRECTION WINDGUSTSPEED WINDGUSTSPEEDQC WINDMEASUREMENTMODE
## Min. : 10.0 Min. : 7.2 Min. :0.0 Min. :4
## 1st Qu.:180.0 1st Qu.: 9.3 1st Qu.:0.0 1st Qu.:4
## Median :290.0 Median :11.3 Median :1.0 Median :4
## Mean :240.9 Mean :11.7 Mean :0.6 Mean :4
## 3rd Qu.:310.0 3rd Qu.:13.4 3rd Qu.:1.0 3rd Qu.:4
## Max. :360.0 Max. :48.9 Max. :4.0 Max. :4
## NA's :482986 NA's :432129 NA's :398324 NA's :324665
## CLOUDCEILING CLOUDCEILINGQC CEILINGDETERMINATION CEILINGDETERMINATIONQC
## Min. : 0 Min. :0.000 Length:484391 Min. :0
## 1st Qu.:22000 1st Qu.:1.000 Class :character 1st Qu.:0
## Median :22000 Median :1.000 Mode :character Median :0
## Mean :19816 Mean :1.056 Mean :0
## 3rd Qu.:22000 3rd Qu.:1.000 3rd Qu.:0
## Max. :22000 Max. :4.000 Max. :1
## NA's :18320 NA's :18130 NA's :446271
## CLOUDCAVOK CLOUDCAVOKQC VISIBILITY VISIBILITYQC
## Length:484391 Min. :1 Min. : 0 Min. :1.000
## Class :character 1st Qu.:1 1st Qu.:16093 1st Qu.:1.000
## Mode :character Median :1 Median :16093 Median :1.000
## Mean :1 Mean :15953 Mean :1.001
## 3rd Qu.:1 3rd Qu.:16093 3rd Qu.:1.000
## Max. :1 Max. :64374 Max. :4.000
## NA's :324434 NA's :1695 NA's :1695
## VISIBILITYTYPE VISIBILITYTYPEQC AIRTEMPERATURE AIRTEMPERATUREQC
## Length:484391 Min. :1 Min. :-17.0 Min. :0
## Class :character 1st Qu.:1 1st Qu.: 9.0 1st Qu.:1
## Mode :character Median :1 Median : 17.2 Median :1
## Mean :1 Mean : 17.9 Mean :1
## 3rd Qu.:1 3rd Qu.: 27.0 3rd Qu.:1
## Max. :1 Max. : 46.0 Max. :5
## NA's :233 NA's :811 NA's :645
## DEWPOINTTEMPERATURE DEWPOINTTEMPERATUREQC SEALEVELPRESSURE SEALEVELPRESSUREQC
## Min. :-24.0000 Min. :0 Min. : 0 Min. :0.0
## 1st Qu.: -5.0000 1st Qu.:1 1st Qu.:1009 1st Qu.:0.0
## Median : -1.0000 Median :1 Median :1013 Median :1.0
## Mean : -0.4715 Mean :1 Mean :1014 Mean :0.6
## 3rd Qu.: 3.0000 3rd Qu.:1 3rd Qu.:1019 3rd Qu.:1.0
## Max. : 24.0000 Max. :5 Max. :1039 Max. :4.0
## NA's :1665 NA's :1489 NA's :425357 NA's :387387
## OBSERVATIONPERIODPP1 OBSERVATIONPERIODPP1QC PRECIPAMOUNT1 PRECIPAMOUNT1QC
## Min. : 0.0 Min. :1 Min. : 0.0 Min. :1
## 1st Qu.: 1.0 1st Qu.:1 1st Qu.: 0.0 1st Qu.:1
## Median : 1.0 Median :1 Median : 0.0 Median :1
## Mean : 4.4 Mean :1 Mean : 1.3 Mean :1
## 3rd Qu.: 6.0 3rd Qu.:1 3rd Qu.: 0.5 3rd Qu.:1
## Max. :24.0 Max. :4 Max. :253.2 Max. :4
## NA's :470051 NA's :479509 NA's :470430 NA's :479484
## PRECIPCONDITION1 PRECIPCONDITION1QC OBSERVATIONPERIODPP2
## Min. :0.0 Min. :1 Min. : 1.0
## 1st Qu.:2.0 1st Qu.:1 1st Qu.: 3.0
## Median :2.0 Median :1 Median : 6.0
## Mean :2.4 Mean :1 Mean : 7.2
## 3rd Qu.:3.0 3rd Qu.:1 3rd Qu.: 6.0
## Max. :3.0 Max. :1 Max. :24.0
## NA's :479115 NA's :479115 NA's :483206
## OBSERVATIONPERIODPP2QC PRECIPAMOUNT2 PRECIPAMOUNT2QC PRECIPCONDITION2
## Min. :1 Min. : 0.0 Min. :0.0 Min. :1.0
## 1st Qu.:1 1st Qu.: 0.0 1st Qu.:1.0 1st Qu.:2.0
## Median :1 Median : 0.8 Median :1.0 Median :3.0
## Mean :1 Mean : 2.5 Mean :0.9 Mean :2.6
## 3rd Qu.:1 3rd Qu.: 2.8 3rd Qu.:1.0 3rd Qu.:3.0
## Max. :1 Max. :96.5 Max. :2.0 Max. :3.0
## NA's :483606 NA's :483216 NA's :483574 NA's :483560
## PRECIPCONDITION2QC OBSERVATIONPERIODPP3 OBSERVATIONPERIODPP3QC
## Min. :1 Min. : 1.0 Min. :0
## 1st Qu.:1 1st Qu.:24.0 1st Qu.:1
## Median :1 Median :24.0 Median :1
## Mean :1 Mean :18.8 Mean :1
## 3rd Qu.:1 3rd Qu.:24.0 3rd Qu.:1
## Max. :1 Max. :24.0 Max. :1
## NA's :483559 NA's :484293 NA's :484332
## PRECIPAMOUNT3 PRECIPAMOUNT3QC PRECIPCONDITION3 PRECIPCONDITION3QC
## Min. : 0.2 Min. :1 Min. :1.0 Min. :1
## 1st Qu.: 0.5 1st Qu.:1 1st Qu.:3.0 1st Qu.:1
## Median : 1.7 Median :1 Median :3.0 Median :1
## Mean : 4.3 Mean :1 Mean :2.8 Mean :1
## 3rd Qu.: 4.5 3rd Qu.:1 3rd Qu.:3.0 3rd Qu.:1
## Max. :44.5 Max. :1 Max. :3.0 Max. :1
## NA's :484298 NA's :484335 NA's :484330 NA's :484329
## OBSERVATIONPERIODPP4 OBSERVATIONPERIODPP4QC PRECIPAMOUNT4 PRECIPAMOUNT4QC
## Mode:logical Mode:logical Mode:logical Mode:logical
## NA's:484391 NA's:484391 NA's:484391 NA's:484391
##
##
##
##
##
## PRECIPCONDITION4 PRECIPCONDITION4QC PRECIPHISTDUR PRECIPHISTDURQC
## Mode:logical Mode:logical Mode:logical Min. :0
## NA's:484391 NA's:484391 NA's:484391 1st Qu.:0
## Median :0
## Mean :0
## 3rd Qu.:0
## Max. :0
## NA's :483930
## PRECIPHISTCHAR PRECIPHISTCHARQC PRECIPDISC PRECIPDISCQC
## Mode:logical Mode:logical Min. :0.0 Mode:logical
## NA's:484391 NA's:484391 1st Qu.:0.0 NA's:484391
## Median :0.0
## Mean :0.1
## 3rd Qu.:0.0
## Max. :3.0
## NA's :442834
## PRECIPBOGUS PRECIPBOGUSQC PRECIPAMOUNTSD PRECIPAMOUNTSDQC
## Min. :0 Mode:logical Min. :0.0 Mode:logical
## 1st Qu.:0 NA's:484391 1st Qu.:0.0 NA's:484391
## Median :0 Median :4.0
## Mean :0 Mean :3.1
## 3rd Qu.:0 3rd Qu.:5.0
## Max. :0 Max. :9.0
## NA's :470523 NA's :483904
## PRECIPCONDITIONSD PRECIPCONDITIONSDQC DEPTHWTREQUIV DEPTHWTREQUIVQC
## Mode:logical Min. :0 Min. :1 Mode:logical
## NA's:484391 1st Qu.:0 1st Qu.:1 NA's:484391
## Median :0 Median :1
## Mean :0 Mean :1
## 3rd Qu.:0 3rd Qu.:1
## Max. :0 Max. :1
## NA's :484390 NA's :484390
## DEPTHWECOND DEPTHWECONDQC HAILSIZE PRECIPAMOUNTSF1
## Min. :1 Mode:logical Min. :1 Mode:logical
## 1st Qu.:1 NA's:484391 1st Qu.:1 NA's:484391
## Median :1 Median :1
## Mean :1 Mean :1
## 3rd Qu.:1 3rd Qu.:1
## Max. :1 Max. :1
## NA's :484390 NA's :484390
## PRECIPAMOUNTSF1QC PRECIPCONDITIONSF1 PRECIPCONDITIONSF1QC OBSERVATIONPERIODSF1
## Min. :1 Mode:logical Min. :1 Mode:logical
## 1st Qu.:1 NA's:484391 1st Qu.:1 NA's:484391
## Median :1 Median :1
## Mean :1 Mean :1
## 3rd Qu.:1 3rd Qu.:1
## Max. :1 Max. :1
## NA's :484390 NA's :484390
## OBSERVATIONPERIODSF1QC PRECIPAMOUNTSF2 PRECIPAMOUNTSF2QC PRECIPCONDITIONSF2
## Min. :1 Mode:logical Min. :1 Mode:logical
## 1st Qu.:1 NA's:484391 1st Qu.:1 NA's:484391
## Median :1 Median :1
## Mean :1 Mean :1
## 3rd Qu.:1 3rd Qu.:1
## Max. :1 Max. :1
## NA's :484390 NA's :484390
## PRECIPCONDITIONSF2QC OBSERVATIONPERIODSF2 OBSERVATIONPERIODSF2QC
## Mode:logical Mode:logical Mode:logical
## NA's:484391 NA's:484391 NA's:484391
##
##
##
##
##
## PRECIPAMOUNTSF3 PRECIPAMOUNTSF3QC PRECIPCONDITIONSF3 PRECIPCONDITIONSF3QC
## Mode:logical Mode:logical Mode:logical Mode:logical
## NA's:484391 NA's:484391 NA's:484391 NA's:484391
##
##
##
##
##
## OBSERVATIONPERIODSF3 OBSERVATIONPERIODSF3QC PRECIPAMOUNTSF4 PRECIPAMOUNTSF4QC
## Mode:logical Mode:logical Mode:logical Mode:logical
## NA's:484391 NA's:484391 NA's:484391 NA's:484391
##
##
##
##
##
## PRECIPCONDITIONSF4 PRECIPCONDITIONSF4QC OBSERVATIONPERIODSF4
## Mode:logical Mode:logical Min. :1030
## NA's:484391 NA's:484391 1st Qu.:1030
## Median :1030
## Mean :1030
## 3rd Qu.:1030
## Max. :1030
## NA's :484390
## OBSERVATIONPERIODSF4QC PRESENTMANUAL1 PRESENTMANUAL1QC PRESENTMANUAL2
## Min. :1 Min. : 0.0 Min. :0 Min. : 0
## 1st Qu.:1 1st Qu.: 0.0 1st Qu.:1 1st Qu.: 0
## Median :1 Median : 0.0 Median :1 Median : 0
## Mean :1 Mean : 0.2 Mean :1 Mean : 0
## 3rd Qu.:1 3rd Qu.: 0.0 3rd Qu.:1 3rd Qu.: 0
## Max. :1 Max. :73.0 Max. :1 Max. :51
## NA's :484390 NA's :423991 NA's :423737 NA's :444590
## PRESENTMANUAL2QC PRESENTMANUAL3 PRESENTMANUAL3QC PRESENTMANUAL4
## Min. :1 Min. :0 Min. :1 Mode:logical
## 1st Qu.:1 1st Qu.:0 1st Qu.:1 NA's:484391
## Median :1 Median :0 Median :1
## Mean :1 Mean :0 Mean :1
## 3rd Qu.:1 3rd Qu.:0 3rd Qu.:1
## Max. :1 Max. :0 Max. :1
## NA's :426334 NA's :444596 NA's :426340
## PRESENTMANUAL4QC PRESENTMANUAL5 PRESENTMANUAL5QC PRESENTMANUAL6
## Min. : 1 Min. :1 Min. :1 Mode:logical
## 1st Qu.: 1 1st Qu.:1 1st Qu.:1 NA's:484391
## Median : 1 Median :1 Median :1
## Mean : 1 Mean :1 Mean :1
## 3rd Qu.: 1 3rd Qu.:1 3rd Qu.:1
## Max. :1025 Max. :1 Max. :1
## NA's :431085 NA's :484390 NA's :431086
## PRESENTMANUAL6QC PRESENTMANUAL7 PRESENTMANUAL7QC PRESENTAUTOMATED1
## Min. :1 Mode:logical Min. :1 Min. : 5.0
## 1st Qu.:1 NA's:484391 1st Qu.:1 1st Qu.:12.0
## Median :1 Median :1 Median :61.0
## Mean :1 Mean :1 Mean :47.5
## 3rd Qu.:1 3rd Qu.:1 3rd Qu.:62.0
## Max. :1 Max. :1 Max. :95.0
## NA's :431086 NA's :431086 NA's :479656
## PRESENTAUTOMATED1QC PRESENTAUTOMATED2 PRESENTAUTOMATED2QC PRESENTAUTOMATED3
## Min. :0.0 Min. : 5.0 Length:484391 Min. :10.0
## 1st Qu.:0.0 1st Qu.:10.0 Class :character 1st Qu.:10.0
## Median :0.0 Median :10.0 Mode :character Median :10.0
## Mean :0.2 Mean :21.2 Mean :13.2
## 3rd Qu.:0.0 3rd Qu.:18.0 3rd Qu.:18.0
## Max. :1.0 Max. :63.0 Max. :18.0
## NA's :460398 NA's :483833 NA's :484386
## PRESENTAUTOMATED3QC PASTMANUAL1 PASTMANUAL1QC WXPASTPERIOD1
## Min. :1 Mode:logical Min. :0 Length:484391
## 1st Qu.:1 NA's:484391 1st Qu.:0 Class :character
## Median :1 Median :0 Mode :character
## Mean :1 Mean :0
## 3rd Qu.:1 3rd Qu.:0
## Max. :1 Max. :0
## NA's :484386 NA's :484137
## WXPASTPERIOD1QC PASTMANUAL2 PASTMANUAL2QC WXPASTPERIOD2
## Min. :724754 Mode:logical Mode:logical Length:484391
## 1st Qu.:724754 NA's:484391 NA's:484391 Class :character
## Median :724754 Mode :character
## Mean :724754
## 3rd Qu.:724754
## Max. :724754
## NA's :484390
## WXPASTPERIOD2QC PASTAUTOMATED1 PASTAUTOMATED1QC WXPASTAUTOPERIOD1
## Mode:logical Mode:logical Min. : 0 Min. :1
## NA's:484391 NA's:484391 1st Qu.: 0 1st Qu.:1
## Median : 0 Median :1
## Mean : 4 Mean :1
## 3rd Qu.: 0 3rd Qu.:1
## Max. :1016 Max. :1
## NA's :484136 NA's :484390
## WXPASTAUTOPERIOD1QC PASTAUTOMATED2 PASTAUTOMATED2QC WXPASTAUTOPERIOD2
## Length:484391 Min. :724754 Mode:logical Mode:logical
## Class :character 1st Qu.:724754 NA's:484391 NA's:484391
## Mode :character Median :724754
## Mean :724754
## 3rd Qu.:724754
## Max. :724754
## NA's :484390
## WXPASTAUTOPERIOD2QC RUNWAYENDBEARING RUNWAYDESIGNATOR RUNWAYVISUALRANGE
## Mode:logical Mode:logical Length:484391 Min. :4
## NA's:484391 NA's:484391 Class :character 1st Qu.:4
## Mode :character Median :4
## Mean :4
## 3rd Qu.:4
## Max. :4
## NA's :484390
## CLOUDCOVER CLOUDCOVERQC CLOUDCOVERLO CLOUDCOVERLOQC
## Min. : 0.00 Min. :0.00 Min. :0 Min. :0
## 1st Qu.: 0.00 1st Qu.:1.00 1st Qu.:0 1st Qu.:0
## Median : 0.00 Median :1.00 Median :0 Median :0
## Mean : 0.73 Mean :1.04 Mean :0 Mean :0
## 3rd Qu.: 0.00 3rd Qu.:1.00 3rd Qu.:0 3rd Qu.:0
## Max. :10.00 Max. :4.00 Max. :0 Max. :1
## NA's :39324 NA's :36846 NA's :484385 NA's :481077
## CLOUDBASEHEIGHT CLOUDBASEHEIGHTQC CLOUDTYPELO CLOUDTYPELOQC
## Min. : 25 Min. :1 Mode:logical Min. :0
## 1st Qu.:1341 1st Qu.:1 NA's:484391 1st Qu.:0
## Median :2134 Median :1 Median :0
## Mean :2122 Mean :1 Mean :0
## 3rd Qu.:3048 3rd Qu.:1 3rd Qu.:0
## Max. :5486 Max. :1 Max. :0
## NA's :459412 NA's :459412 NA's :481083
## CLOUDTYPEMID CLOUDTYPEMIDQC CLOUDTYPEHI CLOUDTYPEHIQC
## Mode:logical Length:484391 Mode:logical Min. :0
## NA's:484391 Class :character NA's:484391 1st Qu.:0
## Mode :character Median :0
## Mean :0
## 3rd Qu.:0
## Max. :0
## NA's :481083
## SUNSHINE SURFACECODE SURFACECODEQC SOILTEMPERATURE
## Mode:logical Mode:logical Length:484391 Min. :724754
## NA's:484391 NA's:484391 Class :character 1st Qu.:724754
## Mode :character Median :724754
## Mean :724754
## 3rd Qu.:724754
## Max. :724754
## NA's :484390
## SOILTEMPERATUREQC SOILDEPTH OBSERVATIONPERIODSOILT
## Mode:logical Mode:logical Mode:logical
## NA's:484391 NA's:484391 NA's:484391
##
##
##
##
##
## OBSERVATIONPERIODSOILTQC ALTIMETERSETTING ALTIMETERSETTINGQC STATIONPRESSURE
## Mode:logical Min. : 985.1 Min. :0.0000 Mode:logical
## NA's:484391 1st Qu.:1012.2 1st Qu.:1.0000 NA's:484391
## Median :1015.9 Median :1.0000
## Mean :1016.3 Mean :0.9986
## 3rd Qu.:1020.0 3rd Qu.:1.0000
## Max. :1049.4 Max. :5.0000
## NA's :2061 NA's :1382
## STATIONPRESSUREQC PRESSURETENDENCY PRESSURETENDENCYQC PRESSURE3HOURCHG
## Min. :0 Min. :0.0 Min. :0.0 Min. :-6.2
## 1st Qu.:0 1st Qu.:2.0 1st Qu.:0.0 1st Qu.: 0.3
## Median :0 Median :3.0 Median :1.0 Median : 0.9
## Mean :0 Mean :4.1 Mean :0.6 Mean : 0.9
## 3rd Qu.:0 3rd Qu.:7.0 3rd Qu.:1.0 3rd Qu.: 1.7
## Max. :0 Max. :8.0 Max. :5.0 Max. : 9.5
## NA's :446307 NA's :442703 NA's :403769 NA's :441059
## PRESSURE3HOURCHGQC PRESSURE24HOURCHG PRESSURE24HOURCHGQC PRESSURETREND
## Min. :0.0 Mode:logical Min. :0 Mode:logical
## 1st Qu.:0.0 NA's:484391 1st Qu.:0 NA's:484391
## Median :1.0 Median :0
## Mean :0.6 Mean :0
## 3rd Qu.:1.0 3rd Qu.:0
## Max. :5.0 Max. :0
## NA's :402135 NA's :446307
## ISOBARICSURFACE ISOBARICSURFACEQC ISOBARICSURFACEHEIGHT
## Mode:logical Mode:logical Mode:logical
## NA's:484391 NA's:484391 NA's:484391
##
##
##
##
##
## ISOBARICSURFACEHEIGHTQC SEASURFACETEMP SEASURFACETEMPQC REMARKSYN
## Mode:logical Mode:logical Min. :5 Mode:logical
## NA's:484391 NA's:484391 1st Qu.:5 NA's:484391
## Median :5
## Mean :5
## 3rd Qu.:5
## Max. :5
## NA's :484384
## REMARKMET REMARKAWY HORIZONTALDATUM VERTICALDATUM
## Length:484391 Length:484391 Length:484391 Length:484391
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## LIGHTNINGFREQUENCY RECEIPTDTG INSERTIONTIME
## Mode:logical Min. :20130500000000 Length:484391
## NA's:484391 1st Qu.:20141100000000 Class :character
## Median :20160600000000 Mode :character
## Mean :20168681186200
## 3rd Qu.:20190800000000
## Max. :20231200000000
## NA's :324434
## BLKSTN
## Min. :724754
## 1st Qu.:724754
## Median :724754
## Mean :724754
## 3rd Qu.:724754
## Max. :724754
## NA's :159965Domain 3
Data Preparation
Handling NULL Values:
We can see the initial NULL counts by column. We will be taking the Julian Day average for each of these values to try to find some statistical relevance for the data we are using.
This turned trickier than planned as we wanted to use 10-year, 20-year and 30-year datasets. In doing so, we separate the data as pre-2004, 2004-2013, and 2014-2023. We will filter the data to find values greater than or equal to 2014, then 2004, then the entire datasets. Each of these will be stored as separate data frames for analysis.
Robert’s Field, OR
Initial Nulls:
colSums(is.na(krdm_data))## PLATFORMID NETWORKTYPE OBSERVATIONTIME
## 0 0 0
## REPORTTYPECODE LATITUDE LONGITUDE
## 0 4 4
## MONTH SECURITYID DISTRIBUTIONCD
## 4 4 0
## STATIONMODE PLATFORMHEIGHT CALLLETTER
## 179124 4 0
## VERSION WINDDIRECTION WINDDIRECTIONQC
## 4 71856 58087
## WINDCONDITIONS WINDCONDITIONSQC WINDSPEED
## 0 180863 646
## WINDSPEEDQC STARTDIRECTION ENDDIRECTION
## 1247 284285 284286
## WINDGUSTSPEED WINDGUSTSPEEDQC WINDMEASUREMENTMODE
## 253698 241106 182802
## CLOUDCEILING CLOUDCEILINGQC CEILINGDETERMINATION
## 15674 15642 0
## CEILINGDETERMINATIONQC CLOUDCAVOK CLOUDCAVOKQC
## 269117 0 182346
## VISIBILITY VISIBILITYQC VISIBILITYTYPE
## 1238 1234 0
## VISIBILITYTYPEQC AIRTEMPERATURE AIRTEMPERATUREQC
## 849 567 544
## DEWPOINTTEMPERATURE DEWPOINTTEMPERATUREQC SEALEVELPRESSURE
## 806 784 77747
## SEALEVELPRESSUREQC OBSERVATIONPERIODPP1 OBSERVATIONPERIODPP1QC
## 76553 247506 266962
## PRECIPAMOUNT1 PRECIPAMOUNT1QC PRECIPCONDITION1
## 248172 265110 259758
## PRECIPCONDITION1QC OBSERVATIONPERIODPP2 OBSERVATIONPERIODPP2QC
## 265062 279016 282882
## PRECIPAMOUNT2 PRECIPAMOUNT2QC PRECIPCONDITION2
## 279020 282502 280924
## PRECIPCONDITION2QC OBSERVATIONPERIODPP3 OBSERVATIONPERIODPP3QC
## 282493 284858 285065
## PRECIPAMOUNT3 PRECIPAMOUNT3QC PRECIPCONDITION3
## 284863 285055 285021
## PRECIPCONDITION3QC OBSERVATIONPERIODPP4 OBSERVATIONPERIODPP4QC
## 285050 0 285230
## PRECIPAMOUNT4 PRECIPAMOUNT4QC PRECIPCONDITION4
## 285230 285230 285230
## PRECIPCONDITION4QC PRECIPHISTDUR PRECIPHISTDURQC
## 0 285229 283235
## PRECIPHISTCHAR PRECIPHISTCHARQC PRECIPDISC
## 285230 285228 236496
## PRECIPDISCQC PRECIPBOGUS PRECIPBOGUSQC
## 285230 271094 285230
## PRECIPAMOUNTSD PRECIPAMOUNTSDQC PRECIPCONDITIONSD
## 284656 285230 285230
## PRECIPCONDITIONSDQC DEPTHWTREQUIV DEPTHWTREQUIVQC
## 285230 285230 285230
## DEPTHWECOND DEPTHWECONDQC HAILSIZE
## 285230 285230 285230
## PRECIPAMOUNTSF1 PRECIPAMOUNTSF1QC PRECIPCONDITIONSF1
## 285230 285230 285230
## PRECIPCONDITIONSF1QC OBSERVATIONPERIODSF1 OBSERVATIONPERIODSF1QC
## 285230 285230 0
## PRECIPAMOUNTSF2 PRECIPAMOUNTSF2QC PRECIPCONDITIONSF2
## 285229 285229 285230
## PRECIPCONDITIONSF2QC OBSERVATIONPERIODSF2 OBSERVATIONPERIODSF2QC
## 285230 0 285228
## PRECIPAMOUNTSF3 PRECIPAMOUNTSF3QC PRECIPCONDITIONSF3
## 285229 285229 285230
## PRECIPCONDITIONSF3QC OBSERVATIONPERIODSF3 OBSERVATIONPERIODSF3QC
## 285230 285230 285230
## PRECIPAMOUNTSF4 PRECIPAMOUNTSF4QC PRECIPCONDITIONSF4
## 285230 285230 285230
## PRECIPCONDITIONSF4QC OBSERVATIONPERIODSF4 OBSERVATIONPERIODSF4QC
## 285230 285230 285230
## PRESENTMANUAL1 PRESENTMANUAL1QC PRESENTMANUAL2
## 216571 0 241672
## PRESENTMANUAL2QC PRESENTMANUAL3 PRESENTMANUAL3QC
## 221405 243529 223327
## PRESENTMANUAL4 PRESENTMANUAL4QC PRESENTMANUAL5
## 0 227140 285230
## PRESENTMANUAL5QC PRESENTMANUAL6 PRESENTMANUAL6QC
## 227141 285230 227141
## PRESENTMANUAL7 PRESENTMANUAL7QC PRESENTAUTOMATED1
## 285230 227141 263139
## PRESENTAUTOMATED1QC PRESENTAUTOMATED2 PRESENTAUTOMATED2QC
## 255648 279767 279767
## PRESENTAUTOMATED3 PRESENTAUTOMATED3QC PASTMANUAL1
## 285212 285212 285230
## PASTMANUAL1QC WXPASTPERIOD1 WXPASTPERIOD1QC
## 283483 285230 285230
## PASTMANUAL2 PASTMANUAL2QC WXPASTPERIOD2
## 285230 284777 285230
## WXPASTPERIOD2QC PASTAUTOMATED1 PASTAUTOMATED1QC
## 285230 285230 283483
## WXPASTAUTOPERIOD1 WXPASTAUTOPERIOD1QC PASTAUTOMATED2
## 285230 285230 285230
## PASTAUTOMATED2QC WXPASTAUTOPERIOD2 WXPASTAUTOPERIOD2QC
## 284777 285230 285230
## RUNWAYENDBEARING RUNWAYDESIGNATOR RUNWAYVISUALRANGE
## 285230 285230 285230
## CLOUDCOVER CLOUDCOVERQC CLOUDCOVERLO
## 49936 46078 284194
## CLOUDCOVERLOQC CLOUDBASEHEIGHT CLOUDBASEHEIGHTQC
## 279696 239857 239857
## CLOUDTYPELO CLOUDTYPELOQC CLOUDTYPEMID
## 284792 280294 284775
## CLOUDTYPEMIDQC CLOUDTYPEHI CLOUDTYPEHIQC
## 280277 285230 280732
## SUNSHINE SURFACECODE SURFACECODEQC
## 285230 285227 285230
## SOILTEMPERATURE SOILTEMPERATUREQC SOILDEPTH
## 285230 285230 285230
## OBSERVATIONPERIODSOILT OBSERVATIONPERIODSOILTQC ALTIMETERSETTING
## 285230 285230 390
## ALTIMETERSETTINGQC STATIONPRESSURE STATIONPRESSUREQC
## 387 285230 271019
## PRESSURETENDENCY PRESSURETENDENCYQC PRESSURE3HOURCHG
## 223796 208978 223164
## PRESSURE3HOURCHGQC PRESSURE24HOURCHG PRESSURE24HOURCHGQC
## 208591 284494 270283
## PRESSURETREND ISOBARICSURFACE ISOBARICSURFACEQC
## 285230 285230 285230
## ISOBARICSURFACEHEIGHT ISOBARICSURFACEHEIGHTQC SEASURFACETEMP
## 285230 285230 285230
## SEASURFACETEMPQC REMARKSYN REMARKMET
## 285227 285230 0
## REMARKAWY HORIZONTALDATUM VERTICALDATUM
## 0 0 0
## LIGHTNINGFREQUENCY RECEIPTDTG INSERTIONTIME
## 285230 182347 0
## BLKSTN
## 106113Removed Columns and Filled-in NULLs
10-year
colSums((is.na(krdm_toKeep10)))## PLATFORMID NETWORKTYPE OBSERVATIONTIME REPORTTYPECODE
## 0 0 0 0
## LATITUDE LONGITUDE WINDDIRECTION WINDSPEED
## 0 0 0 0
## WINDGUSTSPEED CLOUDCEILING VISIBILITY AIRTEMPERATURE
## 0 0 0 0
## DEWPOINTTEMPERATURE PRECIPAMOUNT1 CLOUDCOVER ALTIMETERSETTING
## 0 0 0 0
## date JD
## 0 020-year
colSums((is.na(krdm_toKeep20)))## PLATFORMID NETWORKTYPE OBSERVATIONTIME REPORTTYPECODE
## 0 0 0 0
## LATITUDE LONGITUDE WINDDIRECTION WINDSPEED
## 0 0 0 0
## WINDGUSTSPEED CLOUDCEILING VISIBILITY AIRTEMPERATURE
## 0 0 0 0
## DEWPOINTTEMPERATURE PRECIPAMOUNT1 CLOUDCOVER ALTIMETERSETTING
## 0 0 0 0
## date JD
## 0 030-year
colSums((is.na(krdm_toKeep30)))## PLATFORMID NETWORKTYPE OBSERVATIONTIME REPORTTYPECODE
## 0 0 0 0
## LATITUDE LONGITUDE WINDDIRECTION WINDSPEED
## 0 0 0 0
## WINDGUSTSPEED CLOUDCEILING VISIBILITY AIRTEMPERATURE
## 0 0 0 0
## DEWPOINTTEMPERATURE PRECIPAMOUNT1 CLOUDCOVER ALTIMETERSETTING
## 0 0 0 0
## date JD
## 0 0Buffalo, NY
Initial Nulls:
colSums((is.na(kbuf_data)))## PLATFORMID NETWORKTYPE OBSERVATIONTIME
## 0 0 0
## REPORTTYPECODE LATITUDE LONGITUDE
## 0 1 0
## MONTH SECURITYID DISTRIBUTIONCD
## 2 1 0
## STATIONMODE PLATFORMHEIGHT CALLLETTER
## 254385 1 0
## VERSION WINDDIRECTION WINDDIRECTIONQC
## 0 29998 25421
## WINDCONDITIONS WINDCONDITIONSQC WINDSPEED
## 0 262276 5682
## WINDSPEEDQC STARTDIRECTION ENDDIRECTION
## 5896 413081 413081
## WINDGUSTSPEED WINDGUSTSPEEDQC WINDMEASUREMENTMODE
## 346504 328946 0
## CLOUDCEILING CLOUDCEILINGQC CEILINGDETERMINATION
## 57060 0 0
## CEILINGDETERMINATIONQC CLOUDCAVOK CLOUDCAVOKQC
## 390841 0 0
## VISIBILITY VISIBILITYQC VISIBILITYTYPE
## 5418 5435 0
## VISIBILITYTYPEQC AIRTEMPERATURE AIRTEMPERATUREQC
## 5625 8876 8870
## DEWPOINTTEMPERATURE DEWPOINTTEMPERATUREQC SEALEVELPRESSURE
## 8991 8960 79351
## SEALEVELPRESSUREQC OBSERVATIONPERIODPP1 OBSERVATIONPERIODPP1QC
## 75954 281866 358881
## PRECIPAMOUNT1 PRECIPAMOUNT1QC PRECIPCONDITION1
## 281029 350564 312472
## PRECIPCONDITION1QC OBSERVATIONPERIODPP2 OBSERVATIONPERIODPP2QC
## 351047 391683 405670
## PRECIPAMOUNT2 PRECIPAMOUNT2QC PRECIPCONDITION2
## 391469 404100 400089
## PRECIPCONDITION2QC OBSERVATIONPERIODPP3 OBSERVATIONPERIODPP3QC
## 404102 411593 412608
## PRECIPAMOUNT3 PRECIPAMOUNT3QC PRECIPCONDITION3
## 411558 412336 412202
## PRECIPCONDITION3QC OBSERVATIONPERIODPP4 OBSERVATIONPERIODPP4QC
## 412333 413316 413341
## PRECIPAMOUNT4 PRECIPAMOUNT4QC PRECIPCONDITION4
## 413316 413316 413316
## PRECIPCONDITION4QC PRECIPHISTDUR PRECIPHISTDURQC
## 413316 412381 405549
## PRECIPHISTCHAR PRECIPHISTCHARQC PRECIPDISC
## 0 413352 351951
## PRECIPDISCQC PRECIPBOGUS PRECIPBOGUSQC
## 413352 394813 413352
## PRECIPAMOUNTSD PRECIPAMOUNTSDQC PRECIPCONDITIONSD
## 394974 403229 403470
## PRECIPCONDITIONSDQC DEPTHWTREQUIV DEPTHWTREQUIVQC
## 403470 403147 412692
## DEPTHWECOND DEPTHWECONDQC HAILSIZE
## 413352 413352 413345
## PRECIPAMOUNTSF1 PRECIPAMOUNTSF1QC PRECIPCONDITIONSF1
## 409082 409503 409505
## PRECIPCONDITIONSF1QC OBSERVATIONPERIODSF1 OBSERVATIONPERIODSF1QC
## 409503 413071 413074
## PRECIPAMOUNTSF2 PRECIPAMOUNTSF2QC PRECIPCONDITIONSF2
## 412559 412559 412559
## PRECIPCONDITIONSF2QC OBSERVATIONPERIODSF2 OBSERVATIONPERIODSF2QC
## 412559 413347 413344
## PRECIPAMOUNTSF3 PRECIPAMOUNTSF3QC PRECIPCONDITIONSF3
## 413122 413122 413122
## PRECIPCONDITIONSF3QC OBSERVATIONPERIODSF3 OBSERVATIONPERIODSF3QC
## 413122 413349 413349
## PRECIPAMOUNTSF4 PRECIPAMOUNTSF4QC PRECIPCONDITIONSF4
## 413330 413330 413330
## PRECIPCONDITIONSF4QC OBSERVATIONPERIODSF4 OBSERVATIONPERIODSF4QC
## 413329 413347 413348
## PRESENTMANUAL1 PRESENTMANUAL1QC PRESENTMANUAL2
## 256381 251364 315660
## PRESENTMANUAL2QC PRESENTMANUAL3 PRESENTMANUAL3QC
## 280631 338986 295832
## PRESENTMANUAL4 PRESENTMANUAL4QC PRESENTMANUAL5
## 413093 304877 413346
## PRESENTMANUAL5QC PRESENTMANUAL6 PRESENTMANUAL6QC
## 304929 413350 304930
## PRESENTMANUAL7 PRESENTMANUAL7QC PRESENTAUTOMATED1
## 413352 304930 375711
## PRESENTAUTOMATED1QC PRESENTAUTOMATED2 PRESENTAUTOMATED2QC
## 363033 398406 398391
## PRESENTAUTOMATED3 PRESENTAUTOMATED3QC PASTMANUAL1
## 412971 412971 412119
## PASTMANUAL1QC WXPASTPERIOD1 WXPASTPERIOD1QC
## 406196 412120 412120
## PASTMANUAL2 PASTMANUAL2QC WXPASTPERIOD2
## 412119 409898 412120
## WXPASTPERIOD2QC PASTAUTOMATED1 PASTAUTOMATED1QC
## 412120 413352 407429
## WXPASTAUTOPERIOD1 WXPASTAUTOPERIOD1QC PASTAUTOMATED2
## 413352 413352 413352
## PASTAUTOMATED2QC WXPASTAUTOPERIOD2 WXPASTAUTOPERIOD2QC
## 411131 413352 413352
## RUNWAYENDBEARING RUNWAYDESIGNATOR RUNWAYVISUALRANGE
## 401854 0 401855
## CLOUDCOVER CLOUDCOVERQC CLOUDCOVERLO
## 100387 88673 406627
## CLOUDCOVERLOQC CLOUDBASEHEIGHT CLOUDBASEHEIGHTQC
## 391002 250918 248868
## CLOUDTYPELO CLOUDTYPELOQC CLOUDTYPEMID
## 378424 362799 385880
## CLOUDTYPEMIDQC CLOUDTYPEHI CLOUDTYPEHIQC
## 370255 390349 374724
## SUNSHINE SURFACECODE SURFACECODEQC
## 413352 413349 413352
## SOILTEMPERATURE SOILTEMPERATUREQC SOILDEPTH
## 413352 413352 413351
## OBSERVATIONPERIODSOILT OBSERVATIONPERIODSOILTQC ALTIMETERSETTING
## 413351 413351 68695
## ALTIMETERSETTINGQC STATIONPRESSURE STATIONPRESSUREQC
## 64328 275021 258403
## PRESSURETENDENCY PRESSURETENDENCYQC PRESSURE3HOURCHG
## 286367 269469 281170
## PRESSURE3HOURCHGQC PRESSURE24HOURCHG PRESSURE24HOURCHGQC
## 265382 412488 391547
## PRESSURETREND ISOBARICSURFACE ISOBARICSURFACEQC
## 413352 413349 413352
## ISOBARICSURFACEHEIGHT ISOBARICSURFACEHEIGHTQC SEASURFACETEMP
## 413350 413352 413352
## SEASURFACETEMPQC REMARKSYN REMARKMET
## 413349 0 0
## REMARKAWY HORIZONTALDATUM VERTICALDATUM
## 0 0 0
## LIGHTNINGFREQUENCY RECEIPTDTG INSERTIONTIME
## 413351 262060 0
## BLKSTN
## 158969Removed Columns and Filled-in NULLs
10-year
colSums((is.na(kbuf_toKeep10)))## PLATFORMID NETWORKTYPE OBSERVATIONTIME REPORTTYPECODE
## 0 0 0 0
## LATITUDE LONGITUDE WINDDIRECTION WINDSPEED
## 0 0 0 0
## WINDGUSTSPEED CLOUDCEILING VISIBILITY AIRTEMPERATURE
## 0 0 0 0
## DEWPOINTTEMPERATURE PRECIPAMOUNT1 CLOUDCOVER ALTIMETERSETTING
## 0 0 0 0
## date JD
## 0 020-year
colSums((is.na(kbuf_toKeep20)))## PLATFORMID NETWORKTYPE OBSERVATIONTIME REPORTTYPECODE
## 0 0 0 0
## LATITUDE LONGITUDE WINDDIRECTION WINDSPEED
## 0 0 0 0
## WINDGUSTSPEED CLOUDCEILING VISIBILITY AIRTEMPERATURE
## 0 0 0 0
## DEWPOINTTEMPERATURE PRECIPAMOUNT1 CLOUDCOVER ALTIMETERSETTING
## 0 0 0 0
## date JD
## 0 030-year
colSums((is.na(kbuf_toKeep30)))## PLATFORMID NETWORKTYPE OBSERVATIONTIME REPORTTYPECODE
## 0 0 0 0
## LATITUDE LONGITUDE WINDDIRECTION WINDSPEED
## 0 0 0 0
## WINDGUSTSPEED CLOUDCEILING VISIBILITY AIRTEMPERATURE
## 0 0 0 0
## DEWPOINTTEMPERATURE PRECIPAMOUNT1 CLOUDCOVER ALTIMETERSETTING
## 0 0 0 0
## date JD
## 0 0Topeka, KS
Initial Nulls:
colSums((is.na(kfoe_data)))## PLATFORMID NETWORKTYPE OBSERVATIONTIME
## 0 0 0
## REPORTTYPECODE LATITUDE LONGITUDE
## 0 3 3
## MONTH SECURITYID DISTRIBUTIONCD
## 3 3 0
## STATIONMODE PLATFORMHEIGHT CALLLETTER
## 177319 3 0
## VERSION WINDDIRECTION WINDDIRECTIONQC
## 0 33542 29513
## WINDCONDITIONS WINDCONDITIONSQC WINDSPEED
## 0 179284 2532
## WINDSPEEDQC STARTDIRECTION ENDDIRECTION
## 2709 293760 293772
## WINDGUSTSPEED WINDGUSTSPEEDQC WINDMEASUREMENTMODE
## 246957 234105 181704
## CLOUDCEILING CLOUDCEILINGQC CEILINGDETERMINATION
## 3838 3789 0
## CEILINGDETERMINATIONQC CLOUDCAVOK CLOUDCAVOKQC
## 276797 0 180069
## VISIBILITY VISIBILITYQC VISIBILITYTYPE
## 1438 1437 0
## VISIBILITYTYPEQC AIRTEMPERATURE AIRTEMPERATUREQC
## 879 1466 1443
## DEWPOINTTEMPERATURE DEWPOINTTEMPERATUREQC SEALEVELPRESSURE
## 1748 1713 74091
## SEALEVELPRESSUREQC OBSERVATIONPERIODPP1 OBSERVATIONPERIODPP1QC
## 71413 249614 272994
## PRECIPAMOUNT1 PRECIPAMOUNT1QC PRECIPCONDITION1
## 250144 270610 264772
## PRECIPCONDITION1QC OBSERVATIONPERIODPP2 OBSERVATIONPERIODPP2QC
## 270713 287624 291745
## PRECIPAMOUNT2 PRECIPAMOUNT2QC PRECIPCONDITION2
## 287657 291395 290433
## PRECIPCONDITION2QC OBSERVATIONPERIODPP3 OBSERVATIONPERIODPP3QC
## 291370 293523 293897
## PRECIPAMOUNT3 PRECIPAMOUNT3QC PRECIPCONDITION3
## 293542 293865 293817
## PRECIPCONDITION3QC OBSERVATIONPERIODPP4 OBSERVATIONPERIODPP4QC
## 293848 294162 294162
## PRECIPAMOUNT4 PRECIPAMOUNT4QC PRECIPCONDITION4
## 294162 294162 294162
## PRECIPCONDITION4QC PRECIPHISTDUR PRECIPHISTDURQC
## 294162 294162 291575
## PRECIPHISTCHAR PRECIPHISTCHARQC PRECIPDISC
## 294162 294162 259349
## PRECIPDISCQC PRECIPBOGUS PRECIPBOGUSQC
## 294162 284681 294162
## PRECIPAMOUNTSD PRECIPAMOUNTSDQC PRECIPCONDITIONSD
## 293645 294162 294162
## PRECIPCONDITIONSDQC DEPTHWTREQUIV DEPTHWTREQUIVQC
## 294162 294162 294162
## DEPTHWECOND DEPTHWECONDQC HAILSIZE
## 294162 294162 294161
## PRECIPAMOUNTSF1 PRECIPAMOUNTSF1QC PRECIPCONDITIONSF1
## 294162 294162 294162
## PRECIPCONDITIONSF1QC OBSERVATIONPERIODSF1 OBSERVATIONPERIODSF1QC
## 294162 294162 294162
## PRECIPAMOUNTSF2 PRECIPAMOUNTSF2QC PRECIPCONDITIONSF2
## 294162 294162 294162
## PRECIPCONDITIONSF2QC OBSERVATIONPERIODSF2 OBSERVATIONPERIODSF2QC
## 294162 294162 294162
## PRECIPAMOUNTSF3 PRECIPAMOUNTSF3QC PRECIPCONDITIONSF3
## 294162 294162 294162
## PRECIPCONDITIONSF3QC OBSERVATIONPERIODSF3 OBSERVATIONPERIODSF3QC
## 294162 294162 294162
## PRECIPAMOUNTSF4 PRECIPAMOUNTSF4QC PRECIPCONDITIONSF4
## 294162 294162 294162
## PRECIPCONDITIONSF4QC OBSERVATIONPERIODSF4 OBSERVATIONPERIODSF4QC
## 294162 294162 294162
## PRESENTMANUAL1 PRESENTMANUAL1QC PRESENTMANUAL2
## 217716 214749 241033
## PRESENTMANUAL2QC PRESENTMANUAL3 PRESENTMANUAL3QC
## 223903 246001 228002
## PRESENTMANUAL4 PRESENTMANUAL4QC PRESENTMANUAL5
## 294162 232912 294162
## PRESENTMANUAL5QC PRESENTMANUAL6 PRESENTMANUAL6QC
## 232912 294162 232912
## PRESENTMANUAL7 PRESENTMANUAL7QC PRESENTAUTOMATED1
## 294162 232912 261552
## PRESENTAUTOMATED1QC PRESENTAUTOMATED2 PRESENTAUTOMATED2QC
## 253583 284284 284282
## PRESENTAUTOMATED3 PRESENTAUTOMATED3QC PASTMANUAL1
## 293601 293601 294162
## PASTMANUAL1QC WXPASTPERIOD1 WXPASTPERIOD1QC
## 291195 294162 294162
## PASTMANUAL2 PASTMANUAL2QC WXPASTPERIOD2
## 294162 293287 294162
## WXPASTPERIOD2QC PASTAUTOMATED1 PASTAUTOMATED1QC
## 294162 294162 291195
## WXPASTAUTOPERIOD1 WXPASTAUTOPERIOD1QC PASTAUTOMATED2
## 294162 294162 294162
## PASTAUTOMATED2QC WXPASTAUTOPERIOD2 WXPASTAUTOPERIOD2QC
## 293287 294162 294162
## RUNWAYENDBEARING RUNWAYDESIGNATOR RUNWAYVISUALRANGE
## 292644 0 292642
## CLOUDCOVER CLOUDCOVERQC CLOUDCOVERLO
## 49495 43855 294161
## CLOUDCOVERLOQC CLOUDBASEHEIGHT CLOUDBASEHEIGHTQC
## 288431 239481 239481
## CLOUDTYPELO CLOUDTYPELOQC CLOUDTYPEMID
## 294161 288431 294161
## CLOUDTYPEMIDQC CLOUDTYPEHI CLOUDTYPEHIQC
## 288431 294161 288431
## SUNSHINE SURFACECODE SURFACECODEQC
## 294162 294160 294162
## SOILTEMPERATURE SOILTEMPERATUREQC SOILDEPTH
## 294162 294162 294162
## OBSERVATIONPERIODSOILT OBSERVATIONPERIODSOILTQC ALTIMETERSETTING
## 294162 294162 1020
## ALTIMETERSETTINGQC STATIONPRESSURE STATIONPRESSUREQC
## 999 294162 278370
## PRESSURETENDENCY PRESSURETENDENCYQC PRESSURE3HOURCHG
## 240783 225042 239619
## PRESSURE3HOURCHGQC PRESSURE24HOURCHG PRESSURE24HOURCHGQC
## 224260 293348 277556
## PRESSURETREND ISOBARICSURFACE ISOBARICSURFACEQC
## 294162 294162 294162
## ISOBARICSURFACEHEIGHT ISOBARICSURFACEHEIGHTQC SEASURFACETEMP
## 294162 294162 294162
## SEASURFACETEMPQC REMARKSYN REMARKMET
## 294160 294162 0
## REMARKAWY HORIZONTALDATUM VERTICALDATUM
## 0 0 0
## LIGHTNINGFREQUENCY RECEIPTDTG INSERTIONTIME
## 294162 180069 0
## BLKSTN
## 116849Removed Columns and Filled-in NULLs
10-year
colSums((is.na(kfoe_toKeep10)))## PLATFORMID NETWORKTYPE OBSERVATIONTIME REPORTTYPECODE
## 0 0 0 0
## LATITUDE LONGITUDE WINDDIRECTION WINDSPEED
## 0 0 0 0
## WINDGUSTSPEED CLOUDCEILING VISIBILITY AIRTEMPERATURE
## 0 0 0 0
## DEWPOINTTEMPERATURE PRECIPAMOUNT1 CLOUDCOVER ALTIMETERSETTING
## 0 0 0 0
## date JD
## 0 020-year
colSums((is.na(kfoe_toKeep20)))## PLATFORMID NETWORKTYPE OBSERVATIONTIME REPORTTYPECODE
## 0 0 0 0
## LATITUDE LONGITUDE WINDDIRECTION WINDSPEED
## 0 0 0 0
## WINDGUSTSPEED CLOUDCEILING VISIBILITY AIRTEMPERATURE
## 0 0 0 0
## DEWPOINTTEMPERATURE PRECIPAMOUNT1 CLOUDCOVER ALTIMETERSETTING
## 0 0 0 0
## date JD
## 0 030-year
colSums((is.na(kfoe_toKeep30)))## PLATFORMID NETWORKTYPE OBSERVATIONTIME REPORTTYPECODE
## 0 0 0 0
## LATITUDE LONGITUDE WINDDIRECTION WINDSPEED
## 0 0 0 0
## WINDGUSTSPEED CLOUDCEILING VISIBILITY AIRTEMPERATURE
## 0 0 0 0
## DEWPOINTTEMPERATURE PRECIPAMOUNT1 CLOUDCOVER ALTIMETERSETTING
## 0 0 0 0
## date JD
## 0 0Madison, WI
Initial Nulls:
colSums((is.na(kmsn_data)))## PLATFORMID NETWORKTYPE OBSERVATIONTIME
## 0 0 0
## REPORTTYPECODE LATITUDE LONGITUDE
## 0 2 2
## MONTH SECURITYID DISTRIBUTIONCD
## 2 0 0
## STATIONMODE PLATFORMHEIGHT CALLLETTER
## 224898 2 0
## VERSION WINDDIRECTION WINDDIRECTIONQC
## 0 77023 64798
## WINDCONDITIONS WINDCONDITIONSQC WINDSPEED
## 0 226659 1244
## WINDSPEEDQC STARTDIRECTION ENDDIRECTION
## 2009 359791 359791
## WINDGUSTSPEED WINDGUSTSPEEDQC WINDMEASUREMENTMODE
## 316685 300048 228490
## CLOUDCEILING CLOUDCEILINGQC CEILINGDETERMINATION
## 25645 23392 0
## CEILINGDETERMINATIONQC CLOUDCAVOK CLOUDCAVOKQC
## 341854 0 227891
## VISIBILITY VISIBILITYQC VISIBILITYTYPE
## 177 176 0
## VISIBILITYTYPEQC AIRTEMPERATURE AIRTEMPERATUREQC
## 547 10904 10903
## DEWPOINTTEMPERATURE DEWPOINTTEMPERATUREQC SEALEVELPRESSURE
## 10938 10937 61445
## SEALEVELPRESSUREQC OBSERVATIONPERIODPP1 OBSERVATIONPERIODPP1QC
## 57767 277798 326553
## PRECIPAMOUNT1 PRECIPAMOUNT1QC PRECIPCONDITION1
## 277111 321460 300206
## PRECIPCONDITION1QC OBSERVATIONPERIODPP2 OBSERVATIONPERIODPP2QC
## 321794 348101 357610
## PRECIPAMOUNT2 PRECIPAMOUNT2QC PRECIPCONDITION2
## 348172 356786 354513
## PRECIPCONDITION2QC OBSERVATIONPERIODPP3 OBSERVATIONPERIODPP3QC
## 356789 361525 362158
## PRECIPAMOUNT3 PRECIPAMOUNT3QC PRECIPCONDITION3
## 361524 361996 361955
## PRECIPCONDITION3QC OBSERVATIONPERIODPP4 OBSERVATIONPERIODPP4QC
## 361996 362578 362601
## PRECIPAMOUNT4 PRECIPAMOUNT4QC PRECIPCONDITION4
## 362578 362578 362578
## PRECIPCONDITION4QC PRECIPHISTDUR PRECIPHISTDURQC
## 362578 361687 357402
## PRECIPHISTCHAR PRECIPHISTCHARQC PRECIPDISC
## 0 362604 305512
## PRECIPDISCQC PRECIPBOGUS PRECIPBOGUSQC
## 362604 345610 362604
## PRECIPAMOUNTSD PRECIPAMOUNTSDQC PRECIPCONDITIONSD
## 349150 358019 358098
## PRECIPCONDITIONSDQC DEPTHWTREQUIV DEPTHWTREQUIVQC
## 358097 357941 361967
## DEPTHWECOND DEPTHWECONDQC HAILSIZE
## 362604 362604 362595
## PRECIPAMOUNTSF1 PRECIPAMOUNTSF1QC PRECIPCONDITIONSF1
## 362486 362601 362604
## PRECIPCONDITIONSF1QC OBSERVATIONPERIODSF1 OBSERVATIONPERIODSF1QC
## 362601 362501 362502
## PRECIPAMOUNTSF2 PRECIPAMOUNTSF2QC PRECIPCONDITIONSF2
## 362604 362604 362604
## PRECIPCONDITIONSF2QC OBSERVATIONPERIODSF2 OBSERVATIONPERIODSF2QC
## 362604 362604 362604
## PRECIPAMOUNTSF3 PRECIPAMOUNTSF3QC PRECIPCONDITIONSF3
## 362604 362604 362604
## PRECIPCONDITIONSF3QC OBSERVATIONPERIODSF3 OBSERVATIONPERIODSF3QC
## 362604 362604 362604
## PRECIPAMOUNTSF4 PRECIPAMOUNTSF4QC PRECIPCONDITIONSF4
## 362604 362604 362604
## PRECIPCONDITIONSF4QC OBSERVATIONPERIODSF4 OBSERVATIONPERIODSF4QC
## 362604 362604 362604
## PRESENTMANUAL1 PRESENTMANUAL1QC PRESENTMANUAL2
## 226282 221765 276262
## PRESENTMANUAL2QC PRESENTMANUAL3 PRESENTMANUAL3QC
## 246295 293078 257503
## PRESENTMANUAL4 PRESENTMANUAL4QC PRESENTMANUAL5
## 362309 265345 362601
## PRESENTMANUAL5QC PRESENTMANUAL6 PRESENTMANUAL6QC
## 265410 362601 265410
## PRESENTMANUAL7 PRESENTMANUAL7QC PRESENTAUTOMATED1
## 362601 265410 329873
## PRESENTAUTOMATED1QC PRESENTAUTOMATED2 PRESENTAUTOMATED2QC
## 319410 350408 350408
## PRESENTAUTOMATED3 PRESENTAUTOMATED3QC PASTMANUAL1
## 362093 362093 361279
## PASTMANUAL1QC WXPASTPERIOD1 WXPASTPERIOD1QC
## 356489 361279 361279
## PASTMANUAL2 PASTMANUAL2QC WXPASTPERIOD2
## 361279 359879 361279
## WXPASTPERIOD2QC PASTAUTOMATED1 PASTAUTOMATED1QC
## 361279 362604 357814
## WXPASTAUTOPERIOD1 WXPASTAUTOPERIOD1QC PASTAUTOMATED2
## 362604 362604 362604
## PASTAUTOMATED2QC WXPASTAUTOPERIOD2 WXPASTAUTOPERIOD2QC
## 361204 362604 362604
## RUNWAYENDBEARING RUNWAYDESIGNATOR RUNWAYVISUALRANGE
## 359624 0 359623
## CLOUDCOVER CLOUDCOVERQC CLOUDCOVERLO
## 82642 72917 350468
## CLOUDCOVERLOQC CLOUDBASEHEIGHT CLOUDBASEHEIGHTQC
## 338753 244081 243279
## CLOUDTYPELO CLOUDTYPELOQC CLOUDTYPEMID
## 346933 335218 348805
## CLOUDTYPEMIDQC CLOUDTYPEHI CLOUDTYPEHIQC
## 337090 349432 337717
## SUNSHINE SURFACECODE SURFACECODEQC
## 362604 362602 362604
## SOILTEMPERATURE SOILTEMPERATUREQC SOILDEPTH
## 362604 362604 362604
## OBSERVATIONPERIODSOILT OBSERVATIONPERIODSOILTQC ALTIMETERSETTING
## 362604 362604 34300
## ALTIMETERSETTINGQC STATIONPRESSURE STATIONPRESSUREQC
## 32093 267618 250736
## PRESSURETENDENCY PRESSURETENDENCYQC PRESSURE3HOURCHG
## 262688 245553 260149
## PRESSURE3HOURCHGQC PRESSURE24HOURCHG PRESSURE24HOURCHGQC
## 243352 361794 342773
## PRESSURETREND ISOBARICSURFACE ISOBARICSURFACEQC
## 362604 362604 362604
## ISOBARICSURFACEHEIGHT ISOBARICSURFACEHEIGHTQC SEASURFACETEMP
## 362604 362604 362604
## SEASURFACETEMPQC REMARKSYN REMARKMET
## 362602 0 0
## REMARKAWY HORIZONTALDATUM VERTICALDATUM
## 0 0 0
## LIGHTNINGFREQUENCY RECEIPTDTG INSERTIONTIME
## 362604 227886 0
## BLKSTN
## 137710Removed Columns and Filled-in NULLs
10-year
colSums((is.na(kmsn_toKeep10)))## PLATFORMID NETWORKTYPE OBSERVATIONTIME REPORTTYPECODE
## 0 0 0 0
## LATITUDE LONGITUDE WINDDIRECTION WINDSPEED
## 0 0 0 0
## WINDGUSTSPEED CLOUDCEILING VISIBILITY AIRTEMPERATURE
## 0 0 0 0
## DEWPOINTTEMPERATURE PRECIPAMOUNT1 CLOUDCOVER ALTIMETERSETTING
## 0 0 0 0
## date JD
## 0 020-year
colSums((is.na(kmsn_toKeep20)))## PLATFORMID NETWORKTYPE OBSERVATIONTIME REPORTTYPECODE
## 0 0 0 0
## LATITUDE LONGITUDE WINDDIRECTION WINDSPEED
## 0 0 0 0
## WINDGUSTSPEED CLOUDCEILING VISIBILITY AIRTEMPERATURE
## 0 0 0 0
## DEWPOINTTEMPERATURE PRECIPAMOUNT1 CLOUDCOVER ALTIMETERSETTING
## 0 0 0 0
## date JD
## 0 030-year
colSums((is.na(kmsn_toKeep30)))## PLATFORMID NETWORKTYPE OBSERVATIONTIME REPORTTYPECODE
## 0 0 0 0
## LATITUDE LONGITUDE WINDDIRECTION WINDSPEED
## 0 0 0 0
## WINDGUSTSPEED CLOUDCEILING VISIBILITY AIRTEMPERATURE
## 0 0 0 0
## DEWPOINTTEMPERATURE PRECIPAMOUNT1 CLOUDCOVER ALTIMETERSETTING
## 0 0 0 0
## date JD
## 0 0Tri-cities Airport, TN
Initial Nulls:
colSums((is.na(ktri_data)))## PLATFORMID NETWORKTYPE OBSERVATIONTIME
## 0 0 0
## REPORTTYPECODE LATITUDE LONGITUDE
## 0 0 0
## MONTH SECURITYID DISTRIBUTIONCD
## 0 0 0
## STATIONMODE PLATFORMHEIGHT CALLLETTER
## 203807 0 0
## VERSION WINDDIRECTION WINDDIRECTIONQC
## 0 157154 127614
## WINDCONDITIONS WINDCONDITIONSQC WINDSPEED
## 0 202804 248
## WINDSPEEDQC STARTDIRECTION ENDDIRECTION
## 1083 317679 317679
## WINDGUSTSPEED WINDGUSTSPEEDQC WINDMEASUREMENTMODE
## 301825 286413 206930
## CLOUDCEILING CLOUDCEILINGQC CEILINGDETERMINATION
## 5621 5514 0
## CEILINGDETERMINATIONQC CLOUDCAVOK CLOUDCAVOKQC
## 300166 0 206795
## VISIBILITY VISIBILITYQC VISIBILITYTYPE
## 307 337 0
## VISIBILITYTYPEQC AIRTEMPERATURE AIRTEMPERATUREQC
## 172 1862 1857
## DEWPOINTTEMPERATURE DEWPOINTTEMPERATUREQC SEALEVELPRESSURE
## 1994 1975 59034
## SEALEVELPRESSUREQC OBSERVATIONPERIODPP1 OBSERVATIONPERIODPP1QC
## 56003 258517 292668
## PRECIPAMOUNT1 PRECIPAMOUNT1QC PRECIPCONDITION1
## 260034 289320 280603
## PRECIPCONDITION1QC OBSERVATIONPERIODPP2 OBSERVATIONPERIODPP2QC
## 289582 307763 314260
## PRECIPAMOUNT2 PRECIPAMOUNT2QC PRECIPCONDITION2
## 307792 313623 311913
## PRECIPCONDITION2QC OBSERVATIONPERIODPP3 OBSERVATIONPERIODPP3QC
## 313622 317212 317750
## PRECIPAMOUNT3 PRECIPAMOUNT3QC PRECIPCONDITION3
## 317214 317680 317612
## PRECIPCONDITION3QC OBSERVATIONPERIODPP4 OBSERVATIONPERIODPP4QC
## 317679 318149 318151
## PRECIPAMOUNT4 PRECIPAMOUNT4QC PRECIPCONDITION4
## 318149 318149 318149
## PRECIPCONDITION4QC PRECIPHISTDUR PRECIPHISTDURQC
## 318149 318155 314727
## PRECIPHISTCHAR PRECIPHISTCHARQC PRECIPDISC
## 318155 318155 266624
## PRECIPDISCQC PRECIPBOGUS PRECIPBOGUSQC
## 318155 300890 318155
## PRECIPAMOUNTSD PRECIPAMOUNTSDQC PRECIPCONDITIONSD
## 316984 317900 317931
## PRECIPCONDITIONSDQC DEPTHWTREQUIV DEPTHWTREQUIVQC
## 317931 317897 318131
## DEPTHWECOND DEPTHWECONDQC HAILSIZE
## 318155 318155 318147
## PRECIPAMOUNTSF1 PRECIPAMOUNTSF1QC PRECIPCONDITIONSF1
## 318154 318154 318154
## PRECIPCONDITIONSF1QC OBSERVATIONPERIODSF1 OBSERVATIONPERIODSF1QC
## 318154 318123 318123
## PRECIPAMOUNTSF2 PRECIPAMOUNTSF2QC PRECIPCONDITIONSF2
## 318155 318155 318155
## PRECIPCONDITIONSF2QC OBSERVATIONPERIODSF2 OBSERVATIONPERIODSF2QC
## 318155 318154 318154
## PRECIPAMOUNTSF3 PRECIPAMOUNTSF3QC PRECIPCONDITIONSF3
## 318155 318155 318155
## PRECIPCONDITIONSF3QC OBSERVATIONPERIODSF3 OBSERVATIONPERIODSF3QC
## 318155 318155 318155
## PRECIPAMOUNTSF4 PRECIPAMOUNTSF4QC PRECIPCONDITIONSF4
## 318155 318155 318155
## PRECIPCONDITIONSF4QC OBSERVATIONPERIODSF4 OBSERVATIONPERIODSF4QC
## 318155 318155 318155
## PRESENTMANUAL1 PRESENTMANUAL1QC PRESENTMANUAL2
## 188449 184070 237805
## PRESENTMANUAL2QC PRESENTMANUAL3 PRESENTMANUAL3QC
## 203534 248482 210435
## PRESENTMANUAL4 PRESENTMANUAL4QC PRESENTMANUAL5
## 318150 217076 318155
## PRESENTMANUAL5QC PRESENTMANUAL6 PRESENTMANUAL6QC
## 217076 318155 217076
## PRESENTMANUAL7 PRESENTMANUAL7QC PRESENTAUTOMATED1
## 318155 217076 291129
## PRESENTAUTOMATED1QC PRESENTAUTOMATED2 PRESENTAUTOMATED2QC
## 282133 310178 310178
## PRESENTAUTOMATED3 PRESENTAUTOMATED3QC PASTMANUAL1
## 318125 318125 318155
## PASTMANUAL1QC WXPASTPERIOD1 WXPASTPERIOD1QC
## 313772 318155 318155
## PASTMANUAL2 PASTMANUAL2QC WXPASTPERIOD2
## 318155 317147 318155
## WXPASTPERIOD2QC PASTAUTOMATED1 PASTAUTOMATED1QC
## 318155 318155 313772
## WXPASTAUTOPERIOD1 WXPASTAUTOPERIOD1QC PASTAUTOMATED2
## 318155 318155 318155
## PASTAUTOMATED2QC WXPASTAUTOPERIOD2 WXPASTAUTOPERIOD2QC
## 317147 318155 318155
## RUNWAYENDBEARING RUNWAYDESIGNATOR RUNWAYVISUALRANGE
## 317353 0 317352
## CLOUDCOVER CLOUDCOVERQC CLOUDCOVERLO
## 73468 63942 318154
## CLOUDCOVERLOQC CLOUDBASEHEIGHT CLOUDBASEHEIGHTQC
## 307403 222245 222245
## CLOUDTYPELO CLOUDTYPELOQC CLOUDTYPEMID
## 314260 303509 314935
## CLOUDTYPEMIDQC CLOUDTYPEHI CLOUDTYPEHIQC
## 304184 315539 304788
## SUNSHINE SURFACECODE SURFACECODEQC
## 318155 318153 318155
## SOILTEMPERATURE SOILTEMPERATUREQC SOILDEPTH
## 318155 318155 318155
## OBSERVATIONPERIODSOILT OBSERVATIONPERIODSOILTQC ALTIMETERSETTING
## 318155 318155 98
## ALTIMETERSETTINGQC STATIONPRESSURE STATIONPRESSUREQC
## 93 318155 301938
## PRESSURETENDENCY PRESSURETENDENCYQC PRESSURE3HOURCHG
## 252279 236117 249617
## PRESSURE3HOURCHGQC PRESSURE24HOURCHG PRESSURE24HOURCHGQC
## 233876 317316 301099
## PRESSURETREND ISOBARICSURFACE ISOBARICSURFACEQC
## 318155 318155 318155
## ISOBARICSURFACEHEIGHT ISOBARICSURFACEHEIGHTQC SEASURFACETEMP
## 318155 318155 318155
## SEASURFACETEMPQC REMARKSYN REMARKMET
## 318153 318155 0
## REMARKAWY HORIZONTALDATUM VERTICALDATUM
## 0 0 0
## LIGHTNINGFREQUENCY RECEIPTDTG INSERTIONTIME
## 318155 206795 0
## BLKSTN
## 114348Removed Columns and Filled-in NULLs
10-year
colSums((is.na(ktri_toKeep10)))## PLATFORMID NETWORKTYPE OBSERVATIONTIME REPORTTYPECODE
## 0 0 0 0
## LATITUDE LONGITUDE WINDDIRECTION WINDSPEED
## 0 0 0 0
## WINDGUSTSPEED CLOUDCEILING VISIBILITY AIRTEMPERATURE
## 0 0 0 0
## DEWPOINTTEMPERATURE PRECIPAMOUNT1 CLOUDCOVER ALTIMETERSETTING
## 0 0 0 0
## date JD
## 0 020-year
colSums((is.na(ktri_toKeep20)))## PLATFORMID NETWORKTYPE OBSERVATIONTIME REPORTTYPECODE
## 0 0 0 0
## LATITUDE LONGITUDE WINDDIRECTION WINDSPEED
## 0 0 0 0
## WINDGUSTSPEED CLOUDCEILING VISIBILITY AIRTEMPERATURE
## 0 0 0 0
## DEWPOINTTEMPERATURE PRECIPAMOUNT1 CLOUDCOVER ALTIMETERSETTING
## 0 0 0 0
## date JD
## 0 030-year
colSums((is.na(ktri_toKeep30)))## PLATFORMID NETWORKTYPE OBSERVATIONTIME REPORTTYPECODE
## 0 0 0 0
## LATITUDE LONGITUDE WINDDIRECTION WINDSPEED
## 0 0 0 0
## WINDGUSTSPEED CLOUDCEILING VISIBILITY AIRTEMPERATURE
## 0 0 0 0
## DEWPOINTTEMPERATURE PRECIPAMOUNT1 CLOUDCOVER ALTIMETERSETTING
## 0 0 0 0
## date JD
## 0 0Juneau, AK
Initial Nulls:
colSums((is.na(pajn_data)))## PLATFORMID NETWORKTYPE OBSERVATIONTIME
## 0 0 0
## REPORTTYPECODE LATITUDE LONGITUDE
## 0 3 3
## MONTH SECURITYID DISTRIBUTIONCD
## 3 3 0
## STATIONMODE PLATFORMHEIGHT CALLLETTER
## 212050 3 0
## VERSION WINDDIRECTION WINDDIRECTIONQC
## 3 102930 87370
## WINDCONDITIONS WINDCONDITIONSQC WINDSPEED
## 0 210054 524
## WINDSPEEDQC STARTDIRECTION ENDDIRECTION
## 851 338625 338625
## WINDGUSTSPEED WINDGUSTSPEEDQC WINDMEASUREMENTMODE
## 310591 293547 212127
## CLOUDCEILING CLOUDCEILINGQC CEILINGDETERMINATION
## 29769 27522 0
## CEILINGDETERMINATIONQC CLOUDCAVOK CLOUDCAVOKQC
## 318998 0 212055
## VISIBILITY VISIBILITYQC VISIBILITYTYPE
## 497 497 0
## VISIBILITYTYPEQC AIRTEMPERATURE AIRTEMPERATUREQC
## 445 2324 2322
## DEWPOINTTEMPERATURE DEWPOINTTEMPERATUREQC SEALEVELPRESSURE
## 2456 2452 79571
## SEALEVELPRESSUREQC OBSERVATIONPERIODPP1 OBSERVATIONPERIODPP1QC
## 76870 200862 274168
## PRECIPAMOUNT1 PRECIPAMOUNT1QC PRECIPCONDITION1
## 200426 264933 241816
## PRECIPCONDITION1QC OBSERVATIONPERIODPP2 OBSERVATIONPERIODPP2QC
## 266081 306276 325060
## PRECIPAMOUNT2 PRECIPAMOUNT2QC PRECIPCONDITION2
## 306346 322935 319397
## PRECIPCONDITION2QC OBSERVATIONPERIODPP3 OBSERVATIONPERIODPP3QC
## 322931 335749 337508
## PRECIPAMOUNT3 PRECIPAMOUNT3QC PRECIPCONDITION3
## 335760 337176 337026
## PRECIPCONDITION3QC OBSERVATIONPERIODPP4 OBSERVATIONPERIODPP4QC
## 337168 338907 338938
## PRECIPAMOUNT4 PRECIPAMOUNT4QC PRECIPCONDITION4
## 338907 338907 338907
## PRECIPCONDITION4QC PRECIPHISTDUR PRECIPHISTDURQC
## 338907 338952 329825
## PRECIPHISTCHAR PRECIPHISTCHARQC PRECIPDISC
## 338952 338952 288021
## PRECIPDISCQC PRECIPBOGUS PRECIPBOGUSQC
## 338952 324454 338952
## PRECIPAMOUNTSD PRECIPAMOUNTSDQC PRECIPCONDITIONSD
## 325733 334179 333954
## PRECIPCONDITIONSDQC DEPTHWTREQUIV DEPTHWTREQUIVQC
## 334413 334217 338441
## DEPTHWECOND DEPTHWECONDQC HAILSIZE
## 338952 338952 338952
## PRECIPAMOUNTSF1 PRECIPAMOUNTSF1QC PRECIPCONDITIONSF1
## 338952 338952 338952
## PRECIPCONDITIONSF1QC OBSERVATIONPERIODSF1 OBSERVATIONPERIODSF1QC
## 338952 338701 338701
## PRECIPAMOUNTSF2 PRECIPAMOUNTSF2QC PRECIPCONDITIONSF2
## 338952 338952 338952
## PRECIPCONDITIONSF2QC OBSERVATIONPERIODSF2 OBSERVATIONPERIODSF2QC
## 338952 338952 338952
## PRECIPAMOUNTSF3 PRECIPAMOUNTSF3QC PRECIPCONDITIONSF3
## 338952 338952 338952
## PRECIPCONDITIONSF3QC OBSERVATIONPERIODSF3 OBSERVATIONPERIODSF3QC
## 338952 338952 338952
## PRECIPAMOUNTSF4 PRECIPAMOUNTSF4QC PRECIPCONDITIONSF4
## 338952 338952 338952
## PRECIPCONDITIONSF4QC OBSERVATIONPERIODSF4 OBSERVATIONPERIODSF4QC
## 338952 338952 338952
## PRESENTMANUAL1 PRESENTMANUAL1QC PRESENTMANUAL2
## 189418 182264 253836
## PRESENTMANUAL2QC PRESENTMANUAL3 PRESENTMANUAL3QC
## 218954 271903 233075
## PRESENTMANUAL4 PRESENTMANUAL4QC PRESENTMANUAL5
## 338582 240209 338928
## PRESENTMANUAL5QC PRESENTMANUAL6 PRESENTMANUAL6QC
## 240261 338933 240265
## PRESENTMANUAL7 PRESENTMANUAL7QC PRESENTAUTOMATED1
## 338947 240267 288353
## PRESENTAUTOMATED1QC PRESENTAUTOMATED2 PRESENTAUTOMATED2QC
## 277663 321077 321069
## PRESENTAUTOMATED3 PRESENTAUTOMATED3QC PASTMANUAL1
## 338759 338759 338952
## PASTMANUAL1QC WXPASTPERIOD1 WXPASTPERIOD1QC
## 330958 338952 338952
## PASTMANUAL2 PASTMANUAL2QC WXPASTPERIOD2
## 338952 336525 338952
## WXPASTPERIOD2QC PASTAUTOMATED1 PASTAUTOMATED1QC
## 338952 338952 330958
## WXPASTAUTOPERIOD1 WXPASTAUTOPERIOD1QC PASTAUTOMATED2
## 338952 338952 338952
## PASTAUTOMATED2QC WXPASTAUTOPERIOD2 WXPASTAUTOPERIOD2QC
## 336525 338952 338952
## RUNWAYENDBEARING RUNWAYDESIGNATOR RUNWAYVISUALRANGE
## 335642 0 335642
## CLOUDCOVER CLOUDCOVERQC CLOUDCOVERLO
## 92291 79582 337425
## CLOUDCOVERLOQC CLOUDBASEHEIGHT CLOUDBASEHEIGHTQC
## 324082 205022 204090
## CLOUDTYPELO CLOUDTYPELOQC CLOUDTYPEMID
## 336727 323384 337219
## CLOUDTYPEMIDQC CLOUDTYPEHI CLOUDTYPEHIQC
## 323876 337385 324042
## SUNSHINE SURFACECODE SURFACECODEQC
## 338952 338950 338952
## SOILTEMPERATURE SOILTEMPERATUREQC SOILDEPTH
## 338952 338952 338952
## OBSERVATIONPERIODSOILT OBSERVATIONPERIODSOILTQC ALTIMETERSETTING
## 338952 338952 33334
## ALTIMETERSETTINGQC STATIONPRESSURE STATIONPRESSUREQC
## 31151 287933 271974
## PRESSURETENDENCY PRESSURETENDENCYQC PRESSURE3HOURCHG
## 248516 232420 244280
## PRESSURE3HOURCHGQC PRESSURE24HOURCHG PRESSURE24HOURCHGQC
## 228862 338094 320084
## PRESSURETREND ISOBARICSURFACE ISOBARICSURFACEQC
## 338952 338951 338952
## ISOBARICSURFACEHEIGHT ISOBARICSURFACEHEIGHTQC SEASURFACETEMP
## 338951 338952 338952
## SEASURFACETEMPQC REMARKSYN REMARKMET
## 338950 0 0
## REMARKAWY HORIZONTALDATUM VERTICALDATUM
## 0 0 0
## LIGHTNINGFREQUENCY RECEIPTDTG INSERTIONTIME
## 338952 212050 0
## BLKSTN
## 126908Removed Columns and Filled-in NULLs
10-year
colSums((is.na(pajn_toKeep10)))## PLATFORMID NETWORKTYPE OBSERVATIONTIME REPORTTYPECODE
## 0 0 0 0
## LATITUDE LONGITUDE WINDDIRECTION WINDSPEED
## 0 0 0 0
## WINDGUSTSPEED CLOUDCEILING VISIBILITY AIRTEMPERATURE
## 0 0 0 0
## DEWPOINTTEMPERATURE PRECIPAMOUNT1 CLOUDCOVER ALTIMETERSETTING
## 0 0 0 0
## date JD
## 0 020-year
colSums((is.na(pajn_toKeep20)))## PLATFORMID NETWORKTYPE OBSERVATIONTIME REPORTTYPECODE
## 0 0 0 0
## LATITUDE LONGITUDE WINDDIRECTION WINDSPEED
## 0 0 0 0
## WINDGUSTSPEED CLOUDCEILING VISIBILITY AIRTEMPERATURE
## 0 0 0 0
## DEWPOINTTEMPERATURE PRECIPAMOUNT1 CLOUDCOVER ALTIMETERSETTING
## 0 0 0 0
## date JD
## 0 030-year
colSums((is.na(pajn_toKeep30)))## PLATFORMID NETWORKTYPE OBSERVATIONTIME REPORTTYPECODE
## 0 0 0 0
## LATITUDE LONGITUDE WINDDIRECTION WINDSPEED
## 0 0 0 0
## WINDGUSTSPEED CLOUDCEILING VISIBILITY AIRTEMPERATURE
## 0 0 0 0
## DEWPOINTTEMPERATURE PRECIPAMOUNT1 CLOUDCOVER ALTIMETERSETTING
## 0 0 0 0
## date JD
## 0 0El Paso, TX
Initial Nulls:
colSums((is.na(kelp_data)))## PLATFORMID NETWORKTYPE OBSERVATIONTIME
## 0 0 0
## REPORTTYPECODE LATITUDE LONGITUDE
## 0 0 0
## MONTH SECURITYID DISTRIBUTIONCD
## 0 0 0
## STATIONMODE PLATFORMHEIGHT CALLLETTER
## 203629 0 0
## VERSION WINDDIRECTION WINDDIRECTIONQC
## 0 55389 47220
## WINDCONDITIONS WINDCONDITIONSQC WINDSPEED
## 0 205785 1325
## WINDSPEEDQC STARTDIRECTION ENDDIRECTION
## 2156 332776 332777
## WINDGUSTSPEED WINDGUSTSPEEDQC WINDMEASUREMENTMODE
## 287522 271958 206652
## CLOUDCEILING CLOUDCEILINGQC CEILINGDETERMINATION
## 40062 35622 0
## CEILINGDETERMINATIONQC CLOUDCAVOK CLOUDCAVOKQC
## 315743 0 206565
## VISIBILITY VISIBILITYQC VISIBILITYTYPE
## 209 207 0
## VISIBILITYTYPEQC AIRTEMPERATURE AIRTEMPERATUREQC
## 563 338 332
## DEWPOINTTEMPERATURE DEWPOINTTEMPERATUREQC SEALEVELPRESSURE
## 413 369 12623
## SEALEVELPRESSUREQC OBSERVATIONPERIODPP1 OBSERVATIONPERIODPP1QC
## 12088 299985 321156
## PRECIPAMOUNT1 PRECIPAMOUNT1QC PRECIPCONDITION1
## 296543 319520 308095
## PRECIPCONDITION1QC OBSERVATIONPERIODPP2 OBSERVATIONPERIODPP2QC
## 319599 329579 332354
## PRECIPAMOUNT2 PRECIPAMOUNT2QC PRECIPCONDITION2
## 329627 332113 331581
## PRECIPCONDITION2QC OBSERVATIONPERIODPP3 OBSERVATIONPERIODPP3QC
## 332113 333604 333727
## PRECIPAMOUNT3 PRECIPAMOUNT3QC PRECIPCONDITION3
## 333604 333711 333693
## PRECIPCONDITION3QC OBSERVATIONPERIODPP4 OBSERVATIONPERIODPP4QC
## 333711 333824 333824
## PRECIPAMOUNT4 PRECIPAMOUNT4QC PRECIPCONDITION4
## 333824 333824 333824
## PRECIPCONDITION4QC PRECIPHISTDUR PRECIPHISTDURQC
## 333824 333662 332351
## PRECIPHISTCHAR PRECIPHISTCHARQC PRECIPDISC
## 0 333824 285242
## PRECIPDISCQC PRECIPBOGUS PRECIPBOGUSQC
## 333824 319424 333824
## PRECIPAMOUNTSD PRECIPAMOUNTSDQC PRECIPCONDITIONSD
## 333268 333787 333790
## PRECIPCONDITIONSDQC DEPTHWTREQUIV DEPTHWTREQUIVQC
## 333791 333787 333824
## DEPTHWECOND DEPTHWECONDQC HAILSIZE
## 333824 333824 333824
## PRECIPAMOUNTSF1 PRECIPAMOUNTSF1QC PRECIPCONDITIONSF1
## 333824 333824 333824
## PRECIPCONDITIONSF1QC OBSERVATIONPERIODSF1 OBSERVATIONPERIODSF1QC
## 333824 333814 333814
## PRECIPAMOUNTSF2 PRECIPAMOUNTSF2QC PRECIPCONDITIONSF2
## 333824 333824 333824
## PRECIPCONDITIONSF2QC OBSERVATIONPERIODSF2 OBSERVATIONPERIODSF2QC
## 333824 333824 333824
## PRECIPAMOUNTSF3 PRECIPAMOUNTSF3QC PRECIPCONDITIONSF3
## 333824 333824 333824
## PRECIPCONDITIONSF3QC OBSERVATIONPERIODSF3 OBSERVATIONPERIODSF3QC
## 333824 333824 333824
## PRECIPAMOUNTSF4 PRECIPAMOUNTSF4QC PRECIPCONDITIONSF4
## 333824 333824 333824
## PRECIPCONDITIONSF4QC OBSERVATIONPERIODSF4 OBSERVATIONPERIODSF4QC
## 333824 333824 333824
## PRESENTMANUAL1 PRESENTMANUAL1QC PRESENTMANUAL2
## 252811 252117 274388
## PRESENTMANUAL2QC PRESENTMANUAL3 PRESENTMANUAL3QC
## 257997 275911 258859
## PRESENTMANUAL4 PRESENTMANUAL4QC PRESENTMANUAL5
## 333805 264138 333823
## PRESENTMANUAL5QC PRESENTMANUAL6 PRESENTMANUAL6QC
## 264140 333824 264140
## PRESENTMANUAL7 PRESENTMANUAL7QC PRESENTAUTOMATED1
## 333824 264140 328742
## PRESENTAUTOMATED1QC PRESENTAUTOMATED2 PRESENTAUTOMATED2QC
## 318700 333271 333268
## PRESENTAUTOMATED3 PRESENTAUTOMATED3QC PASTMANUAL1
## 333809 333809 333589
## PASTMANUAL1QC WXPASTPERIOD1 WXPASTPERIOD1QC
## 332774 333589 333589
## PASTMANUAL2 PASTMANUAL2QC WXPASTPERIOD2
## 333589 333501 333589
## WXPASTPERIOD2QC PASTAUTOMATED1 PASTAUTOMATED1QC
## 333589 333824 333009
## WXPASTAUTOPERIOD1 WXPASTAUTOPERIOD1QC PASTAUTOMATED2
## 333824 333824 333824
## PASTAUTOMATED2QC WXPASTAUTOPERIOD2 WXPASTAUTOPERIOD2QC
## 333736 333824 333824
## RUNWAYENDBEARING RUNWAYDESIGNATOR RUNWAYVISUALRANGE
## 333798 0 333798
## CLOUDCOVER CLOUDCOVERQC CLOUDCOVERLO
## 42511 37433 319937
## CLOUDCOVERLOQC CLOUDBASEHEIGHT CLOUDBASEHEIGHTQC
## 309356 244121 242059
## CLOUDTYPELO CLOUDTYPELOQC CLOUDTYPEMID
## 318424 307843 318635
## CLOUDTYPEMIDQC CLOUDTYPEHI CLOUDTYPEHIQC
## 308054 318592 308011
## SUNSHINE SURFACECODE SURFACECODEQC
## 333824 333821 333824
## SOILTEMPERATURE SOILTEMPERATUREQC SOILDEPTH
## 333824 333824 333824
## OBSERVATIONPERIODSOILT OBSERVATIONPERIODSOILTQC ALTIMETERSETTING
## 333824 333824 63805
## ALTIMETERSETTINGQC STATIONPRESSURE STATIONPRESSUREQC
## 59434 228536 214913
## PRESSURETENDENCY PRESSURETENDENCYQC PRESSURE3HOURCHG
## 207496 193486 202823
## PRESSURE3HOURCHGQC PRESSURE24HOURCHG PRESSURE24HOURCHGQC
## 189536 333175 315196
## PRESSURETREND ISOBARICSURFACE ISOBARICSURFACEQC
## 333824 333824 333824
## ISOBARICSURFACEHEIGHT ISOBARICSURFACEHEIGHTQC SEASURFACETEMP
## 333824 333824 333824
## SEASURFACETEMPQC REMARKSYN REMARKMET
## 333821 0 0
## REMARKAWY HORIZONTALDATUM VERTICALDATUM
## 0 0 0
## LIGHTNINGFREQUENCY RECEIPTDTG INSERTIONTIME
## 333824 206560 0
## BLKSTN
## 130195Removed Columns and Filled-in NULLs
10-year
colSums((is.na(kelp_toKeep10)))## PLATFORMID NETWORKTYPE OBSERVATIONTIME REPORTTYPECODE
## 0 0 0 0
## LATITUDE LONGITUDE WINDDIRECTION WINDSPEED
## 0 0 0 0
## WINDGUSTSPEED CLOUDCEILING VISIBILITY AIRTEMPERATURE
## 0 0 0 0
## DEWPOINTTEMPERATURE PRECIPAMOUNT1 CLOUDCOVER ALTIMETERSETTING
## 0 0 0 0
## date JD
## 0 020-year
colSums((is.na(kelp_toKeep20)))## PLATFORMID NETWORKTYPE OBSERVATIONTIME REPORTTYPECODE
## 0 0 0 0
## LATITUDE LONGITUDE WINDDIRECTION WINDSPEED
## 0 0 0 0
## WINDGUSTSPEED CLOUDCEILING VISIBILITY AIRTEMPERATURE
## 0 0 0 0
## DEWPOINTTEMPERATURE PRECIPAMOUNT1 CLOUDCOVER ALTIMETERSETTING
## 0 0 0 0
## date JD
## 0 030-year
colSums((is.na(kelp_toKeep30)))## PLATFORMID NETWORKTYPE OBSERVATIONTIME REPORTTYPECODE
## 0 0 0 0
## LATITUDE LONGITUDE WINDDIRECTION WINDSPEED
## 0 0 0 0
## WINDGUSTSPEED CLOUDCEILING VISIBILITY AIRTEMPERATURE
## 0 0 0 0
## DEWPOINTTEMPERATURE PRECIPAMOUNT1 CLOUDCOVER ALTIMETERSETTING
## 0 0 0 0
## date JD
## 0 0St George, UT
Initial Nulls:
colSums((is.na(ksgu_data)))## PLATFORMID NETWORKTYPE OBSERVATIONTIME
## 0 0 0
## REPORTTYPECODE LATITUDE LONGITUDE
## 0 3 2
## MONTH SECURITYID DISTRIBUTIONCD
## 0 2 0
## STATIONMODE PLATFORMHEIGHT CALLLETTER
## 324434 4 0
## VERSION WINDDIRECTION WINDDIRECTIONQC
## 0 137503 117192
## WINDCONDITIONS WINDCONDITIONSQC WINDSPEED
## 0 322045 1011
## WINDSPEEDQC STARTDIRECTION ENDDIRECTION
## 840 482986 482986
## WINDGUSTSPEED WINDGUSTSPEEDQC WINDMEASUREMENTMODE
## 432129 398324 324665
## CLOUDCEILING CLOUDCEILINGQC CEILINGDETERMINATION
## 18320 18130 0
## CEILINGDETERMINATIONQC CLOUDCAVOK CLOUDCAVOKQC
## 446271 0 324434
## VISIBILITY VISIBILITYQC VISIBILITYTYPE
## 1695 1695 0
## VISIBILITYTYPEQC AIRTEMPERATURE AIRTEMPERATUREQC
## 233 811 645
## DEWPOINTTEMPERATURE DEWPOINTTEMPERATUREQC SEALEVELPRESSURE
## 1665 1489 425357
## SEALEVELPRESSUREQC OBSERVATIONPERIODPP1 OBSERVATIONPERIODPP1QC
## 387387 470051 479509
## PRECIPAMOUNT1 PRECIPAMOUNT1QC PRECIPCONDITION1
## 470430 479484 479115
## PRECIPCONDITION1QC OBSERVATIONPERIODPP2 OBSERVATIONPERIODPP2QC
## 479115 483206 483606
## PRECIPAMOUNT2 PRECIPAMOUNT2QC PRECIPCONDITION2
## 483216 483574 483560
## PRECIPCONDITION2QC OBSERVATIONPERIODPP3 OBSERVATIONPERIODPP3QC
## 483559 484293 484332
## PRECIPAMOUNT3 PRECIPAMOUNT3QC PRECIPCONDITION3
## 484298 484335 484330
## PRECIPCONDITION3QC OBSERVATIONPERIODPP4 OBSERVATIONPERIODPP4QC
## 484329 484391 484391
## PRECIPAMOUNT4 PRECIPAMOUNT4QC PRECIPCONDITION4
## 484391 484391 484391
## PRECIPCONDITION4QC PRECIPHISTDUR PRECIPHISTDURQC
## 484391 484391 483930
## PRECIPHISTCHAR PRECIPHISTCHARQC PRECIPDISC
## 484391 484391 442834
## PRECIPDISCQC PRECIPBOGUS PRECIPBOGUSQC
## 484391 470523 484391
## PRECIPAMOUNTSD PRECIPAMOUNTSDQC PRECIPCONDITIONSD
## 483904 484391 484391
## PRECIPCONDITIONSDQC DEPTHWTREQUIV DEPTHWTREQUIVQC
## 484390 484390 484391
## DEPTHWECOND DEPTHWECONDQC HAILSIZE
## 484390 484391 484390
## PRECIPAMOUNTSF1 PRECIPAMOUNTSF1QC PRECIPCONDITIONSF1
## 484391 484390 484391
## PRECIPCONDITIONSF1QC OBSERVATIONPERIODSF1 OBSERVATIONPERIODSF1QC
## 484390 484391 484390
## PRECIPAMOUNTSF2 PRECIPAMOUNTSF2QC PRECIPCONDITIONSF2
## 484391 484390 484391
## PRECIPCONDITIONSF2QC OBSERVATIONPERIODSF2 OBSERVATIONPERIODSF2QC
## 484391 484391 484391
## PRECIPAMOUNTSF3 PRECIPAMOUNTSF3QC PRECIPCONDITIONSF3
## 484391 484391 484391
## PRECIPCONDITIONSF3QC OBSERVATIONPERIODSF3 OBSERVATIONPERIODSF3QC
## 484391 484391 484391
## PRECIPAMOUNTSF4 PRECIPAMOUNTSF4QC PRECIPCONDITIONSF4
## 484391 484391 484391
## PRECIPCONDITIONSF4QC OBSERVATIONPERIODSF4 OBSERVATIONPERIODSF4QC
## 484391 484390 484390
## PRESENTMANUAL1 PRESENTMANUAL1QC PRESENTMANUAL2
## 423991 423737 444590
## PRESENTMANUAL2QC PRESENTMANUAL3 PRESENTMANUAL3QC
## 426334 444596 426340
## PRESENTMANUAL4 PRESENTMANUAL4QC PRESENTMANUAL5
## 484391 431085 484390
## PRESENTMANUAL5QC PRESENTMANUAL6 PRESENTMANUAL6QC
## 431086 484391 431086
## PRESENTMANUAL7 PRESENTMANUAL7QC PRESENTAUTOMATED1
## 484391 431086 479656
## PRESENTAUTOMATED1QC PRESENTAUTOMATED2 PRESENTAUTOMATED2QC
## 460398 483833 0
## PRESENTAUTOMATED3 PRESENTAUTOMATED3QC PASTMANUAL1
## 484386 484386 484391
## PASTMANUAL1QC WXPASTPERIOD1 WXPASTPERIOD1QC
## 484137 0 484390
## PASTMANUAL2 PASTMANUAL2QC WXPASTPERIOD2
## 484391 484391 0
## WXPASTPERIOD2QC PASTAUTOMATED1 PASTAUTOMATED1QC
## 484391 484391 484136
## WXPASTAUTOPERIOD1 WXPASTAUTOPERIOD1QC PASTAUTOMATED2
## 484390 0 484390
## PASTAUTOMATED2QC WXPASTAUTOPERIOD2 WXPASTAUTOPERIOD2QC
## 484391 484391 484391
## RUNWAYENDBEARING RUNWAYDESIGNATOR RUNWAYVISUALRANGE
## 484391 0 484390
## CLOUDCOVER CLOUDCOVERQC CLOUDCOVERLO
## 39324 36846 484385
## CLOUDCOVERLOQC CLOUDBASEHEIGHT CLOUDBASEHEIGHTQC
## 481077 459412 459412
## CLOUDTYPELO CLOUDTYPELOQC CLOUDTYPEMID
## 484391 481083 484391
## CLOUDTYPEMIDQC CLOUDTYPEHI CLOUDTYPEHIQC
## 0 484391 481083
## SUNSHINE SURFACECODE SURFACECODEQC
## 484391 484391 0
## SOILTEMPERATURE SOILTEMPERATUREQC SOILDEPTH
## 484390 484391 484391
## OBSERVATIONPERIODSOILT OBSERVATIONPERIODSOILTQC ALTIMETERSETTING
## 484391 484391 2061
## ALTIMETERSETTINGQC STATIONPRESSURE STATIONPRESSUREQC
## 1382 484391 446307
## PRESSURETENDENCY PRESSURETENDENCYQC PRESSURE3HOURCHG
## 442703 403769 441059
## PRESSURE3HOURCHGQC PRESSURE24HOURCHG PRESSURE24HOURCHGQC
## 402135 484391 446307
## PRESSURETREND ISOBARICSURFACE ISOBARICSURFACEQC
## 484391 484391 484391
## ISOBARICSURFACEHEIGHT ISOBARICSURFACEHEIGHTQC SEASURFACETEMP
## 484391 484391 484391
## SEASURFACETEMPQC REMARKSYN REMARKMET
## 484384 484391 0
## REMARKAWY HORIZONTALDATUM VERTICALDATUM
## 0 0 0
## LIGHTNINGFREQUENCY RECEIPTDTG INSERTIONTIME
## 484391 324434 0
## BLKSTN
## 159965Removed Columns and Filled-in NULLs
10-year
colSums((is.na(ksgu_toKeep10)))## PLATFORMID NETWORKTYPE OBSERVATIONTIME REPORTTYPECODE
## 0 0 0 0
## LATITUDE LONGITUDE WINDDIRECTION WINDSPEED
## 0 0 0 0
## WINDGUSTSPEED CLOUDCEILING VISIBILITY AIRTEMPERATURE
## 0 0 0 0
## DEWPOINTTEMPERATURE PRECIPAMOUNT1 CLOUDCOVER ALTIMETERSETTING
## 0 0 0 0
## date JD
## 0 020-year
colSums((is.na(ksgu_toKeep20)))## PLATFORMID NETWORKTYPE OBSERVATIONTIME REPORTTYPECODE
## 0 0 0 0
## LATITUDE LONGITUDE WINDDIRECTION WINDSPEED
## 0 0 0 0
## WINDGUSTSPEED CLOUDCEILING VISIBILITY AIRTEMPERATURE
## 0 0 0 0
## DEWPOINTTEMPERATURE PRECIPAMOUNT1 CLOUDCOVER ALTIMETERSETTING
## 0 0 0 0
## date JD
## 0 030-year
colSums((is.na(ksgu_toKeep30)))## PLATFORMID NETWORKTYPE OBSERVATIONTIME REPORTTYPECODE
## 0 0 0 0
## LATITUDE LONGITUDE WINDDIRECTION WINDSPEED
## 0 0 0 0
## WINDGUSTSPEED CLOUDCEILING VISIBILITY AIRTEMPERATURE
## 0 0 0 0
## DEWPOINTTEMPERATURE PRECIPAMOUNT1 CLOUDCOVER ALTIMETERSETTING
## 0 0 0 0
## date JD
## 0 0Data Interrogation and Modeling
We can interrogate the data in many ways. The following are ways we will do this:
- Plot desired variable averages for each timespan and visualize the ANOVA results,
- Use “spline” and get good estimations of the variables by timespan and then run and visualize ANOVA results,
- Run “spline” on the entire dataset by timespan to see if there a reasonable approximation could be determined for the data
Composite Splines
For each of the below plots, the following applies:
- Blue solid line - 10-year dataset spline
- Red dashed lines - 10-year dataset 95% confidence intervals
- Orange solid line - 20-year dataset spline
- Brown dashed lines - 20-year dataset 95% confidence intervals
- Green solid line - 30-year dataset spline
- Purple dashed lines - 30-year dataset 95% confidence intervals
Robert’s Field, OR
Temperature
10, 20, 30-year 10-day avg spline
composite
Dewpoint Temperature
10, 20, 30-year 10-day avg spline
composite
Wind Direction
10, 20, 30-year 10-day avg spline
composite
Wind Speed
10, 20, 30-year 10-day avg spline
composite
Wind Gust Speed
10, 20, 30-year 10-day avg spline
composite
Average Cloud Ceiling Height
10, 20, 30-year 10-day avg spline
composite
Average Visibility
10, 20, 30-year 10-day avg spline
composite
Average Cloud Cover
10, 20, 30-year 10-day avg spline
composite
Average Altimeter Setting
10, 20, 30-year 10-day avg spline
composite
Average Precipitation
10, 20, 30-year Precipitation Comparison by Rain
event
Buffalo, NY
Temperature
10, 20, 30-year 10-day avg spline
composite
Dewpoint Temperature
10, 20, 30-year 10-day avg spline
composite
Wind Direction
10, 20, 30-year 10-day avg spline
composite
Wind Speed
10, 20, 30-year 10-day avg spline
composite
Wind Gust Speed
10, 20, 30-year 10-day avg spline
composite
Average Cloud Ceiling Height
10, 20, 30-year 10-day avg spline
composite
Average Visibility
10, 20, 30-year 10-day avg spline
composite
Average Cloud Cover
10, 20, 30-year 10-day avg spline
composite
Average Altimeter Setting
10, 20, 30-year 10-day avg spline
composite
Average Precipitation
10, 20, 30-year Precipitation Comparison by Rain
event
Topeka, KS
Temperature
10, 20, 30-year 10-day avg spline
composite
Dewpoint Temperature
10, 20, 30-year 10-day avg spline
composite
Wind Direction
10, 20, 30-year 10-day avg spline
composite
Wind Speed
10, 20, 30-year 10-day avg spline
composite
Wind Gust Speed
10, 20, 30-year 10-day avg spline
composite
Average Cloud Ceiling Height
10, 20, 30-year 10-day avg spline
composite
Average Visibility
10, 20, 30-year 10-day avg spline
composite
Average Cloud Cover
10, 20, 30-year 10-day avg spline
composite
Average Altimeter Setting
10, 20, 30-year 10-day avg spline
composite
Average Precipitation
10, 20, 30-year Precipitation Comparison by Rain
event
Madison, WI
Temperature
10, 20, 30-year 10-day avg spline
composite
Dewpoint Temperature
10, 20, 30-year 10-day avg spline
composite
Wind Direction
10, 20, 30-year 10-day avg spline
composite
Wind Speed
10, 20, 30-year 10-day avg spline
composite
Wind Gust Speed
10, 20, 30-year 10-day avg spline
composite
Average Cloud Ceiling Height
10, 20, 30-year 10-day avg spline
composite
Average Visibility
10, 20, 30-year 10-day avg spline
composite
Average Cloud Cover
10, 20, 30-year 10-day avg spline
composite
Average Altimeter Setting
10, 20, 30-year 10-day avg spline
composite
Average Precipitation
10, 20, 30-year Precipitation Comparison by Rain
event
Tri-cities Airport, TN
Temperature
10, 20, 30-year 10-day avg spline
composite
Dewpoint Temperature
10, 20, 30-year 10-day avg spline
composite
Wind Direction
10, 20, 30-year 10-day avg spline
composite
Wind Speed
10, 20, 30-year 10-day avg spline
composite
Wind Gust Speed
10, 20, 30-year 10-day avg spline
composite
Average Cloud Ceiling Height
10, 20, 30-year 10-day avg spline
composite
Average Visibility
10, 20, 30-year 10-day avg spline
composite
Average Cloud Cover
10, 20, 30-year 10-day avg spline
composite
Average Altimeter Setting
10, 20, 30-year 10-day avg spline
composite
Average Precipitation
10, 20, 30-year Precipitation Comparison by Rain
event
Juneau, AK
Temperature
10, 20, 30-year 10-day avg spline
composite
Dewpoint Temperature
10, 20, 30-year 10-day avg spline
composite
Wind Direction
10, 20, 30-year 10-day avg spline
composite
Wind Speed
10, 20, 30-year 10-day avg spline
composite
Wind Gust Speed
10, 20, 30-year 10-day avg spline
composite
Average Cloud Ceiling Height
10, 20, 30-year 10-day avg spline
composite
Average Visibility
10, 20, 30-year 10-day avg spline
composite
Average Cloud Cover
10, 20, 30-year 10-day avg spline
composite
Average Altimeter Setting
10, 20, 30-year 10-day avg spline
composite
Average Precipitation
10, 20, 30-year Precipitation Comparison by Rain
event
El Paso, TX
Temperature
10, 20, 30-year 10-day avg spline
composite
Dewpoint Temperature
10, 20, 30-year 10-day avg spline
composite
Wind Direction
10, 20, 30-year 10-day avg spline
composite
Wind Speed
10, 20, 30-year 10-day avg spline
composite
Wind Gust Speed
10, 20, 30-year 10-day avg spline
composite
Average Cloud Ceiling Height
10, 20, 30-year 10-day avg spline
composite
Average Visibility
10, 20, 30-year 10-day avg spline
composite
Average Cloud Cover
10, 20, 30-year 10-day avg spline
composite
Average Altimeter Setting
10, 20, 30-year 10-day avg spline
composite
Average Precipitation
10, 20, 30-year Precipitation Comparison by Rain
event
St George
Temperature
10, 20, 30-year 10-day avg spline
composite
Dewpoint Temperature
10, 20, 30-year 10-day avg spline
composite
Wind Direction
10, 20, 30-year 10-day avg spline
composite
Wind Speed
10, 20, 30-year 10-day avg spline
composite
Wind Gust Speed
10, 20, 30-year 10-day avg spline
composite
Average Cloud Ceiling Height
10, 20, 30-year 10-day avg spline
composite
Average Visibility
10, 20, 30-year 10-day avg spline
composite
Average Cloud Cover
10, 20, 30-year 10-day avg spline
composite
Average Altimeter Setting
10, 20, 30-year 10-day avg spline
composite
Average Precipitation
10, 20, 30-year Precipitation Comparison by Rain
event
Domain 4
Data Visualization
ANOVA Tests
Summary - the most common, overall significance is the comparison between the Monthly and the 10-day splines. There were a couple outliers, but generally, this shows the highest, consistent level of significance and we can focus on comparing these two outputs.
KRDM
Temperature
anova(krdm_t10_spline_d,krdm_t10_spline_10d)## Analysis of Variance Table
##
## Model 1: krdm_avgTemp10 ~ ns(c(1:366), df = 364)
## Model 2: krdm_avgTemp10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.06
## 2 330 471.69 -329 -471.63 23.324 0.1639anova(krdm_t10_spline_d,krdm_t10_spline_m)## Analysis of Variance Table
##
## Model 1: krdm_avgTemp10 ~ ns(c(1:366), df = 364)
## Model 2: krdm_avgTemp10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.06
## 2 354 662.39 -353 -662.33 30.528 0.1435anova(krdm_t10_spline_m,krdm_t10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: krdm_avgTemp10 ~ ns(c(1:366), df = 11)
## Model 2: krdm_avgTemp10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 662.39
## 2 330 471.69 24 190.7 5.5591 0.00000000000005727 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(krdm_t20_spline_d,krdm_t20_spline_10d)## Analysis of Variance Table
##
## Model 1: krdm_avgTemp20 ~ ns(c(1:366), df = 364)
## Model 2: krdm_avgTemp20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.004
## 2 330 231.527 -329 -231.52 199.7 0.05637 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(krdm_t20_spline_d,krdm_t20_spline_m)## Analysis of Variance Table
##
## Model 1: krdm_avgTemp20 ~ ns(c(1:366), df = 364)
## Model 2: krdm_avgTemp20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.00
## 2 354 363.44 -353 -363.44 292.17 0.04662 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(krdm_t20_spline_m,krdm_t20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: krdm_avgTemp20 ~ ns(c(1:366), df = 11)
## Model 2: krdm_avgTemp20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 363.44
## 2 330 231.53 24 131.91 7.8342 < 0.00000000000000022 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(krdm_t30_spline_d,krdm_t30_spline_10d)## Analysis of Variance Table
##
## Model 1: krdm_avgTemp30 ~ ns(c(1:366), df = 364)
## Model 2: krdm_avgTemp30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.003
## 2 330 150.829 -329 -150.83 145.16 0.0661 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(krdm_t30_spline_d,krdm_t30_spline_m)## Analysis of Variance Table
##
## Model 1: krdm_avgTemp30 ~ ns(c(1:366), df = 364)
## Model 2: krdm_avgTemp30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.003
## 2 354 225.399 -353 -225.4 202.18 0.05603 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(krdm_t30_spline_m,krdm_t30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: krdm_avgTemp30 ~ ns(c(1:366), df = 11)
## Model 2: krdm_avgTemp30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 225.40
## 2 330 150.83 24 74.57 6.798 < 0.00000000000000022 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Dewpoint Temperature
anova(krdm_dp10_spline_d,krdm_dp10_spline_10d) ## Analysis of Variance Table
##
## Model 1: krdm_avgDPTemp10 ~ ns(c(1:366), df = 364)
## Model 2: krdm_avgDPTemp10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.37
## 2 330 382.72 -329 -382.35 3.1279 0.4278anova(krdm_dp10_spline_d,krdm_dp10_spline_m)## Analysis of Variance Table
##
## Model 1: krdm_avgDPTemp10 ~ ns(c(1:366), df = 364)
## Model 2: krdm_avgDPTemp10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.37
## 2 354 580.62 -353 -580.25 4.4241 0.3652anova(krdm_dp10_spline_m,krdm_dp10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: krdm_avgDPTemp10 ~ ns(c(1:366), df = 11)
## Model 2: krdm_avgDPTemp10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 580.62
## 2 330 382.72 24 197.9 7.11 < 0.00000000000000022 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(krdm_dp20_spline_d,krdm_dp20_spline_10d)## Analysis of Variance Table
##
## Model 1: krdm_avgDPTemp20 ~ ns(c(1:366), df = 364)
## Model 2: krdm_avgDPTemp20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.201
## 2 330 210.819 -329 -210.62 3.1788 0.4247anova(krdm_dp20_spline_d,krdm_dp20_spline_m)## Analysis of Variance Table
##
## Model 1: krdm_avgDPTemp20 ~ ns(c(1:366), df = 364)
## Model 2: krdm_avgDPTemp20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.201
## 2 354 308.267 -353 -308.07 4.3334 0.3687anova(krdm_dp20_spline_m,krdm_dp20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: krdm_avgDPTemp20 ~ ns(c(1:366), df = 11)
## Model 2: krdm_avgDPTemp20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 308.27
## 2 330 210.82 24 97.448 6.3558 < 0.00000000000000022 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(krdm_dp30_spline_d,krdm_dp30_spline_10d)## Analysis of Variance Table
##
## Model 1: krdm_avgDPTemp30 ~ ns(c(1:366), df = 364)
## Model 2: krdm_avgDPTemp30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.09
## 2 330 129.03 -329 -128.94 4.344 0.3683anova(krdm_dp30_spline_d,krdm_dp30_spline_m)## Analysis of Variance Table
##
## Model 1: krdm_avgDPTemp30 ~ ns(c(1:366), df = 364)
## Model 2: krdm_avgDPTemp30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.09
## 2 354 184.45 -353 -184.36 5.7888 0.3221anova(krdm_dp30_spline_m,krdm_dp30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: krdm_avgDPTemp30 ~ ns(c(1:366), df = 11)
## Model 2: krdm_avgDPTemp30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 184.45
## 2 330 129.03 24 55.422 5.9059 0.000000000000004804 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Wind Direction
anova(krdm_wd10_spline_d,krdm_wd10_spline_10d)## Analysis of Variance Table
##
## Model 1: krdm_avgWDIR10 ~ ns(c(1:366), df = 364)
## Model 2: krdm_avgWDIR10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 4
## 2 330 68876 -329 -68871 47.613 0.1151anova(krdm_wd10_spline_d,krdm_wd10_spline_m)## Analysis of Variance Table
##
## Model 1: krdm_avgWDIR10 ~ ns(c(1:366), df = 364)
## Model 2: krdm_avgWDIR10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 4
## 2 354 84084 -353 -84079 54.175 0.108anova(krdm_wd10_spline_m,krdm_wd10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: krdm_avgWDIR10 ~ ns(c(1:366), df = 11)
## Model 2: krdm_avgWDIR10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 84084
## 2 330 68876 24 15208 3.036 0.000004402 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(krdm_wd20_spline_d,krdm_wd20_spline_10d)## Analysis of Variance Table
##
## Model 1: krdm_avgWDIR20 ~ ns(c(1:366), df = 364)
## Model 2: krdm_avgWDIR20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 7
## 2 330 35590 -329 -35582 14.938 0.204anova(krdm_wd20_spline_d,krdm_wd20_spline_m)## Analysis of Variance Table
##
## Model 1: krdm_avgWDIR20 ~ ns(c(1:366), df = 364)
## Model 2: krdm_avgWDIR20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 7
## 2 354 42925 -353 -42918 16.792 0.1927anova(krdm_wd20_spline_m,krdm_wd20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: krdm_avgWDIR20 ~ ns(c(1:366), df = 11)
## Model 2: krdm_avgWDIR20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 42925
## 2 330 35590 24 7335.5 2.8341 0.00001787 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(krdm_wd30_spline_d,krdm_wd30_spline_10d)## Analysis of Variance Table
##
## Model 1: krdm_avgWDIR30 ~ ns(c(1:366), df = 364)
## Model 2: krdm_avgWDIR30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 20
## 2 330 25775 -329 -25755 3.9222 0.3861anova(krdm_wd30_spline_d,krdm_wd30_spline_m)## Analysis of Variance Table
##
## Model 1: krdm_avgWDIR30 ~ ns(c(1:366), df = 364)
## Model 2: krdm_avgWDIR30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 20
## 2 354 30727 -353 -30707 4.3584 0.3678anova(krdm_wd30_spline_m,krdm_wd30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: krdm_avgWDIR30 ~ ns(c(1:366), df = 11)
## Model 2: krdm_avgWDIR30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 30727
## 2 330 25775 24 4951.7 2.6415 0.00006638 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Wind Speed
anova(krdm_ws10_spline_d,krdm_ws10_spline_10d)## Analysis of Variance Table
##
## Model 1: krdm_avgWSPD10 ~ ns(c(1:366), df = 364)
## Model 2: krdm_avgWSPD10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.045
## 2 330 43.692 -329 -43.647 2.9279 0.4407anova(krdm_ws10_spline_d,krdm_ws10_spline_m)## Analysis of Variance Table
##
## Model 1: krdm_avgWSPD10 ~ ns(c(1:366), df = 364)
## Model 2: krdm_avgWSPD10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.045
## 2 354 49.681 -353 -49.636 3.1033 0.4294anova(krdm_ws10_spline_m,krdm_ws10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: krdm_avgWSPD10 ~ ns(c(1:366), df = 11)
## Model 2: krdm_avgWSPD10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 49.681
## 2 330 43.692 24 5.9891 1.8848 0.008091 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(krdm_ws20_spline_d,krdm_ws20_spline_10d)## Analysis of Variance Table
##
## Model 1: krdm_avgWSPD20 ~ ns(c(1:366), df = 364)
## Model 2: krdm_avgWSPD20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.029
## 2 330 19.966 -329 -19.937 2.0926 0.5101anova(krdm_ws20_spline_d,krdm_ws20_spline_m)## Analysis of Variance Table
##
## Model 1: krdm_avgWSPD20 ~ ns(c(1:366), df = 364)
## Model 2: krdm_avgWSPD20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.029
## 2 354 23.380 -353 -23.351 2.2843 0.4914anova(krdm_ws20_spline_m,krdm_ws20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: krdm_avgWSPD20 ~ ns(c(1:366), df = 11)
## Model 2: krdm_avgWSPD20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 23.380
## 2 330 19.966 24 3.4137 2.3509 0.0004548 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(krdm_ws30_spline_d,krdm_ws30_spline_10d)## Analysis of Variance Table
##
## Model 1: krdm_avgWSPD30 ~ ns(c(1:366), df = 364)
## Model 2: krdm_avgWSPD30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.030
## 2 330 13.602 -329 -13.572 1.3728 0.606anova(krdm_ws30_spline_d,krdm_ws30_spline_m)## Analysis of Variance Table
##
## Model 1: krdm_avgWSPD30 ~ ns(c(1:366), df = 364)
## Model 2: krdm_avgWSPD30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.030
## 2 354 15.919 -353 -15.889 1.4979 0.5856anova(krdm_ws30_spline_m,krdm_ws30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: krdm_avgWSPD30 ~ ns(c(1:366), df = 11)
## Model 2: krdm_avgWSPD30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 15.919
## 2 330 13.602 24 2.3168 2.342 0.0004818 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Wind Gust Speed
anova(krdm_wg10_spline_d,krdm_wg10_spline_10d)## Analysis of Variance Table
##
## Model 1: krdm_avgWGSP10 ~ ns(c(1:366), df = 364)
## Model 2: krdm_avgWGSP10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.004
## 2 330 59.500 -329 -59.497 49.172 0.1133anova(krdm_wg10_spline_d,krdm_wg10_spline_m)## Analysis of Variance Table
##
## Model 1: krdm_avgWGSP10 ~ ns(c(1:366), df = 364)
## Model 2: krdm_avgWGSP10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.004
## 2 354 80.760 -353 -80.756 62.205 0.1008anova(krdm_wg10_spline_m,krdm_wg10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: krdm_avgWGSP10 ~ ns(c(1:366), df = 11)
## Model 2: krdm_avgWGSP10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 80.76
## 2 330 59.50 24 21.26 4.9129 0.000000000006028 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(krdm_wg20_spline_d,krdm_wg20_spline_10d)## Analysis of Variance Table
##
## Model 1: krdm_avgWGSP20 ~ ns(c(1:366), df = 364)
## Model 2: krdm_avgWGSP20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.0058
## 2 330 30.8709 -329 -30.865 16.046 0.197anova(krdm_wg20_spline_d,krdm_wg20_spline_m)## Analysis of Variance Table
##
## Model 1: krdm_avgWGSP20 ~ ns(c(1:366), df = 364)
## Model 2: krdm_avgWGSP20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.006
## 2 354 40.038 -353 -40.032 19.397 0.1795anova(krdm_wg20_spline_m,krdm_wg20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: krdm_avgWGSP20 ~ ns(c(1:366), df = 11)
## Model 2: krdm_avgWGSP20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 40.038
## 2 330 30.871 24 9.1671 4.0831 0.000000002469 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(krdm_wg30_spline_d,krdm_wg30_spline_10d)## Analysis of Variance Table
##
## Model 1: krdm_avgWGSP30 ~ ns(c(1:366), df = 364)
## Model 2: krdm_avgWGSP30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.0027
## 2 330 18.1484 -329 -18.146 20.675 0.1739anova(krdm_wg30_spline_d,krdm_wg30_spline_m)## Analysis of Variance Table
##
## Model 1: krdm_avgWGSP30 ~ ns(c(1:366), df = 364)
## Model 2: krdm_avgWGSP30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.0027
## 2 354 24.2375 -353 -24.235 25.735 0.1562anova(krdm_wg30_spline_m,krdm_wg30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: krdm_avgWGSP30 ~ ns(c(1:366), df = 11)
## Model 2: krdm_avgWGSP30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 24.238
## 2 330 18.148 24 6.0891 4.6133 0.00000000005282 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Average Cloud Ceiling Height
anova(krdm_cig10_spline_d,krdm_cig10_spline_10d)## Analysis of Variance Table
##
## Model 1: krdm_avgCCIG10 ~ ns(c(1:366), df = 364)
## Model 2: krdm_avgCCIG10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 900670
## 2 330 1266548193 -329 -1265647522 4.2712 0.3712anova(krdm_cig10_spline_d,krdm_cig10_spline_m)## Analysis of Variance Table
##
## Model 1: krdm_avgCCIG10 ~ ns(c(1:366), df = 364)
## Model 2: krdm_avgCCIG10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 900670
## 2 354 1521251912 -353 -1520351242 4.7819 0.3523anova(krdm_cig10_spline_m,krdm_cig10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: krdm_avgCCIG10 ~ ns(c(1:366), df = 11)
## Model 2: krdm_avgCCIG10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 1521251912
## 2 330 1266548193 24 254703719 2.7651 0.00002866 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(krdm_cig20_spline_d,krdm_cig20_spline_10d)## Analysis of Variance Table
##
## Model 1: krdm_avgCCIG20 ~ ns(c(1:366), df = 364)
## Model 2: krdm_avgCCIG20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 46080
## 2 330 600952613 -329 -600906533 39.636 0.1261anova(krdm_cig20_spline_d,krdm_cig20_spline_m)## Analysis of Variance Table
##
## Model 1: krdm_avgCCIG20 ~ ns(c(1:366), df = 364)
## Model 2: krdm_avgCCIG20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 46080
## 2 354 727065483 -353 -727019402 44.695 0.1188anova(krdm_cig20_spline_m,krdm_cig20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: krdm_avgCCIG20 ~ ns(c(1:366), df = 11)
## Model 2: krdm_avgCCIG20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 727065483
## 2 330 600952613 24 126112869 2.8855 0.00001253 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(krdm_cig30_spline_d,krdm_cig30_spline_10d)## Analysis of Variance Table
##
## Model 1: krdm_avgCCIG30 ~ ns(c(1:366), df = 364)
## Model 2: krdm_avgCCIG30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 28561
## 2 330 394151265 -329 -394122704 41.943 0.1226anova(krdm_cig30_spline_d,krdm_cig30_spline_m)## Analysis of Variance Table
##
## Model 1: krdm_avgCCIG30 ~ ns(c(1:366), df = 364)
## Model 2: krdm_avgCCIG30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 28561
## 2 354 464290231 -353 -464261670 46.048 0.1171anova(krdm_cig30_spline_m,krdm_cig30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: krdm_avgCCIG30 ~ ns(c(1:366), df = 11)
## Model 2: krdm_avgCCIG30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 464290231
## 2 330 394151265 24 70138966 2.4468 0.0002431 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Average Visibility
anova(krdm_vis10_spline_d,krdm_vis10_spline_10d)## Analysis of Variance Table
##
## Model 1: krdm_avgVIS10 ~ ns(c(1:366), df = 364)
## Model 2: krdm_avgVIS10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 20411
## 2 330 303916385 -329 -303895974 45.255 0.1181anova(krdm_vis10_spline_d,krdm_vis10_spline_m)## Analysis of Variance Table
##
## Model 1: krdm_avgVIS10 ~ ns(c(1:366), df = 364)
## Model 2: krdm_avgVIS10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 20411
## 2 354 354463682 -353 -354443271 49.193 0.1133anova(krdm_vis10_spline_m,krdm_vis10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: krdm_avgVIS10 ~ ns(c(1:366), df = 11)
## Model 2: krdm_avgVIS10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 354463682
## 2 330 303916385 24 50547296 2.2869 0.0006871 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(krdm_vis20_spline_d,krdm_vis20_spline_10d)## Analysis of Variance Table
##
## Model 1: krdm_avgVIS20 ~ ns(c(1:366), df = 364)
## Model 2: krdm_avgVIS20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 43141
## 2 330 129053052 -329 -129009911 9.0895 0.2597anova(krdm_vis20_spline_d,krdm_vis20_spline_m)## Analysis of Variance Table
##
## Model 1: krdm_avgVIS20 ~ ns(c(1:366), df = 364)
## Model 2: krdm_avgVIS20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 43141
## 2 354 142772597 -353 -142729456 9.3724 0.2559anova(krdm_vis20_spline_m,krdm_vis20_spline_10d) ## Analysis of Variance Table
##
## Model 1: krdm_avgVIS20 ~ ns(c(1:366), df = 11)
## Model 2: krdm_avgVIS20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 142772597
## 2 330 129053052 24 13719545 1.4618 0.07724 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(krdm_vis30_spline_d,krdm_vis30_spline_10d)## Analysis of Variance Table
##
## Model 1: krdm_avgVIS30 ~ ns(c(1:366), df = 364)
## Model 2: krdm_avgVIS30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 76578
## 2 330 81425004 -329 -81348426 3.2289 0.4218anova(krdm_vis30_spline_d,krdm_vis30_spline_m)## Analysis of Variance Table
##
## Model 1: krdm_avgVIS30 ~ ns(c(1:366), df = 364)
## Model 2: krdm_avgVIS30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 76578
## 2 354 91447309 -353 -91370732 3.3801 0.4132anova(krdm_vis30_spline_m,krdm_vis30_spline_10d)## Analysis of Variance Table
##
## Model 1: krdm_avgVIS30 ~ ns(c(1:366), df = 11)
## Model 2: krdm_avgVIS30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 91447309
## 2 330 81425004 24 10022305 1.6924 0.02381 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Average Cloud Cover
anova(krdm_cc10_spline_d,krdm_cc10_spline_10d)## Analysis of Variance Table
##
## Model 1: krdm_avgCCOV10 ~ ns(c(1:366), df = 364)
## Model 2: krdm_avgCCOV10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.00
## 2 330 115.35 -329 -115.35 6469.4 0.009912 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(krdm_cc10_spline_d,krdm_cc10_spline_m)## Analysis of Variance Table
##
## Model 1: krdm_avgCCOV10 ~ ns(c(1:366), df = 364)
## Model 2: krdm_avgCCOV10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.00
## 2 354 135.83 -353 -135.83 7100 0.009462 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(krdm_cc10_spline_m,krdm_cc10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: krdm_avgCCOV10 ~ ns(c(1:366), df = 11)
## Model 2: krdm_avgCCOV10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 135.83
## 2 330 115.35 24 20.48 2.4412 0.0002522 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(krdm_cc20_spline_d,krdm_cc20_spline_10d)## Analysis of Variance Table
##
## Model 1: krdm_avgCCOV20 ~ ns(c(1:366), df = 364)
## Model 2: krdm_avgCCOV20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.002
## 2 330 67.241 -329 -67.239 114.45 0.07442 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(krdm_cc20_spline_d,krdm_cc20_spline_m)## Analysis of Variance Table
##
## Model 1: krdm_avgCCOV20 ~ ns(c(1:366), df = 364)
## Model 2: krdm_avgCCOV20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.002
## 2 354 88.607 -353 -88.605 140.56 0.06717 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(krdm_cc20_spline_m,krdm_cc20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: krdm_avgCCOV20 ~ ns(c(1:366), df = 11)
## Model 2: krdm_avgCCOV20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 88.607
## 2 330 67.241 24 21.366 4.3691 0.0000000003105 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(krdm_cc30_spline_d,krdm_cc30_spline_10d)## Analysis of Variance Table
##
## Model 1: krdm_avgCCOV30 ~ ns(c(1:366), df = 364)
## Model 2: krdm_avgCCOV30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.015
## 2 330 42.380 -329 -42.365 8.6902 0.2653anova(krdm_cc30_spline_d,krdm_cc30_spline_m)## Analysis of Variance Table
##
## Model 1: krdm_avgCCOV30 ~ ns(c(1:366), df = 364)
## Model 2: krdm_avgCCOV30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.015
## 2 354 56.438 -353 -56.424 10.787 0.2391anova(krdm_cc30_spline_m,krdm_cc30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: krdm_avgCCOV30 ~ ns(c(1:366), df = 11)
## Model 2: krdm_avgCCOV30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 56.438
## 2 330 42.380 24 14.059 4.5612 0.00000000007706 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Average Altimeter Setting
anova(krdm_alt10_spline_d,krdm_alt10_spline_10d)## Analysis of Variance Table
##
## Model 1: krdm_avgALTS10 ~ ns(c(1:366), df = 364)
## Model 2: krdm_avgALTS10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.13
## 2 330 929.17 -329 -929.03 21.131 0.1721anova(krdm_alt10_spline_d,krdm_alt10_spline_m)## Analysis of Variance Table
##
## Model 1: krdm_avgALTS10 ~ ns(c(1:366), df = 364)
## Model 2: krdm_avgALTS10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.13
## 2 354 1225.35 -353 -1225.2 25.973 0.1554anova(krdm_alt10_spline_m,krdm_alt10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: krdm_avgALTS10 ~ ns(c(1:366), df = 11)
## Model 2: krdm_avgALTS10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 1225.35
## 2 330 929.17 24 296.18 4.3829 0.0000000002809 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(krdm_alt20_spline_d,krdm_alt20_spline_10d)## Analysis of Variance Table
##
## Model 1: krdm_avgALTS20 ~ ns(c(1:366), df = 364)
## Model 2: krdm_avgALTS20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.00
## 2 330 480.86 -329 -480.86 496.63 0.03576 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(krdm_alt20_spline_d,krdm_alt20_spline_m)## Analysis of Variance Table
##
## Model 1: krdm_avgALTS20 ~ ns(c(1:366), df = 364)
## Model 2: krdm_avgALTS20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.00
## 2 354 598.62 -353 -598.62 576.22 0.03321 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(krdm_alt20_spline_m,krdm_alt20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: krdm_avgALTS20 ~ ns(c(1:366), df = 11)
## Model 2: krdm_avgALTS20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 598.62
## 2 330 480.86 24 117.76 3.3674 0.0000004245 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(krdm_alt30_spline_d,krdm_alt30_spline_10d)## Analysis of Variance Table
##
## Model 1: krdm_avgALTS30 ~ ns(c(1:366), df = 364)
## Model 2: krdm_avgALTS30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.01
## 2 330 332.05 -329 -332.04 129.49 0.06997 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(krdm_alt30_spline_d,krdm_alt30_spline_m)## Analysis of Variance Table
##
## Model 1: krdm_avgALTS30 ~ ns(c(1:366), df = 364)
## Model 2: krdm_avgALTS30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.01
## 2 354 400.83 -353 -400.82 145.69 0.06598 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(krdm_alt30_spline_m,krdm_alt30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: krdm_avgALTS30 ~ ns(c(1:366), df = 11)
## Model 2: krdm_avgALTS30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 400.83
## 2 330 332.05 24 68.773 2.8478 0.00001625 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1KBUF
Temperature
anova(kbuf_t10_spline_d,kbuf_t10_spline_10d)## Analysis of Variance Table
##
## Model 1: kbuf_avgTemp10 ~ ns(c(1:366), df = 364)
## Model 2: kbuf_avgTemp10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.23
## 2 330 457.92 -329 -457.69 5.946 0.318anova(kbuf_t10_spline_d,kbuf_t10_spline_m)## Analysis of Variance Table
##
## Model 1: kbuf_avgTemp10 ~ ns(c(1:366), df = 364)
## Model 2: kbuf_avgTemp10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.23
## 2 354 593.79 -353 -593.55 7.1868 0.2906anova(kbuf_t10_spline_m,kbuf_t10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kbuf_avgTemp10 ~ ns(c(1:366), df = 11)
## Model 2: kbuf_avgTemp10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 593.79
## 2 330 457.92 24 135.86 4.0795 0.000000002534 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kbuf_t20_spline_d,kbuf_t20_spline_10d)## Analysis of Variance Table
##
## Model 1: kbuf_avgTemp20 ~ ns(c(1:366), df = 364)
## Model 2: kbuf_avgTemp20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.078
## 2 330 204.694 -329 -204.62 7.9786 0.2765anova(kbuf_t20_spline_d,kbuf_t20_spline_m)## Analysis of Variance Table
##
## Model 1: kbuf_avgTemp20 ~ ns(c(1:366), df = 364)
## Model 2: kbuf_avgTemp20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.078
## 2 354 271.886 -353 -271.81 9.8781 0.2495anova(kbuf_t20_spline_m,kbuf_t20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kbuf_avgTemp20 ~ ns(c(1:366), df = 11)
## Model 2: kbuf_avgTemp20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 271.89
## 2 330 204.69 24 67.193 4.5136 0.0000000001089 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kbuf_t30_spline_d,kbuf_t30_spline_10d)## Analysis of Variance Table
##
## Model 1: kbuf_avgTemp30 ~ ns(c(1:366), df = 364)
## Model 2: kbuf_avgTemp30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.075
## 2 330 149.280 -329 -149.21 6.0701 0.3149anova(kbuf_t30_spline_d,kbuf_t30_spline_m)## Analysis of Variance Table
##
## Model 1: kbuf_avgTemp30 ~ ns(c(1:366), df = 364)
## Model 2: kbuf_avgTemp30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.075
## 2 354 199.541 -353 -199.47 7.5631 0.2836anova(kbuf_t30_spline_m,kbuf_t30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kbuf_avgTemp30 ~ ns(c(1:366), df = 11)
## Model 2: kbuf_avgTemp30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 199.54
## 2 330 149.28 24 50.261 4.6295 0.00000000004699 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Dewpoint Temperature
anova(kbuf_dp10_spline_d,kbuf_dp10_spline_10d) ## Analysis of Variance Table
##
## Model 1: kbuf_avgDPTemp10 ~ ns(c(1:366), df = 364)
## Model 2: kbuf_avgDPTemp10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.09
## 2 330 565.13 -329 -565.05 19.944 0.177anova(kbuf_dp10_spline_d,kbuf_dp10_spline_m)## Analysis of Variance Table
##
## Model 1: kbuf_avgDPTemp10 ~ ns(c(1:366), df = 364)
## Model 2: kbuf_avgDPTemp10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.09
## 2 354 730.42 -353 -730.33 24.026 0.1615anova(kbuf_dp10_spline_m,kbuf_dp10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kbuf_avgDPTemp10 ~ ns(c(1:366), df = 11)
## Model 2: kbuf_avgDPTemp10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 730.42
## 2 330 565.13 24 165.29 4.0215 0.000000003854 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kbuf_dp20_spline_d,kbuf_dp20_spline_10d)## Analysis of Variance Table
##
## Model 1: kbuf_avgDPTemp20 ~ ns(c(1:366), df = 364)
## Model 2: kbuf_avgDPTemp20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.003
## 2 330 263.665 -329 -263.66 274.36 0.0481 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kbuf_dp20_spline_d,kbuf_dp20_spline_m)## Analysis of Variance Table
##
## Model 1: kbuf_avgDPTemp20 ~ ns(c(1:366), df = 364)
## Model 2: kbuf_avgDPTemp20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.00
## 2 354 339.33 -353 -339.33 329.09 0.04393 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kbuf_dp20_spline_m,kbuf_dp20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kbuf_avgDPTemp20 ~ ns(c(1:366), df = 11)
## Model 2: kbuf_avgDPTemp20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 339.33
## 2 330 263.67 24 75.667 3.946 0.000000006656 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kbuf_dp30_spline_d,kbuf_dp30_spline_10d)## Analysis of Variance Table
##
## Model 1: kbuf_avgDPTemp30 ~ ns(c(1:366), df = 364)
## Model 2: kbuf_avgDPTemp30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.00
## 2 330 179.72 -329 -179.72 1592.4 0.01998 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kbuf_dp30_spline_d,kbuf_dp30_spline_m)## Analysis of Variance Table
##
## Model 1: kbuf_avgDPTemp30 ~ ns(c(1:366), df = 364)
## Model 2: kbuf_avgDPTemp30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.00
## 2 354 225.79 -353 -225.79 1864.6 0.01846 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kbuf_dp30_spline_m,kbuf_dp30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kbuf_avgDPTemp30 ~ ns(c(1:366), df = 11)
## Model 2: kbuf_avgDPTemp30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 225.79
## 2 330 179.72 24 46.074 3.525 0.0000001378 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Wind Direction
anova(kbuf_wd10_spline_d,kbuf_wd10_spline_10d)## Analysis of Variance Table
##
## Model 1: kbuf_avgWDIR10 ~ ns(c(1:366), df = 364)
## Model 2: kbuf_avgWDIR10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 329
## 2 330 120757 -329 -120428 1.1135 0.656anova(kbuf_wd10_spline_d,kbuf_wd10_spline_m)## Analysis of Variance Table
##
## Model 1: kbuf_avgWDIR10 ~ ns(c(1:366), df = 364)
## Model 2: kbuf_avgWDIR10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 329
## 2 354 136012 -353 -135683 1.1693 0.6443anova(kbuf_wd10_spline_m,kbuf_wd10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kbuf_avgWDIR10 ~ ns(c(1:366), df = 11)
## Model 2: kbuf_avgWDIR10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 136012
## 2 330 120757 24 15255 1.737 0.01867 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kbuf_wd20_spline_d,kbuf_wd20_spline_10d)## Analysis of Variance Table
##
## Model 1: kbuf_avgWDIR20 ~ ns(c(1:366), df = 364)
## Model 2: kbuf_avgWDIR20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 168
## 2 330 63466 -329 -63298 1.1479 0.6487anova(kbuf_wd20_spline_d,kbuf_wd20_spline_m)## Analysis of Variance Table
##
## Model 1: kbuf_avgWDIR20 ~ ns(c(1:366), df = 364)
## Model 2: kbuf_avgWDIR20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 168
## 2 354 68984 -353 -68816 1.1631 0.6456anova(kbuf_wd20_spline_m,kbuf_wd20_spline_10d)## Analysis of Variance Table
##
## Model 1: kbuf_avgWDIR20 ~ ns(c(1:366), df = 11)
## Model 2: kbuf_avgWDIR20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 68984
## 2 330 63466 24 5518.5 1.1956 0.2427anova(kbuf_wd30_spline_d,kbuf_wd30_spline_10d)## Analysis of Variance Table
##
## Model 1: kbuf_avgWDIR30 ~ ns(c(1:366), df = 364)
## Model 2: kbuf_avgWDIR30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 75
## 2 330 41211 -329 -41136 1.6598 0.5618anova(kbuf_wd30_spline_d,kbuf_wd30_spline_m)## Analysis of Variance Table
##
## Model 1: kbuf_avgWDIR30 ~ ns(c(1:366), df = 364)
## Model 2: kbuf_avgWDIR30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 75
## 2 354 45615 -353 -45540 1.7126 0.5547anova(kbuf_wd30_spline_m,kbuf_wd30_spline_10d)## Analysis of Variance Table
##
## Model 1: kbuf_avgWDIR30 ~ ns(c(1:366), df = 11)
## Model 2: kbuf_avgWDIR30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 45615
## 2 330 41211 24 4404.3 1.4695 0.07442 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Wind Speed
anova(kbuf_ws10_spline_d,kbuf_ws10_spline_10d)## Analysis of Variance Table
##
## Model 1: kbuf_avgWSPD10 ~ ns(c(1:366), df = 364)
## Model 2: kbuf_avgWSPD10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.204
## 2 330 96.743 -329 -96.539 1.4377 0.5951anova(kbuf_ws10_spline_d,kbuf_ws10_spline_m)## Analysis of Variance Table
##
## Model 1: kbuf_avgWSPD10 ~ ns(c(1:366), df = 364)
## Model 2: kbuf_avgWSPD10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.204
## 2 354 113.296 -353 -113.09 1.5697 0.5747anova(kbuf_ws10_spline_m,kbuf_ws10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kbuf_avgWSPD10 ~ ns(c(1:366), df = 11)
## Model 2: kbuf_avgWSPD10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 113.296
## 2 330 96.743 24 16.552 2.3526 0.0004499 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kbuf_ws20_spline_d,kbuf_ws20_spline_10d)## Analysis of Variance Table
##
## Model 1: kbuf_avgWSPD20 ~ ns(c(1:366), df = 364)
## Model 2: kbuf_avgWSPD20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.010
## 2 330 57.544 -329 -57.534 16.913 0.192anova(kbuf_ws20_spline_d,kbuf_ws20_spline_m)## Analysis of Variance Table
##
## Model 1: kbuf_avgWSPD20 ~ ns(c(1:366), df = 364)
## Model 2: kbuf_avgWSPD20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.010
## 2 354 64.868 -353 -64.858 17.77 0.1874anova(kbuf_ws20_spline_m,kbuf_ws20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kbuf_avgWSPD20 ~ ns(c(1:366), df = 11)
## Model 2: kbuf_avgWSPD20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 64.868
## 2 330 57.544 24 7.3242 1.7501 0.01737 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kbuf_ws30_spline_d,kbuf_ws30_spline_10d)## Analysis of Variance Table
##
## Model 1: kbuf_avgWSPD30 ~ ns(c(1:366), df = 364)
## Model 2: kbuf_avgWSPD30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.003
## 2 330 41.011 -329 -41.008 40.126 0.1253anova(kbuf_ws30_spline_d,kbuf_ws30_spline_m)## Analysis of Variance Table
##
## Model 1: kbuf_avgWSPD30 ~ ns(c(1:366), df = 364)
## Model 2: kbuf_avgWSPD30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.003
## 2 354 44.466 -353 -44.463 40.549 0.1247anova(kbuf_ws30_spline_m,kbuf_ws30_spline_10d)## Analysis of Variance Table
##
## Model 1: kbuf_avgWSPD30 ~ ns(c(1:366), df = 11)
## Model 2: kbuf_avgWSPD30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 44.466
## 2 330 41.011 24 3.4547 1.1583 0.2787Wind Gust Speed
anova(kbuf_wg10_spline_d,kbuf_wg10_spline_10d)## Analysis of Variance Table
##
## Model 1: kbuf_avgWGSP10 ~ ns(c(1:366), df = 364)
## Model 2: kbuf_avgWGSP10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.002
## 2 330 72.616 -329 -72.614 112.67 0.075 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kbuf_wg10_spline_d,kbuf_wg10_spline_m)## Analysis of Variance Table
##
## Model 1: kbuf_avgWGSP10 ~ ns(c(1:366), df = 364)
## Model 2: kbuf_avgWGSP10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.002
## 2 354 99.098 -353 -99.096 143.31 0.06652 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kbuf_wg10_spline_m,kbuf_wg10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kbuf_avgWGSP10 ~ ns(c(1:366), df = 11)
## Model 2: kbuf_avgWGSP10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 99.098
## 2 330 72.616 24 26.482 5.0145 0.000000000002892 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kbuf_wg20_spline_d,kbuf_wg20_spline_10d)## Analysis of Variance Table
##
## Model 1: kbuf_avgWGSP20 ~ ns(c(1:366), df = 364)
## Model 2: kbuf_avgWGSP20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.004
## 2 330 47.238 -329 -47.233 32.615 0.1389anova(kbuf_wg20_spline_d,kbuf_wg20_spline_m)## Analysis of Variance Table
##
## Model 1: kbuf_avgWGSP20 ~ ns(c(1:366), df = 364)
## Model 2: kbuf_avgWGSP20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.004
## 2 354 58.717 -353 -58.712 37.785 0.1291anova(kbuf_wg20_spline_m,kbuf_wg20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kbuf_avgWGSP20 ~ ns(c(1:366), df = 11)
## Model 2: kbuf_avgWGSP20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 58.717
## 2 330 47.238 24 11.479 3.3413 0.0000005111 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kbuf_wg30_spline_d,kbuf_wg30_spline_10d)## Analysis of Variance Table
##
## Model 1: kbuf_avgWGSP30 ~ ns(c(1:366), df = 364)
## Model 2: kbuf_avgWGSP30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.001
## 2 330 170.760 -329 -170.76 683.61 0.03049 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kbuf_wg30_spline_d,kbuf_wg30_spline_m)## Analysis of Variance Table
##
## Model 1: kbuf_avgWGSP30 ~ ns(c(1:366), df = 364)
## Model 2: kbuf_avgWGSP30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.001
## 2 354 187.877 -353 -187.88 701 0.03011 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kbuf_wg30_spline_m,kbuf_wg30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kbuf_avgWGSP30 ~ ns(c(1:366), df = 11)
## Model 2: kbuf_avgWGSP30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 187.88
## 2 330 170.76 24 17.117 1.3783 0.1137Average Cloud Ceiling Height
anova(kbuf_cig10_spline_d,kbuf_cig10_spline_10d)## Analysis of Variance Table
##
## Model 1: kbuf_avgCCIG10 ~ ns(c(1:366), df = 364)
## Model 2: kbuf_avgCCIG10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 1436079
## 2 330 1187782003 -329 -1186345924 2.5109 0.4716anova(kbuf_cig10_spline_d,kbuf_cig10_spline_m)## Analysis of Variance Table
##
## Model 1: kbuf_avgCCIG10 ~ ns(c(1:366), df = 364)
## Model 2: kbuf_avgCCIG10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 1436079
## 2 354 1432129474 -353 -1430693395 2.8222 0.4479anova(kbuf_cig10_spline_m,kbuf_cig10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kbuf_avgCCIG10 ~ ns(c(1:366), df = 11)
## Model 2: kbuf_avgCCIG10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 1432129474
## 2 330 1187782003 24 244347471 2.8286 0.00001855 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kbuf_cig20_spline_d,kbuf_cig20_spline_10d)## Analysis of Variance Table
##
## Model 1: kbuf_avgCCIG20 ~ ns(c(1:366), df = 364)
## Model 2: kbuf_avgCCIG20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 1877528
## 2 330 638104040 -329 -636226512 1.03 0.6748anova(kbuf_cig20_spline_d,kbuf_cig20_spline_m)## Analysis of Variance Table
##
## Model 1: kbuf_avgCCIG20 ~ ns(c(1:366), df = 364)
## Model 2: kbuf_avgCCIG20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 1877528
## 2 354 832619904 -353 -830742376 1.2534 0.6276anova(kbuf_cig20_spline_m,kbuf_cig20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kbuf_avgCCIG20 ~ ns(c(1:366), df = 11)
## Model 2: kbuf_avgCCIG20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 832619904
## 2 330 638104040 24 194515864 4.1915 0.000000001126 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kbuf_cig30_spline_d,kbuf_cig30_spline_10d)## Analysis of Variance Table
##
## Model 1: kbuf_avgCCIG30 ~ ns(c(1:366), df = 364)
## Model 2: kbuf_avgCCIG30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 1106937
## 2 330 402829795 -329 -401722859 1.1031 0.6583anova(kbuf_cig30_spline_d,kbuf_cig30_spline_m)## Analysis of Variance Table
##
## Model 1: kbuf_avgCCIG30 ~ ns(c(1:366), df = 364)
## Model 2: kbuf_avgCCIG30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 1106937
## 2 354 522369222 -353 -521262285 1.334 0.6128anova(kbuf_cig30_spline_m,kbuf_cig30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kbuf_avgCCIG30 ~ ns(c(1:366), df = 11)
## Model 2: kbuf_avgCCIG30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 522369222
## 2 330 402829795 24 119539427 4.0803 0.000000002519 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Average Visibility
anova(kbuf_vis10_spline_d,kbuf_vis10_spline_10d)## Analysis of Variance Table
##
## Model 1: kbuf_avgVIS10 ~ ns(c(1:366), df = 364)
## Model 2: kbuf_avgVIS10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 16338
## 2 330 346007157 -329 -345990818 64.367 0.09912 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kbuf_vis10_spline_d,kbuf_vis10_spline_m)## Analysis of Variance Table
##
## Model 1: kbuf_avgVIS10 ~ ns(c(1:366), df = 364)
## Model 2: kbuf_avgVIS10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 16338
## 2 354 365048167 -353 -365031829 63.292 0.09996 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kbuf_vis10_spline_m,kbuf_vis10_spline_10d)## Analysis of Variance Table
##
## Model 1: kbuf_avgVIS10 ~ ns(c(1:366), df = 11)
## Model 2: kbuf_avgVIS10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 365048167
## 2 330 346007157 24 19041011 0.7567 0.7902anova(kbuf_vis20_spline_d,kbuf_vis20_spline_10d)## Analysis of Variance Table
##
## Model 1: kbuf_avgVIS20 ~ ns(c(1:366), df = 364)
## Model 2: kbuf_avgVIS20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 98898
## 2 330 156421675 -329 -156322777 4.8044 0.3515anova(kbuf_vis20_spline_d,kbuf_vis20_spline_m)## Analysis of Variance Table
##
## Model 1: kbuf_avgVIS20 ~ ns(c(1:366), df = 364)
## Model 2: kbuf_avgVIS20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 98898
## 2 354 174084804 -353 -173985907 4.9837 0.3455anova(kbuf_vis20_spline_m,kbuf_vis20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kbuf_avgVIS20 ~ ns(c(1:366), df = 11)
## Model 2: kbuf_avgVIS20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 174084804
## 2 330 156421675 24 17663129 1.5526 0.04944 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kbuf_vis30_spline_d,kbuf_vis30_spline_10d)## Analysis of Variance Table
##
## Model 1: kbuf_avgVIS30 ~ ns(c(1:366), df = 364)
## Model 2: kbuf_avgVIS30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0
## 2 330 157074200 -329 -157074200 3371568 0.0004342 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kbuf_vis30_spline_d,kbuf_vis30_spline_m)## Analysis of Variance Table
##
## Model 1: kbuf_avgVIS30 ~ ns(c(1:366), df = 364)
## Model 2: kbuf_avgVIS30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0
## 2 354 179538354 -353 -179538354 3591745 0.0004207 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kbuf_vis30_spline_m,kbuf_vis30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kbuf_avgVIS30 ~ ns(c(1:366), df = 11)
## Model 2: kbuf_avgVIS30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 179538354
## 2 330 157074200 24 22464153 1.9665 0.005004 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Average Cloud Cover
anova(kbuf_cc10_spline_d,kbuf_cc10_spline_10d)## Analysis of Variance Table
##
## Model 1: kbuf_avgCCOV10 ~ ns(c(1:366), df = 364)
## Model 2: kbuf_avgCCOV10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.014
## 2 330 106.591 -329 -106.58 22.917 0.1653anova(kbuf_cc10_spline_d,kbuf_cc10_spline_m)## Analysis of Variance Table
##
## Model 1: kbuf_avgCCOV10 ~ ns(c(1:366), df = 364)
## Model 2: kbuf_avgCCOV10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.014
## 2 354 125.208 -353 -125.19 25.09 0.1581anova(kbuf_cc10_spline_m,kbuf_cc10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kbuf_avgCCOV10 ~ ns(c(1:366), df = 11)
## Model 2: kbuf_avgCCOV10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 125.21
## 2 330 106.59 24 18.617 2.4015 0.0003272 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kbuf_cc20_spline_d,kbuf_cc20_spline_10d)## Analysis of Variance Table
##
## Model 1: kbuf_avgCCOV20 ~ ns(c(1:366), df = 364)
## Model 2: kbuf_avgCCOV20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.105
## 2 330 55.741 -329 -55.636 1.6087 0.569anova(kbuf_cc20_spline_d,kbuf_cc20_spline_m)## Analysis of Variance Table
##
## Model 1: kbuf_avgCCOV20 ~ ns(c(1:366), df = 364)
## Model 2: kbuf_avgCCOV20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.105
## 2 354 72.722 -353 -72.617 1.957 0.5248anova(kbuf_cc20_spline_m,kbuf_cc20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kbuf_avgCCOV20 ~ ns(c(1:366), df = 11)
## Model 2: kbuf_avgCCOV20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 72.722
## 2 330 55.741 24 16.981 4.1888 0.000000001147 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kbuf_cc30_spline_d,kbuf_cc30_spline_10d)## Analysis of Variance Table
##
## Model 1: kbuf_avgCCOV30 ~ ns(c(1:366), df = 364)
## Model 2: kbuf_avgCCOV30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.055
## 2 330 35.057 -329 -35.002 1.9453 0.5261anova(kbuf_cc30_spline_d,kbuf_cc30_spline_m)## Analysis of Variance Table
##
## Model 1: kbuf_avgCCOV30 ~ ns(c(1:366), df = 364)
## Model 2: kbuf_avgCCOV30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.055
## 2 354 45.948 -353 -45.893 2.3771 0.483anova(kbuf_cc30_spline_m,kbuf_cc30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kbuf_avgCCOV30 ~ ns(c(1:366), df = 11)
## Model 2: kbuf_avgCCOV30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 45.948
## 2 330 35.057 24 10.891 4.2715 0.0000000006299 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Average Altimeter Setting
anova(kbuf_alt10_spline_d,kbuf_alt10_spline_10d)## Analysis of Variance Table
##
## Model 1: kbuf_avgALTS10 ~ ns(c(1:366), df = 364)
## Model 2: kbuf_avgALTS10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.02
## 2 330 1413.79 -329 -1413.8 273.61 0.04817 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kbuf_alt10_spline_d,kbuf_alt10_spline_m)## Analysis of Variance Table
##
## Model 1: kbuf_avgALTS10 ~ ns(c(1:366), df = 364)
## Model 2: kbuf_avgALTS10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.02
## 2 354 1859.35 -353 -1859.3 335.38 0.04352 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kbuf_alt10_spline_m,kbuf_alt10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kbuf_avgALTS10 ~ ns(c(1:366), df = 11)
## Model 2: kbuf_avgALTS10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 1859.3
## 2 330 1413.8 24 445.57 4.3334 0.0000000004021 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kbuf_alt20_spline_d,kbuf_alt20_spline_10d)## Analysis of Variance Table
##
## Model 1: kbuf_avgALTS20 ~ ns(c(1:366), df = 364)
## Model 2: kbuf_avgALTS20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.24
## 2 330 713.60 -329 -713.36 9.1848 0.2584anova(kbuf_alt20_spline_d,kbuf_alt20_spline_m)## Analysis of Variance Table
##
## Model 1: kbuf_avgALTS20 ~ ns(c(1:366), df = 364)
## Model 2: kbuf_avgALTS20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.24
## 2 354 1121.58 -353 -1121.3 13.456 0.2147anova(kbuf_alt20_spline_m,kbuf_alt20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kbuf_avgALTS20 ~ ns(c(1:366), df = 11)
## Model 2: kbuf_avgALTS20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 1121.6
## 2 330 713.6 24 407.99 7.8613 < 0.00000000000000022 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kbuf_alt30_spline_d,kbuf_alt30_spline_10d)## Analysis of Variance Table
##
## Model 1: kbuf_avgALTS30 ~ ns(c(1:366), df = 364)
## Model 2: kbuf_avgALTS30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.12
## 2 330 469.34 -329 -469.21 11.531 0.2314anova(kbuf_alt30_spline_d,kbuf_alt30_spline_m)## Analysis of Variance Table
##
## Model 1: kbuf_avgALTS30 ~ ns(c(1:366), df = 364)
## Model 2: kbuf_avgALTS30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.12
## 2 354 658.41 -353 -658.28 15.078 0.2031anova(kbuf_alt30_spline_m,kbuf_alt30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kbuf_avgALTS30 ~ ns(c(1:366), df = 11)
## Model 2: kbuf_avgALTS30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 658.41
## 2 330 469.34 24 189.07 5.5391 0.00000000000006614 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1KFOE
Temperature
anova(kfoe_t10_spline_d,kfoe_t10_spline_10d)## Analysis of Variance Table
##
## Model 1: kfoe_avgTemp10 ~ ns(c(1:366), df = 364)
## Model 2: kfoe_avgTemp10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.01
## 2 330 748.80 -329 -748.79 221.63 0.05351 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kfoe_t10_spline_d,kfoe_t10_spline_m)## Analysis of Variance Table
##
## Model 1: kfoe_avgTemp10 ~ ns(c(1:366), df = 364)
## Model 2: kfoe_avgTemp10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.01
## 2 354 1328.99 -353 -1329 366.62 0.04162 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kfoe_t10_spline_m,kfoe_t10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kfoe_avgTemp10 ~ ns(c(1:366), df = 11)
## Model 2: kfoe_avgTemp10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 1329.0
## 2 330 748.8 24 580.18 10.654 < 0.00000000000000022 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kfoe_t20_spline_d,kfoe_t20_spline_10d)## Analysis of Variance Table
##
## Model 1: kfoe_avgTemp20 ~ ns(c(1:366), df = 364)
## Model 2: kfoe_avgTemp20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.09
## 2 330 329.04 -329 -328.95 11.331 0.2334anova(kfoe_t20_spline_d,kfoe_t20_spline_m)## Analysis of Variance Table
##
## Model 1: kfoe_avgTemp20 ~ ns(c(1:366), df = 364)
## Model 2: kfoe_avgTemp20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.09
## 2 354 602.53 -353 -602.45 19.341 0.1797anova(kfoe_t20_spline_m,kfoe_t20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kfoe_avgTemp20 ~ ns(c(1:366), df = 11)
## Model 2: kfoe_avgTemp20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 602.53
## 2 330 329.04 24 273.5 11.429 < 0.00000000000000022 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kfoe_t30_spline_d,kfoe_t30_spline_10d)## Analysis of Variance Table
##
## Model 1: kfoe_avgTemp30 ~ ns(c(1:366), df = 364)
## Model 2: kfoe_avgTemp30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.024
## 2 330 227.966 -329 -227.94 28.857 0.1476anova(kfoe_t30_spline_d,kfoe_t30_spline_m)## Analysis of Variance Table
##
## Model 1: kfoe_avgTemp30 ~ ns(c(1:366), df = 364)
## Model 2: kfoe_avgTemp30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.02
## 2 354 395.47 -353 -395.45 46.66 0.1163anova(kfoe_t30_spline_m,kfoe_t30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kfoe_avgTemp30 ~ ns(c(1:366), df = 11)
## Model 2: kfoe_avgTemp30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 395.47
## 2 330 227.97 24 167.51 10.104 < 0.00000000000000022 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Dewpoint Temperature
anova(kfoe_dp10_spline_d,kfoe_dp10_spline_10d) ## Analysis of Variance Table
##
## Model 1: kfoe_avgDPTemp10 ~ ns(c(1:366), df = 364)
## Model 2: kfoe_avgDPTemp10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.04
## 2 330 827.05 -329 -827.01 67.733 0.09664 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kfoe_dp10_spline_d,kfoe_dp10_spline_m)## Analysis of Variance Table
##
## Model 1: kfoe_avgDPTemp10 ~ ns(c(1:366), df = 364)
## Model 2: kfoe_avgDPTemp10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.04
## 2 354 1374.65 -353 -1374.6 104.93 0.07771 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kfoe_dp10_spline_m,kfoe_dp10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kfoe_avgDPTemp10 ~ ns(c(1:366), df = 11)
## Model 2: kfoe_avgDPTemp10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 1374.65
## 2 330 827.05 24 547.6 9.1041 < 0.00000000000000022 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kfoe_dp20_spline_d,kfoe_dp20_spline_10d)## Analysis of Variance Table
##
## Model 1: kfoe_avgDPTemp20 ~ ns(c(1:366), df = 364)
## Model 2: kfoe_avgDPTemp20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.00
## 2 330 367.11 -329 -367.11 98077 0.002546 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kfoe_dp20_spline_d,kfoe_dp20_spline_m)## Analysis of Variance Table
##
## Model 1: kfoe_avgDPTemp20 ~ ns(c(1:366), df = 364)
## Model 2: kfoe_avgDPTemp20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.00
## 2 354 633.05 -353 -633.05 157624 0.002008 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kfoe_dp20_spline_m,kfoe_dp20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kfoe_avgDPTemp20 ~ ns(c(1:366), df = 11)
## Model 2: kfoe_avgDPTemp20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 633.05
## 2 330 367.11 24 265.93 9.9602 < 0.00000000000000022 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kfoe_dp30_spline_d,kfoe_dp30_spline_10d)## Analysis of Variance Table
##
## Model 1: kfoe_avgDPTemp30 ~ ns(c(1:366), df = 364)
## Model 2: kfoe_avgDPTemp30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.00
## 2 330 240.25 -329 -240.25 2470.2 0.01604 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kfoe_dp30_spline_d,kfoe_dp30_spline_m)## Analysis of Variance Table
##
## Model 1: kfoe_avgDPTemp30 ~ ns(c(1:366), df = 364)
## Model 2: kfoe_avgDPTemp30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.00
## 2 354 406.93 -353 -406.93 3899.4 0.01277 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kfoe_dp30_spline_m,kfoe_dp30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kfoe_avgDPTemp30 ~ ns(c(1:366), df = 11)
## Model 2: kfoe_avgDPTemp30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 406.93
## 2 330 240.25 24 166.68 9.5392 < 0.00000000000000022 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Wind Direction
anova(kfoe_wd10_spline_d,kfoe_wd10_spline_10d)## Analysis of Variance Table
##
## Model 1: kfoe_avgWDIR10 ~ ns(c(1:366), df = 364)
## Model 2: kfoe_avgWDIR10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 9
## 2 330 156056 -329 -156047 54.87 0.1073anova(kfoe_wd10_spline_d,kfoe_wd10_spline_m)## Analysis of Variance Table
##
## Model 1: kfoe_avgWDIR10 ~ ns(c(1:366), df = 364)
## Model 2: kfoe_avgWDIR10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 9
## 2 354 174586 -353 -174577 57.212 0.1051anova(kfoe_wd10_spline_m,kfoe_wd10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kfoe_avgWDIR10 ~ ns(c(1:366), df = 11)
## Model 2: kfoe_avgWDIR10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 174586
## 2 330 156056 24 18530 1.6326 0.03275 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kfoe_wd20_spline_d,kfoe_wd20_spline_10d)## Analysis of Variance Table
##
## Model 1: kfoe_avgWDIR20 ~ ns(c(1:366), df = 364)
## Model 2: kfoe_avgWDIR20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0
## 2 330 74493 -329 -74493 796.45 0.02824 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kfoe_wd20_spline_d,kfoe_wd20_spline_m)## Analysis of Variance Table
##
## Model 1: kfoe_avgWDIR20 ~ ns(c(1:366), df = 364)
## Model 2: kfoe_avgWDIR20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0
## 2 354 83109 -353 -83109 828.16 0.0277 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kfoe_wd20_spline_m,kfoe_wd20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kfoe_avgWDIR20 ~ ns(c(1:366), df = 11)
## Model 2: kfoe_avgWDIR20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 83109
## 2 330 74493 24 8616.2 1.5904 0.0408 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kfoe_wd30_spline_d,kfoe_wd30_spline_10d)## Analysis of Variance Table
##
## Model 1: kfoe_avgWDIR30 ~ ns(c(1:366), df = 364)
## Model 2: kfoe_avgWDIR30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 24
## 2 330 49388 -329 -49363 6.1673 0.3126anova(kfoe_wd30_spline_d,kfoe_wd30_spline_m)## Analysis of Variance Table
##
## Model 1: kfoe_avgWDIR30 ~ ns(c(1:366), df = 364)
## Model 2: kfoe_avgWDIR30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 24
## 2 354 55798 -353 -55773 6.4943 0.305anova(kfoe_wd30_spline_m,kfoe_wd30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kfoe_avgWDIR30 ~ ns(c(1:366), df = 11)
## Model 2: kfoe_avgWDIR30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 55798
## 2 330 49388 24 6409.7 1.7845 0.01434 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Wind Speed
anova(kfoe_ws10_spline_d,kfoe_ws10_spline_10d)## Analysis of Variance Table
##
## Model 1: kfoe_avgWSPD10 ~ ns(c(1:366), df = 364)
## Model 2: kfoe_avgWSPD10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.073
## 2 330 116.057 -329 -115.98 4.8232 0.3508anova(kfoe_ws10_spline_d,kfoe_ws10_spline_m)## Analysis of Variance Table
##
## Model 1: kfoe_avgWSPD10 ~ ns(c(1:366), df = 364)
## Model 2: kfoe_avgWSPD10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.073
## 2 354 131.911 -353 -131.84 5.1097 0.3415anova(kfoe_ws10_spline_m,kfoe_ws10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kfoe_avgWSPD10 ~ ns(c(1:366), df = 11)
## Model 2: kfoe_avgWSPD10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 131.91
## 2 330 116.06 24 15.854 1.8783 0.0084 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kfoe_ws20_spline_d,kfoe_ws20_spline_10d)## Analysis of Variance Table
##
## Model 1: kfoe_avgWSPD20 ~ ns(c(1:366), df = 364)
## Model 2: kfoe_avgWSPD20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.029
## 2 330 61.467 -329 -61.437 6.3304 0.3087anova(kfoe_ws20_spline_d,kfoe_ws20_spline_m)## Analysis of Variance Table
##
## Model 1: kfoe_avgWSPD20 ~ ns(c(1:366), df = 364)
## Model 2: kfoe_avgWSPD20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.029
## 2 354 67.050 -353 -67.02 6.4362 0.3063anova(kfoe_ws20_spline_m,kfoe_ws20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kfoe_avgWSPD20 ~ ns(c(1:366), df = 11)
## Model 2: kfoe_avgWSPD20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 67.050
## 2 330 61.467 24 5.5829 1.2489 0.1972anova(kfoe_ws30_spline_d,kfoe_ws30_spline_10d)## Analysis of Variance Table
##
## Model 1: kfoe_avgWSPD30 ~ ns(c(1:366), df = 364)
## Model 2: kfoe_avgWSPD30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.018
## 2 330 39.056 -329 -39.038 6.6776 0.301anova(kfoe_ws30_spline_d,kfoe_ws30_spline_m)## Analysis of Variance Table
##
## Model 1: kfoe_avgWSPD30 ~ ns(c(1:366), df = 364)
## Model 2: kfoe_avgWSPD30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.018
## 2 354 45.361 -353 -45.343 7.2288 0.2898anova(kfoe_ws30_spline_m,kfoe_ws30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kfoe_avgWSPD30 ~ ns(c(1:366), df = 11)
## Model 2: kfoe_avgWSPD30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 45.361
## 2 330 39.056 24 6.3052 2.2198 0.001053 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Wind Gust Speed
anova(kfoe_wg10_spline_d,kfoe_wg10_spline_10d)## Analysis of Variance Table
##
## Model 1: kfoe_avgWGSP10 ~ ns(c(1:366), df = 364)
## Model 2: kfoe_avgWGSP10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.071
## 2 330 107.026 -329 -106.95 4.5954 0.3588anova(kfoe_wg10_spline_d,kfoe_wg10_spline_m)## Analysis of Variance Table
##
## Model 1: kfoe_avgWGSP10 ~ ns(c(1:366), df = 364)
## Model 2: kfoe_avgWGSP10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.071
## 2 354 135.051 -353 -134.98 5.4052 0.3326anova(kfoe_wg10_spline_m,kfoe_wg10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kfoe_avgWGSP10 ~ ns(c(1:366), df = 11)
## Model 2: kfoe_avgWGSP10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 135.05
## 2 330 107.03 24 28.025 3.6005 0.00000008023 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kfoe_wg20_spline_d,kfoe_wg20_spline_10d)## Analysis of Variance Table
##
## Model 1: kfoe_avgWGSP20 ~ ns(c(1:366), df = 364)
## Model 2: kfoe_avgWGSP20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.033
## 2 330 51.512 -329 -51.479 4.7852 0.3521anova(kfoe_wg20_spline_d,kfoe_wg20_spline_m)## Analysis of Variance Table
##
## Model 1: kfoe_avgWGSP20 ~ ns(c(1:366), df = 364)
## Model 2: kfoe_avgWGSP20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.033
## 2 354 71.312 -353 -71.279 6.1753 0.3124anova(kfoe_wg20_spline_m,kfoe_wg20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kfoe_avgWGSP20 ~ ns(c(1:366), df = 11)
## Model 2: kfoe_avgWGSP20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 71.312
## 2 330 51.512 24 19.801 5.2854 0.0000000000004096 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kfoe_wg30_spline_d,kfoe_wg30_spline_10d)## Analysis of Variance Table
##
## Model 1: kfoe_avgWGSP30 ~ ns(c(1:366), df = 364)
## Model 2: kfoe_avgWGSP30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.012
## 2 330 33.305 -329 -33.293 8.5226 0.2678anova(kfoe_wg30_spline_d,kfoe_wg30_spline_m)## Analysis of Variance Table
##
## Model 1: kfoe_avgWGSP30 ~ ns(c(1:366), df = 364)
## Model 2: kfoe_avgWGSP30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.012
## 2 354 45.964 -353 -45.952 10.963 0.2372anova(kfoe_wg30_spline_m,kfoe_wg30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kfoe_avgWGSP30 ~ ns(c(1:366), df = 11)
## Model 2: kfoe_avgWGSP30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 45.964
## 2 330 33.305 24 12.659 5.2264 0.0000000000006265 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Average Cloud Ceiling Height
anova(kfoe_cig10_spline_d,kfoe_cig10_spline_10d)## Analysis of Variance Table
##
## Model 1: kfoe_avgCCIG10 ~ ns(c(1:366), df = 364)
## Model 2: kfoe_avgCCIG10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 46048
## 2 330 1877304540 -329 -1877258492 123.91 0.07153 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kfoe_cig10_spline_d,kfoe_cig10_spline_m)## Analysis of Variance Table
##
## Model 1: kfoe_avgCCIG10 ~ ns(c(1:366), df = 364)
## Model 2: kfoe_avgCCIG10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 46048
## 2 354 2497910307 -353 -2497864260 153.67 0.06425 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kfoe_cig10_spline_m,kfoe_cig10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kfoe_avgCCIG10 ~ ns(c(1:366), df = 11)
## Model 2: kfoe_avgCCIG10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 2497910307
## 2 330 1877304540 24 620605768 4.5455 0.00000000008637 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kfoe_cig20_spline_d,kfoe_cig20_spline_10d)## Analysis of Variance Table
##
## Model 1: kfoe_avgCCIG20 ~ ns(c(1:366), df = 364)
## Model 2: kfoe_avgCCIG20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 153091
## 2 330 973370958 -329 -973217867 19.323 0.1798anova(kfoe_cig20_spline_d,kfoe_cig20_spline_m)## Analysis of Variance Table
##
## Model 1: kfoe_avgCCIG20 ~ ns(c(1:366), df = 364)
## Model 2: kfoe_avgCCIG20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 153091
## 2 354 1355119367 -353 -1354966276 25.073 0.1582anova(kfoe_cig20_spline_m,kfoe_cig20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kfoe_avgCCIG20 ~ ns(c(1:366), df = 11)
## Model 2: kfoe_avgCCIG20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 1355119367
## 2 330 973370958 24 381748409 5.3926 0.0000000000001893 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kfoe_cig30_spline_d,kfoe_cig30_spline_10d)## Analysis of Variance Table
##
## Model 1: kfoe_avgCCIG30 ~ ns(c(1:366), df = 364)
## Model 2: kfoe_avgCCIG30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 5936
## 2 330 590994953 -329 -590989016 302.59 0.04581 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kfoe_cig30_spline_d,kfoe_cig30_spline_m)## Analysis of Variance Table
##
## Model 1: kfoe_avgCCIG30 ~ ns(c(1:366), df = 364)
## Model 2: kfoe_avgCCIG30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 5936
## 2 354 792827834 -353 -792821898 378.34 0.04097 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kfoe_cig30_spline_m,kfoe_cig30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kfoe_avgCCIG30 ~ ns(c(1:366), df = 11)
## Model 2: kfoe_avgCCIG30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 792827834
## 2 330 590994953 24 201832881 4.6958 0.00000000002905 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Average Visibility
anova(kfoe_vis10_spline_d,kfoe_vis10_spline_10d)## Analysis of Variance Table
##
## Model 1: kfoe_avgVIS10 ~ ns(c(1:366), df = 364)
## Model 2: kfoe_avgVIS10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 532362
## 2 330 336462902 -329 -335930539 1.918 0.5292anova(kfoe_vis10_spline_d,kfoe_vis10_spline_m)## Analysis of Variance Table
##
## Model 1: kfoe_avgVIS10 ~ ns(c(1:366), df = 364)
## Model 2: kfoe_avgVIS10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 532362
## 2 354 392904155 -353 -392371793 2.0879 0.5106anova(kfoe_vis10_spline_m,kfoe_vis10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kfoe_avgVIS10 ~ ns(c(1:366), df = 11)
## Model 2: kfoe_avgVIS10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 392904155
## 2 330 336462902 24 56441253 2.3065 0.0006057 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kfoe_vis20_spline_d,kfoe_vis20_spline_10d)## Analysis of Variance Table
##
## Model 1: kfoe_avgVIS20 ~ ns(c(1:366), df = 364)
## Model 2: kfoe_avgVIS20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 36141
## 2 330 182816631 -329 -182780490 15.372 0.2012anova(kfoe_vis20_spline_d,kfoe_vis20_spline_m)## Analysis of Variance Table
##
## Model 1: kfoe_avgVIS20 ~ ns(c(1:366), df = 364)
## Model 2: kfoe_avgVIS20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 36141
## 2 354 221040435 -353 -221004294 17.323 0.1897anova(kfoe_vis20_spline_m,kfoe_vis20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kfoe_avgVIS20 ~ ns(c(1:366), df = 11)
## Model 2: kfoe_avgVIS20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 221040435
## 2 330 182816631 24 38223804 2.8749 0.00001348 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kfoe_vis30_spline_d,kfoe_vis30_spline_10d)## Analysis of Variance Table
##
## Model 1: kfoe_avgVIS30 ~ ns(c(1:366), df = 364)
## Model 2: kfoe_avgVIS30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 48630
## 2 330 114690107 -329 -114641477 7.1655 0.291anova(kfoe_vis30_spline_d,kfoe_vis30_spline_m)## Analysis of Variance Table
##
## Model 1: kfoe_avgVIS30 ~ ns(c(1:366), df = 364)
## Model 2: kfoe_avgVIS30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 48630
## 2 354 147552633 -353 -147504003 8.5927 0.2668anova(kfoe_vis30_spline_m,kfoe_vis30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kfoe_avgVIS30 ~ ns(c(1:366), df = 11)
## Model 2: kfoe_avgVIS30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 147552633
## 2 330 114690107 24 32862526 3.9398 0.000000006959 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Average Cloud Cover
anova(kfoe_cc10_spline_d,kfoe_cc10_spline_10d)## Analysis of Variance Table
##
## Model 1: kfoe_avgCCOV10 ~ ns(c(1:366), df = 364)
## Model 2: kfoe_avgCCOV10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.346
## 2 330 111.891 -329 -111.55 0.9808 0.6866anova(kfoe_cc10_spline_d,kfoe_cc10_spline_m)## Analysis of Variance Table
##
## Model 1: kfoe_avgCCOV10 ~ ns(c(1:366), df = 364)
## Model 2: kfoe_avgCCOV10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.346
## 2 354 139.748 -353 -139.4 1.1424 0.6499anova(kfoe_cc10_spline_m,kfoe_cc10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kfoe_avgCCOV10 ~ ns(c(1:366), df = 11)
## Model 2: kfoe_avgCCOV10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 139.75
## 2 330 111.89 24 27.856 3.4232 0.0000002853 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kfoe_cc20_spline_d,kfoe_cc20_spline_10d)## Analysis of Variance Table
##
## Model 1: kfoe_avgCCOV20 ~ ns(c(1:366), df = 364)
## Model 2: kfoe_avgCCOV20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.002
## 2 330 102.396 -329 -102.39 162.4 0.0625 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kfoe_cc20_spline_d,kfoe_cc20_spline_m)## Analysis of Variance Table
##
## Model 1: kfoe_avgCCOV20 ~ ns(c(1:366), df = 364)
## Model 2: kfoe_avgCCOV20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.002
## 2 354 127.662 -353 -127.66 188.71 0.05799 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kfoe_cc20_spline_m,kfoe_cc20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kfoe_avgCCOV20 ~ ns(c(1:366), df = 11)
## Model 2: kfoe_avgCCOV20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 127.66
## 2 330 102.40 24 25.266 3.3928 0.0000003543 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kfoe_cc30_spline_d,kfoe_cc30_spline_10d)## Analysis of Variance Table
##
## Model 1: kfoe_avgCCOV30 ~ ns(c(1:366), df = 364)
## Model 2: kfoe_avgCCOV30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.028
## 2 330 65.743 -329 -65.715 7.066 0.293anova(kfoe_cc30_spline_d,kfoe_cc30_spline_m)## Analysis of Variance Table
##
## Model 1: kfoe_avgCCOV30 ~ ns(c(1:366), df = 364)
## Model 2: kfoe_avgCCOV30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.028
## 2 354 79.090 -353 -79.062 7.9231 0.2774anova(kfoe_cc30_spline_m,kfoe_cc30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kfoe_avgCCOV30 ~ ns(c(1:366), df = 11)
## Model 2: kfoe_avgCCOV30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 79.090
## 2 330 65.743 24 13.347 2.7914 0.00002394 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Average Altimeter Setting
anova(kfoe_alt10_spline_d,kfoe_alt10_spline_10d)## Analysis of Variance Table
##
## Model 1: kfoe_avgALTS10 ~ ns(c(1:366), df = 364)
## Model 2: kfoe_avgALTS10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.08
## 2 330 1575.90 -329 -1575.8 57.958 0.1044anova(kfoe_alt10_spline_d,kfoe_alt10_spline_m)## Analysis of Variance Table
##
## Model 1: kfoe_avgALTS10 ~ ns(c(1:366), df = 364)
## Model 2: kfoe_avgALTS10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.08
## 2 354 1971.85 -353 -1971.8 67.59 0.09674 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kfoe_alt10_spline_m,kfoe_alt10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kfoe_avgALTS10 ~ ns(c(1:366), df = 11)
## Model 2: kfoe_avgALTS10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 1971.8
## 2 330 1575.9 24 395.95 3.4547 0.0000002278 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kfoe_alt20_spline_d,kfoe_alt20_spline_10d)## Analysis of Variance Table
##
## Model 1: kfoe_avgALTS20 ~ ns(c(1:366), df = 364)
## Model 2: kfoe_avgALTS20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.01
## 2 330 662.86 -329 -662.85 178.57 0.05961 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kfoe_alt20_spline_d,kfoe_alt20_spline_m)## Analysis of Variance Table
##
## Model 1: kfoe_avgALTS20 ~ ns(c(1:366), df = 364)
## Model 2: kfoe_avgALTS20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.01
## 2 354 936.01 -353 -936 235.02 0.05197 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kfoe_alt20_spline_m,kfoe_alt20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kfoe_avgALTS20 ~ ns(c(1:366), df = 11)
## Model 2: kfoe_avgALTS20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 936.01
## 2 330 662.86 24 273.16 5.6662 0.0000000000000266 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kfoe_alt30_spline_d,kfoe_alt30_spline_10d)## Analysis of Variance Table
##
## Model 1: kfoe_avgALTS30 ~ ns(c(1:366), df = 364)
## Model 2: kfoe_avgALTS30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.02
## 2 330 419.23 -329 -419.21 72.992 0.09311 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kfoe_alt30_spline_d,kfoe_alt30_spline_m)## Analysis of Variance Table
##
## Model 1: kfoe_avgALTS30 ~ ns(c(1:366), df = 364)
## Model 2: kfoe_avgALTS30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.02
## 2 354 582.89 -353 -582.87 94.588 0.08184 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kfoe_alt30_spline_m,kfoe_alt30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kfoe_avgALTS30 ~ ns(c(1:366), df = 11)
## Model 2: kfoe_avgALTS30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 582.89
## 2 330 419.23 24 163.66 5.3678 0.0000000000002264 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1KMSN
Temperature
anova(kmsn_t10_spline_d,kmsn_t10_spline_10d)## Analysis of Variance Table
##
## Model 1: kmsn_avgTemp10 ~ ns(c(1:366), df = 364)
## Model 2: kmsn_avgTemp10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.03
## 2 330 557.98 -329 -557.95 60.123 0.1025anova(kmsn_t10_spline_d,kmsn_t10_spline_m)## Analysis of Variance Table
##
## Model 1: kmsn_avgTemp10 ~ ns(c(1:366), df = 364)
## Model 2: kmsn_avgTemp10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.03
## 2 354 969.15 -353 -969.12 97.331 0.08068 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kmsn_t10_spline_m,kmsn_t10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kmsn_avgTemp10 ~ ns(c(1:366), df = 11)
## Model 2: kmsn_avgTemp10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 969.15
## 2 330 557.98 24 411.17 10.132 < 0.00000000000000022 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kmsn_t20_spline_d,kmsn_t20_spline_10d)## Analysis of Variance Table
##
## Model 1: kmsn_avgTemp20 ~ ns(c(1:366), df = 364)
## Model 2: kmsn_avgTemp20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.006
## 2 330 222.215 -329 -222.21 111.24 0.07548 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kmsn_t20_spline_d,kmsn_t20_spline_m)## Analysis of Variance Table
##
## Model 1: kmsn_avgTemp20 ~ ns(c(1:366), df = 364)
## Model 2: kmsn_avgTemp20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.01
## 2 354 384.06 -353 -384.06 179.2 0.05951 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kmsn_t20_spline_m,kmsn_t20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kmsn_avgTemp20 ~ ns(c(1:366), df = 11)
## Model 2: kmsn_avgTemp20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 384.06
## 2 330 222.22 24 161.85 10.015 < 0.00000000000000022 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kmsn_t30_spline_d,kmsn_t30_spline_10d)## Analysis of Variance Table
##
## Model 1: kmsn_avgTemp30 ~ ns(c(1:366), df = 364)
## Model 2: kmsn_avgTemp30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.00
## 2 330 150.84 -329 -150.84 10276 0.007865 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kmsn_t30_spline_d,kmsn_t30_spline_m)## Analysis of Variance Table
##
## Model 1: kmsn_avgTemp30 ~ ns(c(1:366), df = 364)
## Model 2: kmsn_avgTemp30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.00
## 2 354 251.84 -353 -251.84 15989 0.006305 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kmsn_t30_spline_m,kmsn_t30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kmsn_avgTemp30 ~ ns(c(1:366), df = 11)
## Model 2: kmsn_avgTemp30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 251.84
## 2 330 150.84 24 101 9.2063 < 0.00000000000000022 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Dewpoint Temperature
anova(kmsn_dp10_spline_d,kmsn_dp10_spline_10d) ## Analysis of Variance Table
##
## Model 1: kmsn_avgDPTemp10 ~ ns(c(1:366), df = 364)
## Model 2: kmsn_avgDPTemp10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.02
## 2 330 741.80 -329 -741.78 114.21 0.07449 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kmsn_dp10_spline_d,kmsn_dp10_spline_m)## Analysis of Variance Table
##
## Model 1: kmsn_avgDPTemp10 ~ ns(c(1:366), df = 364)
## Model 2: kmsn_avgDPTemp10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.02
## 2 354 1259.35 -353 -1259.3 180.71 0.05926 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kmsn_dp10_spline_m,kmsn_dp10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kmsn_avgDPTemp10 ~ ns(c(1:366), df = 11)
## Model 2: kmsn_avgDPTemp10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 1259.3
## 2 330 741.8 24 517.55 9.5933 < 0.00000000000000022 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kmsn_dp20_spline_d,kmsn_dp20_spline_10d)## Analysis of Variance Table
##
## Model 1: kmsn_avgDPTemp20 ~ ns(c(1:366), df = 364)
## Model 2: kmsn_avgDPTemp20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.00
## 2 330 330.37 -329 -330.37 485.96 0.03615 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kmsn_dp20_spline_d,kmsn_dp20_spline_m)## Analysis of Variance Table
##
## Model 1: kmsn_avgDPTemp20 ~ ns(c(1:366), df = 364)
## Model 2: kmsn_avgDPTemp20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.00
## 2 354 516.99 -353 -516.98 708.77 0.02994 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kmsn_dp20_spline_m,kmsn_dp20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kmsn_avgDPTemp20 ~ ns(c(1:366), df = 11)
## Model 2: kmsn_avgDPTemp20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 516.99
## 2 330 330.37 24 186.62 7.7671 < 0.00000000000000022 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kmsn_dp30_spline_d,kmsn_dp30_spline_10d)## Analysis of Variance Table
##
## Model 1: kmsn_avgDPTemp30 ~ ns(c(1:366), df = 364)
## Model 2: kmsn_avgDPTemp30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.002
## 2 330 220.190 -329 -220.19 273.6 0.04817 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kmsn_dp30_spline_d,kmsn_dp30_spline_m)## Analysis of Variance Table
##
## Model 1: kmsn_avgDPTemp30 ~ ns(c(1:366), df = 364)
## Model 2: kmsn_avgDPTemp30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.0
## 2 354 346.7 -353 -346.7 401.51 0.03977 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kmsn_dp30_spline_m,kmsn_dp30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kmsn_avgDPTemp30 ~ ns(c(1:366), df = 11)
## Model 2: kmsn_avgDPTemp30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 346.70
## 2 330 220.19 24 126.51 7.9001 < 0.00000000000000022 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Wind Direction
anova(kmsn_wd10_spline_d,kmsn_wd10_spline_10d)## Analysis of Variance Table
##
## Model 1: kmsn_avgWDIR10 ~ ns(c(1:366), df = 364)
## Model 2: kmsn_avgWDIR10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 786
## 2 330 156965 -329 -156179 0.6041 0.8009anova(kmsn_wd10_spline_d,kmsn_wd10_spline_m)## Analysis of Variance Table
##
## Model 1: kmsn_avgWDIR10 ~ ns(c(1:366), df = 364)
## Model 2: kmsn_avgWDIR10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 786
## 2 354 187044 -353 -186258 0.6714 0.7769anova(kmsn_wd10_spline_m,kmsn_wd10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kmsn_avgWDIR10 ~ ns(c(1:366), df = 11)
## Model 2: kmsn_avgWDIR10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 187044
## 2 330 156965 24 30079 2.6349 0.0000694 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kmsn_wd20_spline_d,kmsn_wd20_spline_10d)## Analysis of Variance Table
##
## Model 1: kmsn_avgWDIR20 ~ ns(c(1:366), df = 364)
## Model 2: kmsn_avgWDIR20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 182
## 2 330 89063 -329 -88882 1.4885 0.587anova(kmsn_wd20_spline_d,kmsn_wd20_spline_m)## Analysis of Variance Table
##
## Model 1: kmsn_avgWDIR20 ~ ns(c(1:366), df = 364)
## Model 2: kmsn_avgWDIR20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 182
## 2 354 104032 -353 -103851 1.6209 0.5673anova(kmsn_wd20_spline_m,kmsn_wd20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kmsn_avgWDIR20 ~ ns(c(1:366), df = 11)
## Model 2: kmsn_avgWDIR20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 104032
## 2 330 89063 24 14969 2.311 0.0005885 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kmsn_wd30_spline_d,kmsn_wd30_spline_10d)## Analysis of Variance Table
##
## Model 1: kmsn_avgWDIR30 ~ ns(c(1:366), df = 364)
## Model 2: kmsn_avgWDIR30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 48
## 2 330 61434 -329 -61387 3.9211 0.3861anova(kmsn_wd30_spline_d,kmsn_wd30_spline_m)## Analysis of Variance Table
##
## Model 1: kmsn_avgWDIR30 ~ ns(c(1:366), df = 364)
## Model 2: kmsn_avgWDIR30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 48
## 2 354 73502 -353 -73454 4.3729 0.3672anova(kmsn_wd30_spline_m,kmsn_wd30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kmsn_avgWDIR30 ~ ns(c(1:366), df = 11)
## Model 2: kmsn_avgWDIR30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 73502
## 2 330 61434 24 12068 2.7009 0.00004441 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Wind Speed
anova(kmsn_ws10_spline_d,kmsn_ws10_spline_10d)## Analysis of Variance Table
##
## Model 1: kmsn_avgWSPD10 ~ ns(c(1:366), df = 364)
## Model 2: kmsn_avgWSPD10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.005
## 2 330 73.419 -329 -73.413 43.468 0.1205anova(kmsn_ws10_spline_d,kmsn_ws10_spline_m)## Analysis of Variance Table
##
## Model 1: kmsn_avgWSPD10 ~ ns(c(1:366), df = 364)
## Model 2: kmsn_avgWSPD10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.005
## 2 354 80.428 -353 -80.423 44.381 0.1192anova(kmsn_ws10_spline_m,kmsn_ws10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kmsn_avgWSPD10 ~ ns(c(1:366), df = 11)
## Model 2: kmsn_avgWSPD10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 80.428
## 2 330 73.419 24 7.0095 1.3128 0.1515anova(kmsn_ws20_spline_d,kmsn_ws20_spline_10d)## Analysis of Variance Table
##
## Model 1: kmsn_avgWSPD20 ~ ns(c(1:366), df = 364)
## Model 2: kmsn_avgWSPD20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.002
## 2 330 39.579 -329 -39.577 68.053 0.09641 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kmsn_ws20_spline_d,kmsn_ws20_spline_m)## Analysis of Variance Table
##
## Model 1: kmsn_avgWSPD20 ~ ns(c(1:366), df = 364)
## Model 2: kmsn_avgWSPD20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.002
## 2 354 43.521 -353 -43.519 69.743 0.09525 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kmsn_ws20_spline_m,kmsn_ws20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kmsn_avgWSPD20 ~ ns(c(1:366), df = 11)
## Model 2: kmsn_avgWSPD20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 43.521
## 2 330 39.579 24 3.9416 1.3693 0.1184anova(kmsn_ws30_spline_d,kmsn_ws30_spline_10d)## Analysis of Variance Table
##
## Model 1: kmsn_avgWSPD30 ~ ns(c(1:366), df = 364)
## Model 2: kmsn_avgWSPD30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.0073
## 2 330 26.1282 -329 -26.121 10.94 0.2374anova(kmsn_ws30_spline_d,kmsn_ws30_spline_m)## Analysis of Variance Table
##
## Model 1: kmsn_avgWSPD30 ~ ns(c(1:366), df = 364)
## Model 2: kmsn_avgWSPD30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.0073
## 2 354 28.5698 -353 -28.562 11.149 0.2353anova(kmsn_ws30_spline_m,kmsn_ws30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kmsn_avgWSPD30 ~ ns(c(1:366), df = 11)
## Model 2: kmsn_avgWSPD30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 28.570
## 2 330 26.128 24 2.4415 1.2849 0.1703Wind Gust Speed
anova(kmsn_wg10_spline_d,kmsn_wg10_spline_10d)## Analysis of Variance Table
##
## Model 1: kmsn_avgWGSP10 ~ ns(c(1:366), df = 364)
## Model 2: kmsn_avgWGSP10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.017
## 2 330 66.855 -329 -66.838 12.253 0.2247anova(kmsn_wg10_spline_d,kmsn_wg10_spline_m)## Analysis of Variance Table
##
## Model 1: kmsn_avgWGSP10 ~ ns(c(1:366), df = 364)
## Model 2: kmsn_avgWGSP10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.017
## 2 354 78.878 -353 -78.861 13.474 0.2145anova(kmsn_wg10_spline_m,kmsn_wg10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kmsn_avgWGSP10 ~ ns(c(1:366), df = 11)
## Model 2: kmsn_avgWGSP10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 78.878
## 2 330 66.855 24 12.023 2.4728 0.0002049 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kmsn_wg20_spline_d,kmsn_wg20_spline_10d)## Analysis of Variance Table
##
## Model 1: kmsn_avgWGSP20 ~ ns(c(1:366), df = 364)
## Model 2: kmsn_avgWGSP20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.024
## 2 330 44.530 -329 -44.505 5.5582 0.3283anova(kmsn_wg20_spline_d,kmsn_wg20_spline_m)## Analysis of Variance Table
##
## Model 1: kmsn_avgWGSP20 ~ ns(c(1:366), df = 364)
## Model 2: kmsn_avgWGSP20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.024
## 2 354 52.304 -353 -52.28 6.0853 0.3146anova(kmsn_wg20_spline_m,kmsn_wg20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kmsn_avgWGSP20 ~ ns(c(1:366), df = 11)
## Model 2: kmsn_avgWGSP20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 52.304
## 2 330 44.530 24 7.7744 2.4006 0.0003291 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kmsn_wg30_spline_d,kmsn_wg30_spline_10d)## Analysis of Variance Table
##
## Model 1: kmsn_avgWGSP30 ~ ns(c(1:366), df = 364)
## Model 2: kmsn_avgWGSP30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.066
## 2 330 35.200 -329 -35.134 1.613 0.5684anova(kmsn_wg30_spline_d,kmsn_wg30_spline_m)## Analysis of Variance Table
##
## Model 1: kmsn_avgWGSP30 ~ ns(c(1:366), df = 364)
## Model 2: kmsn_avgWGSP30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.066
## 2 354 41.886 -353 -41.819 1.7893 0.5448anova(kmsn_wg30_spline_m,kmsn_wg30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kmsn_avgWGSP30 ~ ns(c(1:366), df = 11)
## Model 2: kmsn_avgWGSP30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 41.886
## 2 330 35.200 24 6.6853 2.6114 0.00008131 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Average Cloud Ceiling Height
anova(kmsn_cig10_spline_d,kmsn_cig10_spline_10d)## Analysis of Variance Table
##
## Model 1: kmsn_avgCCIG10 ~ ns(c(1:366), df = 364)
## Model 2: kmsn_avgCCIG10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 51027
## 2 330 1540322389 -329 -1540271361 91.749 0.08308 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kmsn_cig10_spline_d,kmsn_cig10_spline_m)## Analysis of Variance Table
##
## Model 1: kmsn_avgCCIG10 ~ ns(c(1:366), df = 364)
## Model 2: kmsn_avgCCIG10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 51027
## 2 354 1765358595 -353 -1765307568 98.004 0.0804 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kmsn_cig10_spline_m,kmsn_cig10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kmsn_avgCCIG10 ~ ns(c(1:366), df = 11)
## Model 2: kmsn_avgCCIG10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 1765358595
## 2 330 1540322389 24 225036206 2.0088 0.003882 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kmsn_cig20_spline_d,kmsn_cig20_spline_10d)## Analysis of Variance Table
##
## Model 1: kmsn_avgCCIG20 ~ ns(c(1:366), df = 364)
## Model 2: kmsn_avgCCIG20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 391460
## 2 330 907817411 -329 -907425951 7.0458 0.2934anova(kmsn_cig20_spline_d,kmsn_cig20_spline_m)## Analysis of Variance Table
##
## Model 1: kmsn_avgCCIG20 ~ ns(c(1:366), df = 364)
## Model 2: kmsn_avgCCIG20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 391460
## 2 354 1017370504 -353 -1016979044 7.3595 0.2874anova(kmsn_cig20_spline_m,kmsn_cig20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kmsn_avgCCIG20 ~ ns(c(1:366), df = 11)
## Model 2: kmsn_avgCCIG20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 1017370504
## 2 330 907817411 24 109553093 1.6593 0.02844 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kmsn_cig30_spline_d,kmsn_cig30_spline_10d)## Analysis of Variance Table
##
## Model 1: kmsn_avgCCIG30 ~ ns(c(1:366), df = 364)
## Model 2: kmsn_avgCCIG30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 1128832
## 2 330 600991249 -329 -599862417 1.6152 0.5681anova(kmsn_cig30_spline_d,kmsn_cig30_spline_m)## Analysis of Variance Table
##
## Model 1: kmsn_avgCCIG30 ~ ns(c(1:366), df = 364)
## Model 2: kmsn_avgCCIG30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 1128832
## 2 354 681588263 -353 -680459432 1.7076 0.5554anova(kmsn_cig30_spline_m,kmsn_cig30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kmsn_avgCCIG30 ~ ns(c(1:366), df = 11)
## Model 2: kmsn_avgCCIG30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 681588263
## 2 330 600991249 24 80597014 1.844 0.01024 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Average Visibility
anova(kmsn_vis10_spline_d,kmsn_vis10_spline_10d)## Analysis of Variance Table
##
## Model 1: kmsn_avgVIS10 ~ ns(c(1:366), df = 364)
## Model 2: kmsn_avgVIS10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 333462
## 2 330 355851782 -329 -355518321 3.2406 0.4211anova(kmsn_vis10_spline_d,kmsn_vis10_spline_m)## Analysis of Variance Table
##
## Model 1: kmsn_avgVIS10 ~ ns(c(1:366), df = 364)
## Model 2: kmsn_avgVIS10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 333462
## 2 354 403330486 -353 -402997025 3.4236 0.4108anova(kmsn_vis10_spline_m,kmsn_vis10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kmsn_avgVIS10 ~ ns(c(1:366), df = 11)
## Model 2: kmsn_avgVIS10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 403330486
## 2 330 355851782 24 47478704 1.8346 0.0108 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kmsn_vis20_spline_d,kmsn_vis20_spline_10d)## Analysis of Variance Table
##
## Model 1: kmsn_avgVIS20 ~ ns(c(1:366), df = 364)
## Model 2: kmsn_avgVIS20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 1
## 2 330 230565582 -329 -230565581 602108 0.001027 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kmsn_vis20_spline_d,kmsn_vis20_spline_m) # this one## Analysis of Variance Table
##
## Model 1: kmsn_avgVIS20 ~ ns(c(1:366), df = 364)
## Model 2: kmsn_avgVIS20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 1
## 2 354 255648860 -353 -255648858 622221 0.001011 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kmsn_vis20_spline_m,kmsn_vis20_spline_10d) ## Analysis of Variance Table
##
## Model 1: kmsn_avgVIS20 ~ ns(c(1:366), df = 11)
## Model 2: kmsn_avgVIS20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 255648860
## 2 330 230565582 24 25083278 1.4959 0.06552 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kmsn_vis30_spline_d,kmsn_vis30_spline_10d)## Analysis of Variance Table
##
## Model 1: kmsn_avgVIS30 ~ ns(c(1:366), df = 364)
## Model 2: kmsn_avgVIS30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 3331
## 2 330 167332782 -329 -167329450 152.67 0.06445 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kmsn_vis30_spline_d,kmsn_vis30_spline_m)## Analysis of Variance Table
##
## Model 1: kmsn_avgVIS30 ~ ns(c(1:366), df = 364)
## Model 2: kmsn_avgVIS30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 3331
## 2 354 186119394 -353 -186116063 158.27 0.06331 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kmsn_vis30_spline_m,kmsn_vis30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kmsn_avgVIS30 ~ ns(c(1:366), df = 11)
## Model 2: kmsn_avgVIS30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 186119394
## 2 330 167332782 24 18786612 1.5437 0.05171 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Average Cloud Cover
anova(kmsn_cc10_spline_d,kmsn_cc10_spline_10d)## Analysis of Variance Table
##
## Model 1: kmsn_avgCCOV10 ~ ns(c(1:366), df = 364)
## Model 2: kmsn_avgCCOV10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.234
## 2 330 173.487 -329 -173.25 2.2501 0.4945anova(kmsn_cc10_spline_d,kmsn_cc10_spline_m)## Analysis of Variance Table
##
## Model 1: kmsn_avgCCOV10 ~ ns(c(1:366), df = 364)
## Model 2: kmsn_avgCCOV10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.234
## 2 354 196.913 -353 -196.68 2.3807 0.4827anova(kmsn_cc10_spline_m,kmsn_cc10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kmsn_avgCCOV10 ~ ns(c(1:366), df = 11)
## Model 2: kmsn_avgCCOV10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 196.91
## 2 330 173.49 24 23.426 1.8567 0.009518 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kmsn_cc20_spline_d,kmsn_cc20_spline_10d)## Analysis of Variance Table
##
## Model 1: kmsn_avgCCOV20 ~ ns(c(1:366), df = 364)
## Model 2: kmsn_avgCCOV20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.241
## 2 330 108.634 -329 -108.39 1.3676 0.6069anova(kmsn_cc20_spline_d,kmsn_cc20_spline_m)## Analysis of Variance Table
##
## Model 1: kmsn_avgCCOV20 ~ ns(c(1:366), df = 364)
## Model 2: kmsn_avgCCOV20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.241
## 2 354 121.422 -353 -121.18 1.425 0.5972anova(kmsn_cc20_spline_m,kmsn_cc20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kmsn_avgCCOV20 ~ ns(c(1:366), df = 11)
## Model 2: kmsn_avgCCOV20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 121.42
## 2 330 108.63 24 12.788 1.6186 0.03524 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kmsn_cc30_spline_d,kmsn_cc30_spline_10d)## Analysis of Variance Table
##
## Model 1: kmsn_avgCCOV30 ~ ns(c(1:366), df = 364)
## Model 2: kmsn_avgCCOV30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.318
## 2 330 73.262 -329 -72.944 0.6969 0.7682anova(kmsn_cc30_spline_d,kmsn_cc30_spline_m)## Analysis of Variance Table
##
## Model 1: kmsn_avgCCOV30 ~ ns(c(1:366), df = 364)
## Model 2: kmsn_avgCCOV30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.318
## 2 354 83.664 -353 -83.346 0.7422 0.7535anova(kmsn_cc30_spline_m,kmsn_cc30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kmsn_avgCCOV30 ~ ns(c(1:366), df = 11)
## Model 2: kmsn_avgCCOV30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 83.664
## 2 330 73.262 24 10.402 1.9522 0.005445 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Average Altimeter Setting
anova(kmsn_alt10_spline_d,kmsn_alt10_spline_10d)## Analysis of Variance Table
##
## Model 1: kmsn_avgALTS10 ~ ns(c(1:366), df = 364)
## Model 2: kmsn_avgALTS10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.0
## 2 330 1598.1 -329 -1598.1 1460.1 0.02086 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kmsn_alt10_spline_d,kmsn_alt10_spline_m)## Analysis of Variance Table
##
## Model 1: kmsn_avgALTS10 ~ ns(c(1:366), df = 364)
## Model 2: kmsn_avgALTS10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.0
## 2 354 2102.4 -353 -2102.4 1790.2 0.01884 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kmsn_alt10_spline_m,kmsn_alt10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kmsn_avgALTS10 ~ ns(c(1:366), df = 11)
## Model 2: kmsn_avgALTS10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 2102.4
## 2 330 1598.1 24 504.28 4.3388 0.0000000003869 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kmsn_alt20_spline_d,kmsn_alt20_spline_10d)## Analysis of Variance Table
##
## Model 1: kmsn_avgALTS20 ~ ns(c(1:366), df = 364)
## Model 2: kmsn_avgALTS20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.02
## 2 330 744.68 -329 -744.66 143.52 0.06647 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kmsn_alt20_spline_d,kmsn_alt20_spline_m)## Analysis of Variance Table
##
## Model 1: kmsn_avgALTS20 ~ ns(c(1:366), df = 364)
## Model 2: kmsn_avgALTS20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.02
## 2 354 1113.96 -353 -1114 200.1 0.05632 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kmsn_alt20_spline_m,kmsn_alt20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kmsn_avgALTS20 ~ ns(c(1:366), df = 11)
## Model 2: kmsn_avgALTS20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 1113.96
## 2 330 744.68 24 369.29 6.8186 < 0.00000000000000022 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kmsn_alt30_spline_d,kmsn_alt30_spline_10d)## Analysis of Variance Table
##
## Model 1: kmsn_avgALTS30 ~ ns(c(1:366), df = 364)
## Model 2: kmsn_avgALTS30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.08
## 2 330 447.60 -329 -447.52 16.413 0.1948anova(kmsn_alt30_spline_d,kmsn_alt30_spline_m)## Analysis of Variance Table
##
## Model 1: kmsn_avgALTS30 ~ ns(c(1:366), df = 364)
## Model 2: kmsn_avgALTS30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.08
## 2 354 659.07 -353 -658.99 22.525 0.1668anova(kmsn_alt30_spline_m,kmsn_alt30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kmsn_avgALTS30 ~ ns(c(1:366), df = 11)
## Model 2: kmsn_avgALTS30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 659.07
## 2 330 447.60 24 211.47 6.4962 < 0.00000000000000022 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1KTRI
Temperature
anova(ktri_t10_spline_d,ktri_t10_spline_10d)## Analysis of Variance Table
##
## Model 1: ktri_avgTemp10 ~ ns(c(1:366), df = 364)
## Model 2: ktri_avgTemp10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.14
## 2 330 502.09 -329 -501.95 10.561 0.2415anova(ktri_t10_spline_d,ktri_t10_spline_m)## Analysis of Variance Table
##
## Model 1: ktri_avgTemp10 ~ ns(c(1:366), df = 364)
## Model 2: ktri_avgTemp10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.14
## 2 354 604.69 -353 -604.54 11.855 0.2284anova(ktri_t10_spline_m,ktri_t10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: ktri_avgTemp10 ~ ns(c(1:366), df = 11)
## Model 2: ktri_avgTemp10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 604.69
## 2 330 502.09 24 102.6 2.8097 0.00002113 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ktri_t20_spline_d,ktri_t20_spline_10d)## Analysis of Variance Table
##
## Model 1: ktri_avgTemp20 ~ ns(c(1:366), df = 364)
## Model 2: ktri_avgTemp20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.002
## 2 330 188.420 -329 -188.42 267.96 0.04868 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ktri_t20_spline_d,ktri_t20_spline_m)## Analysis of Variance Table
##
## Model 1: ktri_avgTemp20 ~ ns(c(1:366), df = 364)
## Model 2: ktri_avgTemp20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.002
## 2 354 218.421 -353 -218.42 289.5 0.04683 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ktri_t20_spline_m,ktri_t20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: ktri_avgTemp20 ~ ns(c(1:366), df = 11)
## Model 2: ktri_avgTemp20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 218.42
## 2 330 188.42 24 30 2.1893 0.001277 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ktri_t30_spline_d,ktri_t30_spline_10d)## Analysis of Variance Table
##
## Model 1: ktri_avgTemp30 ~ ns(c(1:366), df = 364)
## Model 2: ktri_avgTemp30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.003
## 2 330 111.714 -329 -111.71 120.77 0.07245 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ktri_t30_spline_d,ktri_t30_spline_m)## Analysis of Variance Table
##
## Model 1: ktri_avgTemp30 ~ ns(c(1:366), df = 364)
## Model 2: ktri_avgTemp30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.003
## 2 354 148.377 -353 -148.37 149.5 0.06514 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ktri_t30_spline_m,ktri_t30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: ktri_avgTemp30 ~ ns(c(1:366), df = 11)
## Model 2: ktri_avgTemp30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 148.38
## 2 330 111.71 24 36.662 4.5124 0.0000000001098 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Dewpoint Temperature
anova(ktri_dp10_spline_d,ktri_dp10_spline_10d) ## Analysis of Variance Table
##
## Model 1: ktri_avgDPTemp10 ~ ns(c(1:366), df = 364)
## Model 2: ktri_avgDPTemp10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.04
## 2 330 817.44 -329 -817.4 65.141 0.09853 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ktri_dp10_spline_d,ktri_dp10_spline_m)## Analysis of Variance Table
##
## Model 1: ktri_avgDPTemp10 ~ ns(c(1:366), df = 364)
## Model 2: ktri_avgDPTemp10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.04
## 2 354 1070.99 -353 -1071 79.545 0.08921 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ktri_dp10_spline_m,ktri_dp10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: ktri_avgDPTemp10 ~ ns(c(1:366), df = 11)
## Model 2: ktri_avgDPTemp10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 1070.99
## 2 330 817.44 24 253.55 4.2649 0.0000000006609 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ktri_dp20_spline_d,ktri_dp20_spline_10d)## Analysis of Variance Table
##
## Model 1: ktri_avgDPTemp20 ~ ns(c(1:366), df = 364)
## Model 2: ktri_avgDPTemp20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.02
## 2 330 332.12 -329 -332.11 58.038 0.1044anova(ktri_dp20_spline_d,ktri_dp20_spline_m)## Analysis of Variance Table
##
## Model 1: ktri_avgDPTemp20 ~ ns(c(1:366), df = 364)
## Model 2: ktri_avgDPTemp20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.02
## 2 354 396.14 -353 -396.12 64.518 0.09901 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ktri_dp20_spline_m,ktri_dp20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: ktri_avgDPTemp20 ~ ns(c(1:366), df = 11)
## Model 2: ktri_avgDPTemp20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 396.14
## 2 330 332.12 24 64.013 2.6502 0.00006262 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ktri_dp30_spline_d,ktri_dp30_spline_10d)## Analysis of Variance Table
##
## Model 1: ktri_avgDPTemp30 ~ ns(c(1:366), df = 364)
## Model 2: ktri_avgDPTemp30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.00
## 2 330 200.05 -329 -200.05 54909 0.003402 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ktri_dp30_spline_d,ktri_dp30_spline_m)## Analysis of Variance Table
##
## Model 1: ktri_avgDPTemp30 ~ ns(c(1:366), df = 364)
## Model 2: ktri_avgDPTemp30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.00
## 2 354 264.17 -353 -264.17 67579 0.003067 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ktri_dp30_spline_m,ktri_dp30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: ktri_avgDPTemp30 ~ ns(c(1:366), df = 11)
## Model 2: ktri_avgDPTemp30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 264.17
## 2 330 200.05 24 64.121 4.4073 0.0000000002354 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Wind Direction
anova(ktri_wd10_spline_d,ktri_wd10_spline_10d)## Analysis of Variance Table
##
## Model 1: ktri_avgWDIR10 ~ ns(c(1:366), df = 364)
## Model 2: ktri_avgWDIR10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 947
## 2 330 141096 -329 -140150 0.45 0.863anova(ktri_wd10_spline_d,ktri_wd10_spline_m)## Analysis of Variance Table
##
## Model 1: ktri_avgWDIR10 ~ ns(c(1:366), df = 364)
## Model 2: ktri_avgWDIR10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 947
## 2 354 172016 -353 -171070 0.5119 0.8369anova(ktri_wd10_spline_m,ktri_wd10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: ktri_avgWDIR10 ~ ns(c(1:366), df = 11)
## Model 2: ktri_avgWDIR10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 172016
## 2 330 141096 24 30920 3.0132 0.000005163 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ktri_wd20_spline_d,ktri_wd20_spline_10d)## Analysis of Variance Table
##
## Model 1: ktri_avgWDIR20 ~ ns(c(1:366), df = 364)
## Model 2: ktri_avgWDIR20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 269
## 2 330 71919 -329 -71650 0.81 0.7327anova(ktri_wd20_spline_d,ktri_wd20_spline_m)## Analysis of Variance Table
##
## Model 1: ktri_avgWDIR20 ~ ns(c(1:366), df = 364)
## Model 2: ktri_avgWDIR20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 269
## 2 354 88768 -353 -88499 0.9324 0.6989anova(ktri_wd20_spline_m,ktri_wd20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: ktri_avgWDIR20 ~ ns(c(1:366), df = 11)
## Model 2: ktri_avgWDIR20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 88768
## 2 330 71919 24 16850 3.2215 0.000001195 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ktri_wd30_spline_d,ktri_wd30_spline_10d)## Analysis of Variance Table
##
## Model 1: ktri_avgWDIR30 ~ ns(c(1:366), df = 364)
## Model 2: ktri_avgWDIR30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 201
## 2 330 44341 -329 -44140 0.6684 0.7778anova(ktri_wd30_spline_d,ktri_wd30_spline_m)## Analysis of Variance Table
##
## Model 1: ktri_avgWDIR30 ~ ns(c(1:366), df = 364)
## Model 2: ktri_avgWDIR30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 201
## 2 354 55388 -353 -55188 0.7789 0.742anova(ktri_wd30_spline_m,ktri_wd30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: ktri_avgWDIR30 ~ ns(c(1:366), df = 11)
## Model 2: ktri_avgWDIR30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 55388
## 2 330 44341 24 11047 3.4258 0.0000002801 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Wind Speed
anova(ktri_ws10_spline_d,ktri_ws10_spline_10d)## Analysis of Variance Table
##
## Model 1: ktri_avgWSPD10 ~ ns(c(1:366), df = 364)
## Model 2: ktri_avgWSPD10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.018
## 2 330 61.370 -329 -61.351 10.082 0.247anova(ktri_ws10_spline_d,ktri_ws10_spline_m)## Analysis of Variance Table
##
## Model 1: ktri_avgWSPD10 ~ ns(c(1:366), df = 364)
## Model 2: ktri_avgWSPD10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.018
## 2 354 66.202 -353 -66.183 10.136 0.2464anova(ktri_ws10_spline_m,ktri_ws10_spline_10d)## Analysis of Variance Table
##
## Model 1: ktri_avgWSPD10 ~ ns(c(1:366), df = 11)
## Model 2: ktri_avgWSPD10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 66.202
## 2 330 61.370 24 4.832 1.0826 0.3617anova(ktri_ws20_spline_d,ktri_ws20_spline_10d)## Analysis of Variance Table
##
## Model 1: ktri_avgWSPD20 ~ ns(c(1:366), df = 364)
## Model 2: ktri_avgWSPD20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.000
## 2 330 28.781 -329 -28.781 2056.7 0.01758 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ktri_ws20_spline_d,ktri_ws20_spline_m)## Analysis of Variance Table
##
## Model 1: ktri_avgWSPD20 ~ ns(c(1:366), df = 364)
## Model 2: ktri_avgWSPD20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.000
## 2 354 32.547 -353 -32.547 2167.7 0.01712 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ktri_ws20_spline_m,ktri_ws20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: ktri_avgWSPD20 ~ ns(c(1:366), df = 11)
## Model 2: ktri_avgWSPD20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 32.547
## 2 330 28.781 24 3.766 1.7992 0.01321 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ktri_ws30_spline_d,ktri_ws30_spline_10d)## Analysis of Variance Table
##
## Model 1: ktri_avgWSPD30 ~ ns(c(1:366), df = 364)
## Model 2: ktri_avgWSPD30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.0043
## 2 330 20.1525 -329 -20.148 14.234 0.2089anova(ktri_ws30_spline_d,ktri_ws30_spline_m)## Analysis of Variance Table
##
## Model 1: ktri_avgWSPD30 ~ ns(c(1:366), df = 364)
## Model 2: ktri_avgWSPD30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.0043
## 2 354 22.4841 -353 -22.48 14.801 0.2049anova(ktri_ws30_spline_m,ktri_ws30_spline_10d)## Analysis of Variance Table
##
## Model 1: ktri_avgWSPD30 ~ ns(c(1:366), df = 11)
## Model 2: ktri_avgWSPD30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 22.484
## 2 330 20.152 24 2.3316 1.5909 0.0407 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Wind Gust Speed
anova(ktri_wg10_spline_d,ktri_wg10_spline_10d)## Analysis of Variance Table
##
## Model 1: ktri_avgWGSP10 ~ ns(c(1:366), df = 364)
## Model 2: ktri_avgWGSP10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.077
## 2 330 59.823 -329 -59.747 2.3705 0.4835anova(ktri_wg10_spline_d,ktri_wg10_spline_m)## Analysis of Variance Table
##
## Model 1: ktri_avgWGSP10 ~ ns(c(1:366), df = 364)
## Model 2: ktri_avgWGSP10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.077
## 2 354 82.898 -353 -82.821 3.0626 0.4319anova(ktri_wg10_spline_m,ktri_wg10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: ktri_avgWGSP10 ~ ns(c(1:366), df = 11)
## Model 2: ktri_avgWGSP10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 82.898
## 2 330 59.823 24 23.075 5.3036 0.0000000000003594 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ktri_wg20_spline_d,ktri_wg20_spline_10d)## Analysis of Variance Table
##
## Model 1: ktri_avgWGSP20 ~ ns(c(1:366), df = 364)
## Model 2: ktri_avgWGSP20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.007
## 2 330 32.605 -329 -32.598 13.486 0.2144anova(ktri_wg20_spline_d,ktri_wg20_spline_m)## Analysis of Variance Table
##
## Model 1: ktri_avgWGSP20 ~ ns(c(1:366), df = 364)
## Model 2: ktri_avgWGSP20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.007
## 2 354 41.849 -353 -41.842 16.133 0.1965anova(ktri_wg20_spline_m,ktri_wg20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: ktri_avgWGSP20 ~ ns(c(1:366), df = 11)
## Model 2: ktri_avgWGSP20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 41.849
## 2 330 32.605 24 9.2441 3.8983 0.000000009392 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ktri_wg30_spline_d,ktri_wg30_spline_10d)## Analysis of Variance Table
##
## Model 1: ktri_avgWGSP30 ~ ns(c(1:366), df = 364)
## Model 2: ktri_avgWGSP30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.0018
## 2 330 21.2684 -329 -21.267 35.802 0.1326anova(ktri_wg30_spline_d,ktri_wg30_spline_m)## Analysis of Variance Table
##
## Model 1: ktri_avgWGSP30 ~ ns(c(1:366), df = 364)
## Model 2: ktri_avgWGSP30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.002
## 2 354 32.353 -353 -32.351 50.76 0.1115anova(ktri_wg30_spline_m,ktri_wg30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: ktri_avgWGSP30 ~ ns(c(1:366), df = 11)
## Model 2: ktri_avgWGSP30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 32.353
## 2 330 21.268 24 11.085 7.1662 < 0.00000000000000022 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Average Cloud Ceiling Height
anova(ktri_cig10_spline_d,ktri_cig10_spline_10d)## Analysis of Variance Table
##
## Model 1: ktri_avgCCIG10 ~ ns(c(1:366), df = 364)
## Model 2: ktri_avgCCIG10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 92359
## 2 330 1528182149 -329 -1528089789 50.289 0.1121anova(ktri_cig10_spline_d,ktri_cig10_spline_m)## Analysis of Variance Table
##
## Model 1: ktri_avgCCIG10 ~ ns(c(1:366), df = 364)
## Model 2: ktri_avgCCIG10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 92359
## 2 354 1793911493 -353 -1793819134 55.02 0.1072anova(ktri_cig10_spline_m,ktri_cig10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: ktri_avgCCIG10 ~ ns(c(1:366), df = 11)
## Model 2: ktri_avgCCIG10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 1793911493
## 2 330 1528182149 24 265729344 2.3909 0.0003506 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ktri_cig20_spline_d,ktri_cig20_spline_10d)## Analysis of Variance Table
##
## Model 1: ktri_avgCCIG20 ~ ns(c(1:366), df = 364)
## Model 2: ktri_avgCCIG20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 131362
## 2 330 813732310 -329 -813600948 18.826 0.1821anova(ktri_cig20_spline_d,ktri_cig20_spline_m)## Analysis of Variance Table
##
## Model 1: ktri_avgCCIG20 ~ ns(c(1:366), df = 364)
## Model 2: ktri_avgCCIG20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 131362
## 2 354 918817332 -353 -918685970 19.812 0.1776anova(ktri_cig20_spline_m,ktri_cig20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: ktri_avgCCIG20 ~ ns(c(1:366), df = 11)
## Model 2: ktri_avgCCIG20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 918817332
## 2 330 813732310 24 105085023 1.7757 0.01507 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ktri_cig30_spline_d,ktri_cig30_spline_10d)## Analysis of Variance Table
##
## Model 1: ktri_avgCCIG30 ~ ns(c(1:366), df = 364)
## Model 2: ktri_avgCCIG30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 3201
## 2 330 500191283 -329 -500188082 474.96 0.03657 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ktri_cig30_spline_d,ktri_cig30_spline_m)## Analysis of Variance Table
##
## Model 1: ktri_avgCCIG30 ~ ns(c(1:366), df = 364)
## Model 2: ktri_avgCCIG30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 3201
## 2 354 571690352 -353 -571687151 505.94 0.03544 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ktri_cig30_spline_m,ktri_cig30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: ktri_avgCCIG30 ~ ns(c(1:366), df = 11)
## Model 2: ktri_avgCCIG30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 571690352
## 2 330 500191283 24 71499070 1.9655 0.005033 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Average Visibility
anova(ktri_vis10_spline_d,ktri_vis10_spline_10d)## Analysis of Variance Table
##
## Model 1: ktri_avgVIS10 ~ ns(c(1:366), df = 364)
## Model 2: ktri_avgVIS10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 14060
## 2 330 288618215 -329 -288604155 62.392 0.1007anova(ktri_vis10_spline_d,ktri_vis10_spline_m)## Analysis of Variance Table
##
## Model 1: ktri_avgVIS10 ~ ns(c(1:366), df = 364)
## Model 2: ktri_avgVIS10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 14060
## 2 354 345542368 -353 -345528308 69.62 0.09533 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ktri_vis10_spline_m,ktri_vis10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: ktri_avgVIS10 ~ ns(c(1:366), df = 11)
## Model 2: ktri_avgVIS10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 345542368
## 2 330 288618215 24 56924153 2.7119 0.0000412 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ktri_vis20_spline_d,ktri_vis20_spline_10d)## Analysis of Variance Table
##
## Model 1: ktri_avgVIS20 ~ ns(c(1:366), df = 364)
## Model 2: ktri_avgVIS20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 41764
## 2 330 169423885 -329 -169382121 12.327 0.224anova(ktri_vis20_spline_d,ktri_vis20_spline_m)## Analysis of Variance Table
##
## Model 1: ktri_avgVIS20 ~ ns(c(1:366), df = 364)
## Model 2: ktri_avgVIS20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 41764
## 2 354 194132304 -353 -194090540 13.165 0.217anova(ktri_vis20_spline_m,ktri_vis20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: ktri_avgVIS20 ~ ns(c(1:366), df = 11)
## Model 2: ktri_avgVIS20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 194132304
## 2 330 169423885 24 24708419 2.0053 0.003966 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ktri_vis30_spline_d,ktri_vis30_spline_10d)## Analysis of Variance Table
##
## Model 1: ktri_avgVIS30 ~ ns(c(1:366), df = 364)
## Model 2: ktri_avgVIS30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 7196
## 2 330 157067406 -329 -157060209 66.337 0.09764 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ktri_vis30_spline_d,ktri_vis30_spline_m)## Analysis of Variance Table
##
## Model 1: ktri_avgVIS30 ~ ns(c(1:366), df = 364)
## Model 2: ktri_avgVIS30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 7196
## 2 354 172213800 -353 -172206604 67.79 0.0966 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ktri_vis30_spline_m,ktri_vis30_spline_10d)## Analysis of Variance Table
##
## Model 1: ktri_avgVIS30 ~ ns(c(1:366), df = 11)
## Model 2: ktri_avgVIS30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 172213800
## 2 330 157067406 24 15146394 1.3259 0.1432Average Cloud Cover
anova(ktri_cc10_spline_d,ktri_cc10_spline_10d)## Analysis of Variance Table
##
## Model 1: ktri_avgCCOV10 ~ ns(c(1:366), df = 364)
## Model 2: ktri_avgCCOV10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.001
## 2 330 75.508 -329 -75.507 354.93 0.0423 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ktri_cc10_spline_d,ktri_cc10_spline_m)## Analysis of Variance Table
##
## Model 1: ktri_avgCCOV10 ~ ns(c(1:366), df = 364)
## Model 2: ktri_avgCCOV10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.001
## 2 354 99.700 -353 -99.699 436.79 0.03814 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ktri_cc10_spline_m,ktri_cc10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: ktri_avgCCOV10 ~ ns(c(1:366), df = 11)
## Model 2: ktri_avgCCOV10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 99.700
## 2 330 75.508 24 24.192 4.4054 0.0000000002386 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ktri_cc20_spline_d,ktri_cc20_spline_10d)## Analysis of Variance Table
##
## Model 1: ktri_avgCCOV20 ~ ns(c(1:366), df = 364)
## Model 2: ktri_avgCCOV20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.005
## 2 330 56.512 -329 -56.507 37.424 0.1297anova(ktri_cc20_spline_d,ktri_cc20_spline_m)## Analysis of Variance Table
##
## Model 1: ktri_avgCCOV20 ~ ns(c(1:366), df = 364)
## Model 2: ktri_avgCCOV20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.005
## 2 354 66.406 -353 -66.401 40.987 0.124anova(ktri_cc20_spline_m,ktri_cc20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: ktri_avgCCOV20 ~ ns(c(1:366), df = 11)
## Model 2: ktri_avgCCOV20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 66.406
## 2 330 56.512 24 9.8937 2.4073 0.0003151 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ktri_cc30_spline_d,ktri_cc30_spline_10d)## Analysis of Variance Table
##
## Model 1: ktri_avgCCOV30 ~ ns(c(1:366), df = 364)
## Model 2: ktri_avgCCOV30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.006
## 2 330 45.599 -329 -45.594 24.877 0.1588anova(ktri_cc30_spline_d,ktri_cc30_spline_m)## Analysis of Variance Table
##
## Model 1: ktri_avgCCOV30 ~ ns(c(1:366), df = 364)
## Model 2: ktri_avgCCOV30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.006
## 2 354 51.990 -353 -51.984 26.436 0.1541anova(ktri_cc30_spline_m,ktri_cc30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: ktri_avgCCOV30 ~ ns(c(1:366), df = 11)
## Model 2: ktri_avgCCOV30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 51.990
## 2 330 45.599 24 6.3909 1.9271 0.006316 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Average Altimeter Setting
anova(ktri_alt10_spline_d,ktri_alt10_spline_10d)## Analysis of Variance Table
##
## Model 1: ktri_avgALTS10 ~ ns(c(1:366), df = 364)
## Model 2: ktri_avgALTS10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.00
## 2 330 870.53 -329 -870.53 534.88 0.03446 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ktri_alt10_spline_d,ktri_alt10_spline_m)## Analysis of Variance Table
##
## Model 1: ktri_avgALTS10 ~ ns(c(1:366), df = 364)
## Model 2: ktri_avgALTS10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.0
## 2 354 1137.8 -353 -1137.8 651.57 0.03123 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ktri_alt10_spline_m,ktri_alt10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: ktri_avgALTS10 ~ ns(c(1:366), df = 11)
## Model 2: ktri_avgALTS10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 1137.80
## 2 330 870.53 24 267.26 4.2214 0.0000000009061 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ktri_alt20_spline_d,ktri_alt20_spline_10d)## Analysis of Variance Table
##
## Model 1: ktri_avgALTS20 ~ ns(c(1:366), df = 364)
## Model 2: ktri_avgALTS20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.04
## 2 330 402.95 -329 -402.91 33.223 0.1376anova(ktri_alt20_spline_d,ktri_alt20_spline_m)## Analysis of Variance Table
##
## Model 1: ktri_avgALTS20 ~ ns(c(1:366), df = 364)
## Model 2: ktri_avgALTS20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.04
## 2 354 574.74 -353 -574.7 44.166 0.1195anova(ktri_alt20_spline_m,ktri_alt20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: ktri_avgALTS20 ~ ns(c(1:366), df = 11)
## Model 2: ktri_avgALTS20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 574.74
## 2 330 402.95 24 171.79 5.862 0.000000000000006569 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ktri_alt30_spline_d,ktri_alt30_spline_10d)## Analysis of Variance Table
##
## Model 1: ktri_avgALTS30 ~ ns(c(1:366), df = 364)
## Model 2: ktri_avgALTS30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.017
## 2 330 276.841 -329 -276.82 48.358 0.1143anova(ktri_alt30_spline_d,ktri_alt30_spline_m)## Analysis of Variance Table
##
## Model 1: ktri_avgALTS30 ~ ns(c(1:366), df = 364)
## Model 2: ktri_avgALTS30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.02
## 2 354 358.42 -353 -358.41 58.353 0.1041anova(ktri_alt30_spline_m,ktri_alt30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: ktri_avgALTS30 ~ ns(c(1:366), df = 11)
## Model 2: ktri_avgALTS30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 358.42
## 2 330 276.84 24 81.584 4.0521 0.00000000309 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1PAJN
Temperature
anova(pajn_t10_spline_d,pajn_t10_spline_10d)## Analysis of Variance Table
##
## Model 1: pajn_avgTemp10 ~ ns(c(1:366), df = 364)
## Model 2: pajn_avgTemp10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.096
## 2 330 169.059 -329 -168.96 5.3472 0.3343anova(pajn_t10_spline_d,pajn_t10_spline_m)## Analysis of Variance Table
##
## Model 1: pajn_avgTemp10 ~ ns(c(1:366), df = 364)
## Model 2: pajn_avgTemp10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.10
## 2 354 359.35 -353 -359.25 10.596 0.2411anova(pajn_t10_spline_m,pajn_t10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: pajn_avgTemp10 ~ ns(c(1:366), df = 11)
## Model 2: pajn_avgTemp10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 359.35
## 2 330 169.06 24 190.29 15.476 < 0.00000000000000022 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(pajn_t20_spline_d,pajn_t20_spline_10d)## Analysis of Variance Table
##
## Model 1: pajn_avgTemp20 ~ ns(c(1:366), df = 364)
## Model 2: pajn_avgTemp20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.044
## 2 330 85.655 -329 -85.611 5.9325 0.3183anova(pajn_t20_spline_d,pajn_t20_spline_m)## Analysis of Variance Table
##
## Model 1: pajn_avgTemp20 ~ ns(c(1:366), df = 364)
## Model 2: pajn_avgTemp20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.044
## 2 354 153.648 -353 -153.6 9.9205 0.2489anova(pajn_t20_spline_m,pajn_t20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: pajn_avgTemp20 ~ ns(c(1:366), df = 11)
## Model 2: pajn_avgTemp20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 153.648
## 2 330 85.655 24 67.994 10.915 < 0.00000000000000022 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(pajn_t30_spline_d,pajn_t30_spline_10d)## Analysis of Variance Table
##
## Model 1: pajn_avgTemp30 ~ ns(c(1:366), df = 364)
## Model 2: pajn_avgTemp30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.026
## 2 330 59.563 -329 -59.538 7.0425 0.2935anova(pajn_t30_spline_d,pajn_t30_spline_m)## Analysis of Variance Table
##
## Model 1: pajn_avgTemp30 ~ ns(c(1:366), df = 364)
## Model 2: pajn_avgTemp30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.026
## 2 354 93.609 -353 -93.583 10.317 0.2443anova(pajn_t30_spline_m,pajn_t30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: pajn_avgTemp30 ~ ns(c(1:366), df = 11)
## Model 2: pajn_avgTemp30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 93.609
## 2 330 59.563 24 34.046 7.8594 < 0.00000000000000022 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Dewpoint Temperature
anova(pajn_dp10_spline_d,pajn_dp10_spline_10d) ## Analysis of Variance Table
##
## Model 1: pajn_avgDPTemp10 ~ ns(c(1:366), df = 364)
## Model 2: pajn_avgDPTemp10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.059
## 2 330 292.324 -329 -292.26 14.936 0.204anova(pajn_dp10_spline_d,pajn_dp10_spline_m)## Analysis of Variance Table
##
## Model 1: pajn_avgDPTemp10 ~ ns(c(1:366), df = 364)
## Model 2: pajn_avgDPTemp10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.06
## 2 354 618.20 -353 -618.14 29.442 0.1461anova(pajn_dp10_spline_m,pajn_dp10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: pajn_avgDPTemp10 ~ ns(c(1:366), df = 11)
## Model 2: pajn_avgDPTemp10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 618.20
## 2 330 292.32 24 325.88 15.328 < 0.00000000000000022 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(pajn_dp20_spline_d,pajn_dp20_spline_10d)## Analysis of Variance Table
##
## Model 1: pajn_avgDPTemp20 ~ ns(c(1:366), df = 364)
## Model 2: pajn_avgDPTemp20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.098
## 2 330 132.774 -329 -132.68 4.1295 0.377anova(pajn_dp20_spline_d,pajn_dp20_spline_m)## Analysis of Variance Table
##
## Model 1: pajn_avgDPTemp20 ~ ns(c(1:366), df = 364)
## Model 2: pajn_avgDPTemp20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.098
## 2 354 226.976 -353 -226.88 6.5813 0.3031anova(pajn_dp20_spline_m,pajn_dp20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: pajn_avgDPTemp20 ~ ns(c(1:366), df = 11)
## Model 2: pajn_avgDPTemp20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 226.98
## 2 330 132.77 24 94.202 9.7555 < 0.00000000000000022 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(pajn_dp30_spline_d,pajn_dp30_spline_10d)## Analysis of Variance Table
##
## Model 1: pajn_avgDPTemp30 ~ ns(c(1:366), df = 364)
## Model 2: pajn_avgDPTemp30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.057
## 2 330 108.761 -329 -108.7 5.7473 0.3231anova(pajn_dp30_spline_d,pajn_dp30_spline_m)## Analysis of Variance Table
##
## Model 1: pajn_avgDPTemp30 ~ ns(c(1:366), df = 364)
## Model 2: pajn_avgDPTemp30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.057
## 2 354 155.839 -353 -155.78 7.6764 0.2816anova(pajn_dp30_spline_m,pajn_dp30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: pajn_avgDPTemp30 ~ ns(c(1:366), df = 11)
## Model 2: pajn_avgDPTemp30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 155.84
## 2 330 108.76 24 47.078 5.9518 0.000000000000003465 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Wind Direction
anova(pajn_wd10_spline_d,pajn_wd10_spline_10d)## Analysis of Variance Table
##
## Model 1: pajn_avgWDIR10 ~ ns(c(1:366), df = 364)
## Model 2: pajn_avgWDIR10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 3
## 2 330 70149 -329 -70146 72.454 0.09345 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(pajn_wd10_spline_d,pajn_wd10_spline_m)## Analysis of Variance Table
##
## Model 1: pajn_avgWDIR10 ~ ns(c(1:366), df = 364)
## Model 2: pajn_avgWDIR10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 3
## 2 354 86804 -353 -86801 83.562 0.08705 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(pajn_wd10_spline_m,pajn_wd10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: pajn_avgWDIR10 ~ ns(c(1:366), df = 11)
## Model 2: pajn_avgWDIR10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 86804
## 2 330 70149 24 16656 3.2647 0.0000008803 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(pajn_wd20_spline_d,pajn_wd20_spline_10d)## Analysis of Variance Table
##
## Model 1: pajn_avgWDIR20 ~ ns(c(1:366), df = 364)
## Model 2: pajn_avgWDIR20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 520
## 2 330 40444 -329 -39924 0.2332 0.9608anova(pajn_wd20_spline_d,pajn_wd20_spline_m)## Analysis of Variance Table
##
## Model 1: pajn_avgWDIR20 ~ ns(c(1:366), df = 364)
## Model 2: pajn_avgWDIR20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 520
## 2 354 49238 -353 -48718 0.2653 0.947anova(pajn_wd20_spline_m,pajn_wd20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: pajn_avgWDIR20 ~ ns(c(1:366), df = 11)
## Model 2: pajn_avgWDIR20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 49238
## 2 330 40444 24 8793.6 2.9896 0.000006085 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(pajn_wd30_spline_d,pajn_wd30_spline_10d)## Analysis of Variance Table
##
## Model 1: pajn_avgWDIR30 ~ ns(c(1:366), df = 364)
## Model 2: pajn_avgWDIR30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 383.8
## 2 330 25046.8 -329 -24663 0.1953 0.9757anova(pajn_wd30_spline_d,pajn_wd30_spline_m)## Analysis of Variance Table
##
## Model 1: pajn_avgWDIR30 ~ ns(c(1:366), df = 364)
## Model 2: pajn_avgWDIR30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 384
## 2 354 32563 -353 -32180 0.2375 0.9591anova(pajn_wd30_spline_m,pajn_wd30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: pajn_avgWDIR30 ~ ns(c(1:366), df = 11)
## Model 2: pajn_avgWDIR30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 32563
## 2 330 25047 24 7516.6 4.1264 0.000000001803 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Wind Speed
anova(pajn_ws10_spline_d,pajn_ws10_spline_10d)## Analysis of Variance Table
##
## Model 1: pajn_avgWSPD10 ~ ns(c(1:366), df = 364)
## Model 2: pajn_avgWSPD10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.015
## 2 330 151.703 -329 -151.69 30.126 0.1445anova(pajn_ws10_spline_d,pajn_ws10_spline_m)## Analysis of Variance Table
##
## Model 1: pajn_avgWSPD10 ~ ns(c(1:366), df = 364)
## Model 2: pajn_avgWSPD10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.015
## 2 354 182.689 -353 -182.67 33.814 0.1364anova(pajn_ws10_spline_m,pajn_ws10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: pajn_avgWSPD10 ~ ns(c(1:366), df = 11)
## Model 2: pajn_avgWSPD10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 182.69
## 2 330 151.70 24 30.986 2.8085 0.00002129 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(pajn_ws20_spline_d,pajn_ws20_spline_10d)## Analysis of Variance Table
##
## Model 1: pajn_avgWSPD20 ~ ns(c(1:366), df = 364)
## Model 2: pajn_avgWSPD20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.042
## 2 330 82.108 -329 -82.066 5.92 0.3187anova(pajn_ws20_spline_d,pajn_ws20_spline_m)## Analysis of Variance Table
##
## Model 1: pajn_avgWSPD20 ~ ns(c(1:366), df = 364)
## Model 2: pajn_avgWSPD20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.042
## 2 354 97.254 -353 -97.212 6.5358 0.3041anova(pajn_ws20_spline_m,pajn_ws20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: pajn_avgWSPD20 ~ ns(c(1:366), df = 11)
## Model 2: pajn_avgWSPD20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 97.254
## 2 330 82.108 24 15.146 2.5364 0.0001343 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(pajn_ws30_spline_d,pajn_ws30_spline_10d)## Analysis of Variance Table
##
## Model 1: pajn_avgWSPD30 ~ ns(c(1:366), df = 364)
## Model 2: pajn_avgWSPD30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.019
## 2 330 49.711 -329 -49.692 7.7883 0.2797anova(pajn_ws30_spline_d,pajn_ws30_spline_m)## Analysis of Variance Table
##
## Model 1: pajn_avgWSPD30 ~ ns(c(1:366), df = 364)
## Model 2: pajn_avgWSPD30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.019
## 2 354 60.582 -353 -60.563 8.8468 0.2631anova(pajn_ws30_spline_m,pajn_ws30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: pajn_avgWSPD30 ~ ns(c(1:366), df = 11)
## Model 2: pajn_avgWSPD30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 60.582
## 2 330 49.711 24 10.871 3.0069 0.000005392 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Wind Gust Speed
anova(pajn_wg10_spline_d,pajn_wg10_spline_10d)## Analysis of Variance Table
##
## Model 1: pajn_avgWGSP10 ~ ns(c(1:366), df = 364)
## Model 2: pajn_avgWGSP10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.000
## 2 330 81.011 -329 -81.011 814.96 0.02792 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(pajn_wg10_spline_d,pajn_wg10_spline_m)## Analysis of Variance Table
##
## Model 1: pajn_avgWGSP10 ~ ns(c(1:366), df = 364)
## Model 2: pajn_avgWGSP10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0
## 2 354 119 -353 -119 1115.8 0.02387 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(pajn_wg10_spline_m,pajn_wg10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: pajn_avgWGSP10 ~ ns(c(1:366), df = 11)
## Model 2: pajn_avgWGSP10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 119.002
## 2 330 81.011 24 37.991 6.4482 < 0.00000000000000022 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(pajn_wg20_spline_d,pajn_wg20_spline_10d)## Analysis of Variance Table
##
## Model 1: pajn_avgWGSP20 ~ ns(c(1:366), df = 364)
## Model 2: pajn_avgWGSP20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.022
## 2 330 57.854 -329 -57.831 7.8578 0.2785anova(pajn_wg20_spline_d,pajn_wg20_spline_m)## Analysis of Variance Table
##
## Model 1: pajn_avgWGSP20 ~ ns(c(1:366), df = 364)
## Model 2: pajn_avgWGSP20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.022
## 2 354 70.517 -353 -70.495 8.9272 0.2619anova(pajn_wg20_spline_m,pajn_wg20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: pajn_avgWGSP20 ~ ns(c(1:366), df = 11)
## Model 2: pajn_avgWGSP20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 70.517
## 2 330 57.854 24 12.663 3.0097 0.00000529 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(pajn_wg30_spline_d,pajn_wg30_spline_10d)## Analysis of Variance Table
##
## Model 1: pajn_avgWGSP30 ~ ns(c(1:366), df = 364)
## Model 2: pajn_avgWGSP30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.051
## 2 330 43.706 -329 -43.654 2.5795 0.466anova(pajn_wg30_spline_d,pajn_wg30_spline_m)## Analysis of Variance Table
##
## Model 1: pajn_avgWGSP30 ~ ns(c(1:366), df = 364)
## Model 2: pajn_avgWGSP30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.051
## 2 354 53.877 -353 -53.826 2.9643 0.4383anova(pajn_wg30_spline_m,pajn_wg30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: pajn_avgWGSP30 ~ ns(c(1:366), df = 11)
## Model 2: pajn_avgWGSP30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 53.877
## 2 330 43.706 24 10.171 3.2 0.000001391 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Average Cloud Ceiling Height
anova(pajn_cig10_spline_d,pajn_cig10_spline_10d)## Analysis of Variance Table
##
## Model 1: pajn_avgCCIG10 ~ ns(c(1:366), df = 364)
## Model 2: pajn_avgCCIG10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 893308
## 2 330 936619487 -329 -935726179 3.1838 0.4244anova(pajn_cig10_spline_d,pajn_cig10_spline_m)## Analysis of Variance Table
##
## Model 1: pajn_avgCCIG10 ~ ns(c(1:366), df = 364)
## Model 2: pajn_avgCCIG10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 893308
## 2 354 1423790124 -353 -1422896816 4.5123 0.3619anova(pajn_cig10_spline_m,pajn_cig10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: pajn_avgCCIG10 ~ ns(c(1:366), df = 11)
## Model 2: pajn_avgCCIG10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 1423790124
## 2 330 936619487 24 487170637 7.1519 < 0.00000000000000022 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(pajn_cig20_spline_d,pajn_cig20_spline_10d)## Analysis of Variance Table
##
## Model 1: pajn_avgCCIG20 ~ ns(c(1:366), df = 364)
## Model 2: pajn_avgCCIG20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 1376853
## 2 330 458657170 -329 -457280317 1.0095 0.6797anova(pajn_cig20_spline_d,pajn_cig20_spline_m)## Analysis of Variance Table
##
## Model 1: pajn_avgCCIG20 ~ ns(c(1:366), df = 364)
## Model 2: pajn_avgCCIG20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 1376853
## 2 354 636766571 -353 -635389719 1.3073 0.6176anova(pajn_cig20_spline_m,pajn_cig20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: pajn_avgCCIG20 ~ ns(c(1:366), df = 11)
## Model 2: pajn_avgCCIG20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 636766571
## 2 330 458657170 24 178109402 5.3395 0.0000000000002774 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(pajn_cig30_spline_d,pajn_cig30_spline_10d)## Analysis of Variance Table
##
## Model 1: pajn_avgCCIG30 ~ ns(c(1:366), df = 364)
## Model 2: pajn_avgCCIG30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 869455
## 2 330 353692093 -329 -352822638 1.2334 0.6314anova(pajn_cig30_spline_d,pajn_cig30_spline_m)## Analysis of Variance Table
##
## Model 1: pajn_avgCCIG30 ~ ns(c(1:366), df = 364)
## Model 2: pajn_avgCCIG30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 869455
## 2 354 485879498 -353 -485010043 1.5803 0.5731anova(pajn_cig30_spline_m,pajn_cig30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: pajn_avgCCIG30 ~ ns(c(1:366), df = 11)
## Model 2: pajn_avgCCIG30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 485879498
## 2 330 353692093 24 132187405 5.1389 0.000000000001178 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Average Visibility
anova(pajn_vis10_spline_d,pajn_vis10_spline_10d)## Analysis of Variance Table
##
## Model 1: pajn_avgVIS10 ~ ns(c(1:366), df = 364)
## Model 2: pajn_avgVIS10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 185417
## 2 330 271678090 -329 -271492673 4.4505 0.3642anova(pajn_vis10_spline_d,pajn_vis10_spline_m)## Analysis of Variance Table
##
## Model 1: pajn_avgVIS10 ~ ns(c(1:366), df = 364)
## Model 2: pajn_avgVIS10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 185417
## 2 354 327678948 -353 -327493531 5.0036 0.3449anova(pajn_vis10_spline_m,pajn_vis10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: pajn_avgVIS10 ~ ns(c(1:366), df = 11)
## Model 2: pajn_avgVIS10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 327678948
## 2 330 271678090 24 56000858 2.8343 0.00001784 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(pajn_vis20_spline_d,pajn_vis20_spline_10d)## Analysis of Variance Table
##
## Model 1: pajn_avgVIS20 ~ ns(c(1:366), df = 364)
## Model 2: pajn_avgVIS20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 91889
## 2 330 130582155 -329 -130490266 4.3164 0.3694anova(pajn_vis20_spline_d,pajn_vis20_spline_m)## Analysis of Variance Table
##
## Model 1: pajn_avgVIS20 ~ ns(c(1:366), df = 364)
## Model 2: pajn_avgVIS20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 91889
## 2 354 164353033 -353 -164261145 5.064 0.343anova(pajn_vis20_spline_m,pajn_vis20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: pajn_avgVIS20 ~ ns(c(1:366), df = 11)
## Model 2: pajn_avgVIS20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 164353033
## 2 330 130582155 24 33770878 3.556 0.0000001104 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(pajn_vis30_spline_d,pajn_vis30_spline_10d)## Analysis of Variance Table
##
## Model 1: pajn_avgVIS30 ~ ns(c(1:366), df = 364)
## Model 2: pajn_avgVIS30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 85589
## 2 330 175536646 -329 -175451057 6.2308 0.311anova(pajn_vis30_spline_d,pajn_vis30_spline_m)## Analysis of Variance Table
##
## Model 1: pajn_avgVIS30 ~ ns(c(1:366), df = 364)
## Model 2: pajn_avgVIS30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 85589
## 2 354 218436110 -353 -218350521 7.2271 0.2899anova(pajn_vis30_spline_m,pajn_vis30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: pajn_avgVIS30 ~ ns(c(1:366), df = 11)
## Model 2: pajn_avgVIS30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 218436110
## 2 330 175536646 24 42899464 3.3604 0.0000004463 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Average Cloud Cover
anova(pajn_cc10_spline_d,pajn_cc10_spline_10d)## Analysis of Variance Table
##
## Model 1: pajn_avgCCOV10 ~ ns(c(1:366), df = 364)
## Model 2: pajn_avgCCOV10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.07
## 2 330 100.80 -329 -100.73 4.3456 0.3682anova(pajn_cc10_spline_d,pajn_cc10_spline_m)## Analysis of Variance Table
##
## Model 1: pajn_avgCCOV10 ~ ns(c(1:366), df = 364)
## Model 2: pajn_avgCCOV10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.07
## 2 354 154.96 -353 -154.89 6.2275 0.3111anova(pajn_cc10_spline_m,pajn_cc10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: pajn_avgCCOV10 ~ ns(c(1:366), df = 11)
## Model 2: pajn_avgCCOV10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 154.96
## 2 330 100.80 24 54.155 7.3869 < 0.00000000000000022 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(pajn_cc20_spline_d,pajn_cc20_spline_10d)## Analysis of Variance Table
##
## Model 1: pajn_avgCCOV20 ~ ns(c(1:366), df = 364)
## Model 2: pajn_avgCCOV20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.120
## 2 330 44.304 -329 -44.185 1.1229 0.654anova(pajn_cc20_spline_d,pajn_cc20_spline_m)## Analysis of Variance Table
##
## Model 1: pajn_avgCCOV20 ~ ns(c(1:366), df = 364)
## Model 2: pajn_avgCCOV20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.120
## 2 354 62.593 -353 -62.474 1.4798 0.5884anova(pajn_cc20_spline_m,pajn_cc20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: pajn_avgCCOV20 ~ ns(c(1:366), df = 11)
## Model 2: pajn_avgCCOV20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 62.593
## 2 330 44.304 24 18.289 5.6761 0.00000000000002479 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(pajn_cc30_spline_d,pajn_cc30_spline_10d)## Analysis of Variance Table
##
## Model 1: pajn_avgCCOV30 ~ ns(c(1:366), df = 364)
## Model 2: pajn_avgCCOV30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.090
## 2 330 34.319 -329 -34.229 1.1558 0.647anova(pajn_cc30_spline_d,pajn_cc30_spline_m)## Analysis of Variance Table
##
## Model 1: pajn_avgCCOV30 ~ ns(c(1:366), df = 364)
## Model 2: pajn_avgCCOV30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.090
## 2 354 47.734 -353 -47.644 1.4993 0.5853anova(pajn_cc30_spline_m,pajn_cc30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: pajn_avgCCOV30 ~ ns(c(1:366), df = 11)
## Model 2: pajn_avgCCOV30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 47.734
## 2 330 34.319 24 13.414 5.3744 0.0000000000002159 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Average Altimeter Setting
anova(pajn_alt10_spline_d,pajn_alt10_spline_10d)## Analysis of Variance Table
##
## Model 1: pajn_avgALTS10 ~ ns(c(1:366), df = 364)
## Model 2: pajn_avgALTS10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.02
## 2 330 2345.36 -329 -2345.3 418.27 0.03897 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(pajn_alt10_spline_d,pajn_alt10_spline_m)## Analysis of Variance Table
##
## Model 1: pajn_avgALTS10 ~ ns(c(1:366), df = 364)
## Model 2: pajn_avgALTS10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.0
## 2 354 3451.3 -353 -3451.3 573.66 0.03328 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(pajn_alt10_spline_m,pajn_alt10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: pajn_avgALTS10 ~ ns(c(1:366), df = 11)
## Model 2: pajn_avgALTS10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 3451.3
## 2 330 2345.4 24 1105.9 6.4837 < 0.00000000000000022 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(pajn_alt20_spline_d,pajn_alt20_spline_10d)## Analysis of Variance Table
##
## Model 1: pajn_avgALTS20 ~ ns(c(1:366), df = 364)
## Model 2: pajn_avgALTS20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.0
## 2 330 1149.7 -329 -1149.7 1241.6 0.02262 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(pajn_alt20_spline_d,pajn_alt20_spline_m)## Analysis of Variance Table
##
## Model 1: pajn_avgALTS20 ~ ns(c(1:366), df = 364)
## Model 2: pajn_avgALTS20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.0
## 2 354 1713.3 -353 -1713.3 1724.4 0.0192 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(pajn_alt20_spline_m,pajn_alt20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: pajn_avgALTS20 ~ ns(c(1:366), df = 11)
## Model 2: pajn_avgALTS20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 1713.3
## 2 330 1149.7 24 563.59 6.7402 < 0.00000000000000022 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(pajn_alt30_spline_d,pajn_alt30_spline_10d)## Analysis of Variance Table
##
## Model 1: pajn_avgALTS30 ~ ns(c(1:366), df = 364)
## Model 2: pajn_avgALTS30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.00
## 2 330 863.11 -329 -863.1 966.12 0.02565 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(pajn_alt30_spline_d,pajn_alt30_spline_m)## Analysis of Variance Table
##
## Model 1: pajn_avgALTS30 ~ ns(c(1:366), df = 364)
## Model 2: pajn_avgALTS30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.0
## 2 354 1177.8 -353 -1177.8 1228.7 0.02274 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(pajn_alt30_spline_m,pajn_alt30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: pajn_avgALTS30 ~ ns(c(1:366), df = 11)
## Model 2: pajn_avgALTS30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 1177.75
## 2 330 863.11 24 314.64 5.0125 0.000000000002934 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1KELP
Temperature
anova(kelp_t10_spline_d,kelp_t10_spline_10d)## Analysis of Variance Table
##
## Model 1: kelp_avgTemp10 ~ ns(c(1:366), df = 364)
## Model 2: kelp_avgTemp10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.67
## 2 330 382.62 -329 -381.95 1.7338 0.5519anova(kelp_t10_spline_d,kelp_t10_spline_m)## Analysis of Variance Table
##
## Model 1: kelp_avgTemp10 ~ ns(c(1:366), df = 364)
## Model 2: kelp_avgTemp10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.67
## 2 354 480.19 -353 -479.52 2.0288 0.5169anova(kelp_t10_spline_m,kelp_t10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kelp_avgTemp10 ~ ns(c(1:366), df = 11)
## Model 2: kelp_avgTemp10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 480.19
## 2 330 382.62 24 97.574 3.5065 0.0000001574 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kelp_t20_spline_d,kelp_t20_spline_10d)## Analysis of Variance Table
##
## Model 1: kelp_avgTemp20 ~ ns(c(1:366), df = 364)
## Model 2: kelp_avgTemp20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.187
## 2 330 186.345 -329 -186.16 3.0326 0.4338anova(kelp_t20_spline_d,kelp_t20_spline_m)## Analysis of Variance Table
##
## Model 1: kelp_avgTemp20 ~ ns(c(1:366), df = 364)
## Model 2: kelp_avgTemp20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.187
## 2 354 236.988 -353 -236.8 3.5954 0.4017anova(kelp_t20_spline_m,kelp_t20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kelp_avgTemp20 ~ ns(c(1:366), df = 11)
## Model 2: kelp_avgTemp20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 236.99
## 2 330 186.34 24 50.643 3.7368 0.0000000301 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kelp_t30_spline_d,kelp_t30_spline_10d)## Analysis of Variance Table
##
## Model 1: kelp_avgTemp30 ~ ns(c(1:366), df = 364)
## Model 2: kelp_avgTemp30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.101
## 2 330 129.230 -329 -129.13 3.8873 0.3876anova(kelp_t30_spline_d,kelp_t30_spline_m)## Analysis of Variance Table
##
## Model 1: kelp_avgTemp30 ~ ns(c(1:366), df = 364)
## Model 2: kelp_avgTemp30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.101
## 2 354 153.863 -353 -153.76 4.3141 0.3695anova(kelp_t30_spline_m,kelp_t30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kelp_avgTemp30 ~ ns(c(1:366), df = 11)
## Model 2: kelp_avgTemp30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 153.86
## 2 330 129.23 24 24.634 2.621 0.00007621 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Dewpoint Temperature
anova(kelp_dp10_spline_d,kelp_dp10_spline_10d) ## Analysis of Variance Table
##
## Model 1: kelp_avgDPTemp10 ~ ns(c(1:366), df = 364)
## Model 2: kelp_avgDPTemp10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.37
## 2 330 638.92 -329 -638.55 5.2322 0.3377anova(kelp_dp10_spline_d,kelp_dp10_spline_m)## Analysis of Variance Table
##
## Model 1: kelp_avgDPTemp10 ~ ns(c(1:366), df = 364)
## Model 2: kelp_avgDPTemp10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.37
## 2 354 908.15 -353 -907.78 6.9326 0.2957anova(kelp_dp10_spline_m,kelp_dp10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kelp_avgDPTemp10 ~ ns(c(1:366), df = 11)
## Model 2: kelp_avgDPTemp10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 908.15
## 2 330 638.92 24 269.24 5.7942 0.00000000000001065 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kelp_dp20_spline_d,kelp_dp20_spline_10d)## Analysis of Variance Table
##
## Model 1: kelp_avgDPTemp20 ~ ns(c(1:366), df = 364)
## Model 2: kelp_avgDPTemp20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.085
## 2 330 260.981 -329 -260.9 9.3755 0.2558anova(kelp_dp20_spline_d,kelp_dp20_spline_m)## Analysis of Variance Table
##
## Model 1: kelp_avgDPTemp20 ~ ns(c(1:366), df = 364)
## Model 2: kelp_avgDPTemp20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.08
## 2 354 420.01 -353 -419.93 14.064 0.2101anova(kelp_dp20_spline_m,kelp_dp20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kelp_avgDPTemp20 ~ ns(c(1:366), df = 11)
## Model 2: kelp_avgDPTemp20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 420.01
## 2 330 260.98 24 159.03 8.3787 < 0.00000000000000022 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kelp_dp30_spline_d,kelp_dp30_spline_10d)## Analysis of Variance Table
##
## Model 1: kelp_avgDPTemp30 ~ ns(c(1:366), df = 364)
## Model 2: kelp_avgDPTemp30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.045
## 2 330 152.201 -329 -152.16 10.23 0.2453anova(kelp_dp30_spline_d,kelp_dp30_spline_m)## Analysis of Variance Table
##
## Model 1: kelp_avgDPTemp30 ~ ns(c(1:366), df = 364)
## Model 2: kelp_avgDPTemp30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.045
## 2 354 288.642 -353 -288.6 18.085 0.1858anova(kelp_dp30_spline_m,kelp_dp30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kelp_avgDPTemp30 ~ ns(c(1:366), df = 11)
## Model 2: kelp_avgDPTemp30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 288.64
## 2 330 152.20 24 136.44 12.326 < 0.00000000000000022 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Wind Direction
anova(kelp_wd10_spline_d,kelp_wd10_spline_10d)## Analysis of Variance Table
##
## Model 1: kelp_avgWDIR10 ~ ns(c(1:366), df = 364)
## Model 2: kelp_avgWDIR10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 48
## 2 330 88537 -329 -88490 5.6172 0.3266anova(kelp_wd10_spline_d,kelp_wd10_spline_m)## Analysis of Variance Table
##
## Model 1: kelp_avgWDIR10 ~ ns(c(1:366), df = 364)
## Model 2: kelp_avgWDIR10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 48
## 2 354 97708 -353 -97660 5.7779 0.3224anova(kelp_wd10_spline_m,kelp_wd10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kelp_avgWDIR10 ~ ns(c(1:366), df = 11)
## Model 2: kelp_avgWDIR10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 97708
## 2 330 88537 24 9170.6 1.4242 0.09218 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kelp_wd20_spline_d,kelp_wd20_spline_10d)## Analysis of Variance Table
##
## Model 1: kelp_avgWDIR20 ~ ns(c(1:366), df = 364)
## Model 2: kelp_avgWDIR20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 8
## 2 330 39211 -329 -39203 15.287 0.2017anova(kelp_wd20_spline_d,kelp_wd20_spline_m)## Analysis of Variance Table
##
## Model 1: kelp_avgWDIR20 ~ ns(c(1:366), df = 364)
## Model 2: kelp_avgWDIR20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 8
## 2 354 51586 -353 -51579 18.745 0.1825anova(kelp_wd20_spline_m,kelp_wd20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kelp_avgWDIR20 ~ ns(c(1:366), df = 11)
## Model 2: kelp_avgWDIR20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 51586
## 2 330 39211 24 12375 4.3396 0.0000000003844 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kelp_wd30_spline_d,kelp_wd30_spline_10d)## Analysis of Variance Table
##
## Model 1: kelp_avgWDIR30 ~ ns(c(1:366), df = 364)
## Model 2: kelp_avgWDIR30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 7
## 2 330 25404 -329 -25397 11.037 0.2364anova(kelp_wd30_spline_d,kelp_wd30_spline_m)## Analysis of Variance Table
##
## Model 1: kelp_avgWDIR30 ~ ns(c(1:366), df = 364)
## Model 2: kelp_avgWDIR30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 7
## 2 354 34048 -353 -34041 13.787 0.2122anova(kelp_wd30_spline_m,kelp_wd30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kelp_avgWDIR30 ~ ns(c(1:366), df = 11)
## Model 2: kelp_avgWDIR30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 34048
## 2 330 25404 24 8643.9 4.6786 0.00000000003292 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Wind Speed
anova(kelp_ws10_spline_d,kelp_ws10_spline_10d)## Analysis of Variance Table
##
## Model 1: kelp_avgWSPD10 ~ ns(c(1:366), df = 364)
## Model 2: kelp_avgWSPD10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.152
## 2 330 91.887 -329 -91.734 1.8331 0.5393anova(kelp_ws10_spline_d,kelp_ws10_spline_m)## Analysis of Variance Table
##
## Model 1: kelp_avgWSPD10 ~ ns(c(1:366), df = 364)
## Model 2: kelp_avgWSPD10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.152
## 2 354 100.075 -353 -99.923 1.8609 0.536anova(kelp_ws10_spline_m,kelp_ws10_spline_10d)## Analysis of Variance Table
##
## Model 1: kelp_avgWSPD10 ~ ns(c(1:366), df = 11)
## Model 2: kelp_avgWSPD10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 100.075
## 2 330 91.887 24 8.1887 1.2254 0.2165anova(kelp_ws20_spline_d,kelp_ws20_spline_10d)## Analysis of Variance Table
##
## Model 1: kelp_avgWSPD20 ~ ns(c(1:366), df = 364)
## Model 2: kelp_avgWSPD20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.069
## 2 330 43.214 -329 -43.145 1.9118 0.53anova(kelp_ws20_spline_d,kelp_ws20_spline_m)## Analysis of Variance Table
##
## Model 1: kelp_avgWSPD20 ~ ns(c(1:366), df = 364)
## Model 2: kelp_avgWSPD20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.069
## 2 354 47.366 -353 -47.297 1.9533 0.5252anova(kelp_ws20_spline_m,kelp_ws20_spline_10d)## Analysis of Variance Table
##
## Model 1: kelp_avgWSPD20 ~ ns(c(1:366), df = 11)
## Model 2: kelp_avgWSPD20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 47.366
## 2 330 43.214 24 4.152 1.3211 0.1462anova(kelp_ws30_spline_d,kelp_ws30_spline_10d)## Analysis of Variance Table
##
## Model 1: kelp_avgWSPD30 ~ ns(c(1:366), df = 364)
## Model 2: kelp_avgWSPD30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.0146
## 2 330 25.4612 -329 -25.447 5.307 0.3355anova(kelp_ws30_spline_d,kelp_ws30_spline_m)## Analysis of Variance Table
##
## Model 1: kelp_avgWSPD30 ~ ns(c(1:366), df = 364)
## Model 2: kelp_avgWSPD30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.0146
## 2 354 29.4625 -353 -29.448 5.724 0.3238anova(kelp_ws30_spline_m,kelp_ws30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kelp_avgWSPD30 ~ ns(c(1:366), df = 11)
## Model 2: kelp_avgWSPD30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 29.462
## 2 330 25.461 24 4.0013 2.1609 0.001526 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Wind Gust Speed
anova(kelp_wg10_spline_d,kelp_wg10_spline_10d)## Analysis of Variance Table
##
## Model 1: kelp_avgWGSP10 ~ ns(c(1:366), df = 364)
## Model 2: kelp_avgWGSP10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.202
## 2 330 104.802 -329 -104.6 1.5712 0.5744anova(kelp_wg10_spline_d,kelp_wg10_spline_m)## Analysis of Variance Table
##
## Model 1: kelp_avgWGSP10 ~ ns(c(1:366), df = 364)
## Model 2: kelp_avgWGSP10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.202
## 2 354 132.781 -353 -132.58 1.8561 0.5366anova(kelp_wg10_spline_m,kelp_wg10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kelp_avgWGSP10 ~ ns(c(1:366), df = 11)
## Model 2: kelp_avgWGSP10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 132.78
## 2 330 104.80 24 27.98 3.6709 0.00000004837 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kelp_wg20_spline_d,kelp_wg20_spline_10d)## Analysis of Variance Table
##
## Model 1: kelp_avgWGSP20 ~ ns(c(1:366), df = 364)
## Model 2: kelp_avgWGSP20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.014
## 2 330 68.666 -329 -68.652 15.151 0.2026anova(kelp_wg20_spline_d,kelp_wg20_spline_m)## Analysis of Variance Table
##
## Model 1: kelp_avgWGSP20 ~ ns(c(1:366), df = 364)
## Model 2: kelp_avgWGSP20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.014
## 2 354 90.895 -353 -90.881 18.693 0.1828anova(kelp_wg20_spline_m,kelp_wg20_spline_10d)## Analysis of Variance Table
##
## Model 1: kelp_avgWGSP20 ~ ns(c(1:366), df = 11)
## Model 2: kelp_avgWGSP20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 90.895
## 2 330 68.666 24 22.228 4.4511 0.0000000001713 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kelp_wg30_spline_d,kelp_wg30_spline_10d)## Analysis of Variance Table
##
## Model 1: kelp_avgWGSP30 ~ ns(c(1:366), df = 364)
## Model 2: kelp_avgWGSP30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.002
## 2 330 54.891 -329 -54.888 69.147 0.09565 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kelp_wg30_spline_d,kelp_wg30_spline_m)## Analysis of Variance Table
##
## Model 1: kelp_avgWGSP30 ~ ns(c(1:366), df = 364)
## Model 2: kelp_avgWGSP30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.002
## 2 354 72.434 -353 -72.431 85.043 0.08629 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kelp_wg30_spline_m,kelp_wg30_spline_10d)## Analysis of Variance Table
##
## Model 1: kelp_avgWGSP30 ~ ns(c(1:366), df = 11)
## Model 2: kelp_avgWGSP30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 72.434
## 2 330 54.891 24 17.543 4.3944 0.0000000002584 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Average Cloud Ceiling Height
anova(kelp_cig10_spline_d,kelp_cig10_spline_10d)## Analysis of Variance Table
##
## Model 1: kelp_avgCCIG10 ~ ns(c(1:366), df = 364)
## Model 2: kelp_avgCCIG10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 52947
## 2 330 993254651 -329 -993201704 57.017 0.1053anova(kelp_cig10_spline_d,kelp_cig10_spline_m)## Analysis of Variance Table
##
## Model 1: kelp_avgCCIG10 ~ ns(c(1:366), df = 364)
## Model 2: kelp_avgCCIG10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 52947
## 2 354 1166528545 -353 -1166475599 62.411 0.1007anova(kelp_cig10_spline_m,kelp_cig10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kelp_avgCCIG10 ~ ns(c(1:366), df = 11)
## Model 2: kelp_avgCCIG10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 1166528545
## 2 330 993254651 24 173273894 2.3987 0.0003333 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kelp_cig20_spline_d,kelp_cig20_spline_10d)## Analysis of Variance Table
##
## Model 1: kelp_avgCCIG20 ~ ns(c(1:366), df = 364)
## Model 2: kelp_avgCCIG20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 456701
## 2 330 492674927 -329 -492218226 3.2759 0.419anova(kelp_cig20_spline_d,kelp_cig20_spline_m)## Analysis of Variance Table
##
## Model 1: kelp_avgCCIG20 ~ ns(c(1:366), df = 364)
## Model 2: kelp_avgCCIG20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 456701
## 2 354 629790278 -353 -629333578 3.9037 0.3869anova(kelp_cig20_spline_m,kelp_cig20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kelp_avgCCIG20 ~ ns(c(1:366), df = 11)
## Model 2: kelp_avgCCIG20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 629790278
## 2 330 492674927 24 137115351 3.8267 0.00000001575 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kelp_cig30_spline_d,kelp_cig30_spline_10d)## Analysis of Variance Table
##
## Model 1: kelp_avgCCIG30 ~ ns(c(1:366), df = 364)
## Model 2: kelp_avgCCIG30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 196258
## 2 330 338413804 -329 -338217546 5.2381 0.3376anova(kelp_cig30_spline_d,kelp_cig30_spline_m)## Analysis of Variance Table
##
## Model 1: kelp_avgCCIG30 ~ ns(c(1:366), df = 364)
## Model 2: kelp_avgCCIG30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 196258
## 2 354 436466691 -353 -436270433 6.2973 0.3095anova(kelp_cig30_spline_m,kelp_cig30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kelp_avgCCIG30 ~ ns(c(1:366), df = 11)
## Model 2: kelp_avgCCIG30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 436466691
## 2 330 338413804 24 98052887 3.984 0.000000005058 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Average Visibility
anova(kelp_vis10_spline_d,kelp_vis10_spline_10d)## Analysis of Variance Table
##
## Model 1: kelp_avgVIS10 ~ ns(c(1:366), df = 364)
## Model 2: kelp_avgVIS10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 36377
## 2 330 21030104 -329 -20993727 1.7542 0.5492anova(kelp_vis10_spline_d,kelp_vis10_spline_m)## Analysis of Variance Table
##
## Model 1: kelp_avgVIS10 ~ ns(c(1:366), df = 364)
## Model 2: kelp_avgVIS10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 36377
## 2 354 21969704 -353 -21933328 1.7081 0.5553anova(kelp_vis10_spline_m,kelp_vis10_spline_10d)## Analysis of Variance Table
##
## Model 1: kelp_avgVIS10 ~ ns(c(1:366), df = 11)
## Model 2: kelp_avgVIS10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 21969704
## 2 330 21030104 24 939600 0.6143 0.9236anova(kelp_vis20_spline_d,kelp_vis20_spline_10d)## Analysis of Variance Table
##
## Model 1: kelp_avgVIS20 ~ ns(c(1:366), df = 364)
## Model 2: kelp_avgVIS20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 1860
## 2 330 13488659 -329 -13486799 22.035 0.1686anova(kelp_vis20_spline_d,kelp_vis20_spline_m)## Analysis of Variance Table
##
## Model 1: kelp_avgVIS20 ~ ns(c(1:366), df = 364)
## Model 2: kelp_avgVIS20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 1860
## 2 354 15338551 -353 -15336690 23.354 0.1638anova(kelp_vis20_spline_m,kelp_vis20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kelp_avgVIS20 ~ ns(c(1:366), df = 11)
## Model 2: kelp_avgVIS20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 15338551
## 2 330 13488659 24 1849891 1.8857 0.008046 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kelp_vis30_spline_d,kelp_vis30_spline_10d)## Analysis of Variance Table
##
## Model 1: kelp_avgVIS30 ~ ns(c(1:366), df = 364)
## Model 2: kelp_avgVIS30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 397
## 2 330 37303493 -329 -37303096 285.66 0.04714 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kelp_vis30_spline_d,kelp_vis30_spline_m)## Analysis of Variance Table
##
## Model 1: kelp_avgVIS30 ~ ns(c(1:366), df = 364)
## Model 2: kelp_avgVIS30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 397
## 2 354 47783988 -353 -47783591 341.04 0.04315 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kelp_vis30_spline_m,kelp_vis30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kelp_avgVIS30 ~ ns(c(1:366), df = 11)
## Model 2: kelp_avgVIS30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 47783988
## 2 330 37303493 24 10480495 3.8631 0.00000001211 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Average Cloud Cover
anova(kelp_cc10_spline_d,kelp_cc10_spline_10d)## Analysis of Variance Table
##
## Model 1: kelp_avgCCOV10 ~ ns(c(1:366), df = 364)
## Model 2: kelp_avgCCOV10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.004
## 2 330 103.505 -329 -103.5 76.912 0.09071 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kelp_cc10_spline_d,kelp_cc10_spline_m)## Analysis of Variance Table
##
## Model 1: kelp_avgCCOV10 ~ ns(c(1:366), df = 364)
## Model 2: kelp_avgCCOV10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.004
## 2 354 123.197 -353 -123.19 85.321 0.08615 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kelp_cc10_spline_m,kelp_cc10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kelp_avgCCOV10 ~ ns(c(1:366), df = 11)
## Model 2: kelp_avgCCOV10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 123.2
## 2 330 103.5 24 19.692 2.616 0.00007884 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kelp_cc20_spline_d,kelp_cc20_spline_10d)## Analysis of Variance Table
##
## Model 1: kelp_avgCCOV20 ~ ns(c(1:366), df = 364)
## Model 2: kelp_avgCCOV20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.016
## 2 330 59.205 -329 -59.189 11.375 0.233anova(kelp_cc20_spline_d,kelp_cc20_spline_m)## Analysis of Variance Table
##
## Model 1: kelp_avgCCOV20 ~ ns(c(1:366), df = 364)
## Model 2: kelp_avgCCOV20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.016
## 2 354 73.487 -353 -73.471 13.16 0.217anova(kelp_cc20_spline_m,kelp_cc20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kelp_avgCCOV20 ~ ns(c(1:366), df = 11)
## Model 2: kelp_avgCCOV20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 73.487
## 2 330 59.205 24 14.283 3.3171 0.0000006071 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kelp_cc30_spline_d,kelp_cc30_spline_10d)## Analysis of Variance Table
##
## Model 1: kelp_avgCCOV30 ~ ns(c(1:366), df = 364)
## Model 2: kelp_avgCCOV30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.019
## 2 330 43.652 -329 -43.633 6.8121 0.2981anova(kelp_cc30_spline_d,kelp_cc30_spline_m)## Analysis of Variance Table
##
## Model 1: kelp_avgCCOV30 ~ ns(c(1:366), df = 364)
## Model 2: kelp_avgCCOV30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.019
## 2 354 54.354 -353 -54.334 7.9062 0.2777anova(kelp_cc30_spline_m,kelp_cc30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kelp_avgCCOV30 ~ ns(c(1:366), df = 11)
## Model 2: kelp_avgCCOV30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 54.354
## 2 330 43.652 24 10.702 3.371 0.0000004139 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Average Altimeter Setting
anova(kelp_alt10_spline_d,kelp_alt10_spline_10d)## Analysis of Variance Table
##
## Model 1: kelp_avgALTS10 ~ ns(c(1:366), df = 364)
## Model 2: kelp_avgALTS10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.04
## 2 330 509.95 -329 -509.91 38.825 0.1274anova(kelp_alt10_spline_d,kelp_alt10_spline_m)## Analysis of Variance Table
##
## Model 1: kelp_avgALTS10 ~ ns(c(1:366), df = 364)
## Model 2: kelp_avgALTS10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.04
## 2 354 623.95 -353 -623.91 44.276 0.1194anova(kelp_alt10_spline_m,kelp_alt10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kelp_avgALTS10 ~ ns(c(1:366), df = 11)
## Model 2: kelp_avgALTS10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 623.95
## 2 330 509.95 24 114 3.074 0.000003374 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kelp_alt20_spline_d,kelp_alt20_spline_10d)## Analysis of Variance Table
##
## Model 1: kelp_avgALTS20 ~ ns(c(1:366), df = 364)
## Model 2: kelp_avgALTS20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.005
## 2 330 228.371 -329 -228.37 142.36 0.06674 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kelp_alt20_spline_d,kelp_alt20_spline_m)## Analysis of Variance Table
##
## Model 1: kelp_avgALTS20 ~ ns(c(1:366), df = 364)
## Model 2: kelp_avgALTS20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.005
## 2 354 295.664 -353 -295.66 171.78 0.06077 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kelp_alt20_spline_m,kelp_alt20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kelp_avgALTS20 ~ ns(c(1:366), df = 11)
## Model 2: kelp_avgALTS20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 295.66
## 2 330 228.37 24 67.292 4.0516 0.0000000031 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kelp_alt30_spline_d,kelp_alt30_spline_10d)## Analysis of Variance Table
##
## Model 1: kelp_avgALTS30 ~ ns(c(1:366), df = 364)
## Model 2: kelp_avgALTS30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.002
## 2 330 134.118 -329 -134.12 164.66 0.06207 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kelp_alt30_spline_d,kelp_alt30_spline_m)## Analysis of Variance Table
##
## Model 1: kelp_avgALTS30 ~ ns(c(1:366), df = 364)
## Model 2: kelp_avgALTS30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.002
## 2 354 170.043 -353 -170.04 194.57 0.05711 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(kelp_alt30_spline_m,kelp_alt30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: kelp_avgALTS30 ~ ns(c(1:366), df = 11)
## Model 2: kelp_avgALTS30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 170.04
## 2 330 134.12 24 35.925 3.6831 0.00000004432 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1KSGU
Temperature
anova(ksgu_t10_spline_d,ksgu_t10_spline_10d)## Analysis of Variance Table
##
## Model 1: ksgu_avgTemp10 ~ ns(c(1:366), df = 364)
## Model 2: ksgu_avgTemp10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.016
## 2 330 290.570 -329 -290.56 55.973 0.1062anova(ksgu_t10_spline_d,ksgu_t10_spline_m)## Analysis of Variance Table
##
## Model 1: ksgu_avgTemp10 ~ ns(c(1:366), df = 364)
## Model 2: ksgu_avgTemp10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.02
## 2 354 431.71 -353 -431.7 77.509 0.09037 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ksgu_t10_spline_m,ksgu_t10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: ksgu_avgTemp10 ~ ns(c(1:366), df = 11)
## Model 2: ksgu_avgTemp10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 431.71
## 2 330 290.57 24 141.14 6.6789 < 0.00000000000000022 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ksgu_t20_spline_d,ksgu_t20_spline_10d)## Analysis of Variance Table
##
## Model 1: ksgu_avgTemp20 ~ ns(c(1:366), df = 364)
## Model 2: ksgu_avgTemp20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.001
## 2 330 155.854 -329 -155.85 460.16 0.03715 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ksgu_t20_spline_d,ksgu_t20_spline_m)## Analysis of Variance Table
##
## Model 1: ksgu_avgTemp20 ~ ns(c(1:366), df = 364)
## Model 2: ksgu_avgTemp20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.001
## 2 354 256.876 -353 -256.88 706.86 0.02998 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ksgu_t20_spline_m,ksgu_t20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: ksgu_avgTemp20 ~ ns(c(1:366), df = 11)
## Model 2: ksgu_avgTemp20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 256.88
## 2 330 155.85 24 101.02 8.9125 < 0.00000000000000022 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ksgu_t30_spline_d,ksgu_t30_spline_10d)## Analysis of Variance Table
##
## Model 1: ksgu_avgTemp30 ~ ns(c(1:366), df = 364)
## Model 2: ksgu_avgTemp30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.00
## 2 330 115.35 -329 -115.35 935.58 0.02606 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ksgu_t30_spline_d,ksgu_t30_spline_m)## Analysis of Variance Table
##
## Model 1: ksgu_avgTemp30 ~ ns(c(1:366), df = 364)
## Model 2: ksgu_avgTemp30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.00
## 2 354 186.42 -353 -186.42 1409.2 0.02124 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ksgu_t30_spline_m,ksgu_t30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: ksgu_avgTemp30 ~ ns(c(1:366), df = 11)
## Model 2: ksgu_avgTemp30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 186.42
## 2 330 115.35 24 71.067 8.4711 < 0.00000000000000022 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Dewpoint Temperature
anova(ksgu_dp10_spline_d,ksgu_dp10_spline_10d) ## Analysis of Variance Table
##
## Model 1: ksgu_avgDPTemp10 ~ ns(c(1:366), df = 364)
## Model 2: ksgu_avgDPTemp10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.47
## 2 330 662.18 -329 -661.71 4.3051 0.3698anova(ksgu_dp10_spline_d,ksgu_dp10_spline_m)## Analysis of Variance Table
##
## Model 1: ksgu_avgDPTemp10 ~ ns(c(1:366), df = 364)
## Model 2: ksgu_avgDPTemp10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.47
## 2 354 881.04 -353 -880.57 5.3395 0.3345anova(ksgu_dp10_spline_m,ksgu_dp10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: ksgu_avgDPTemp10 ~ ns(c(1:366), df = 11)
## Model 2: ksgu_avgDPTemp10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 881.04
## 2 330 662.18 24 218.87 4.5447 0.00000000008689 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ksgu_dp20_spline_d,ksgu_dp20_spline_10d)## Analysis of Variance Table
##
## Model 1: ksgu_avgDPTemp20 ~ ns(c(1:366), df = 364)
## Model 2: ksgu_avgDPTemp20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.082
## 2 330 226.117 -329 -226.03 8.3884 0.2699anova(ksgu_dp20_spline_d,ksgu_dp20_spline_m)## Analysis of Variance Table
##
## Model 1: ksgu_avgDPTemp20 ~ ns(c(1:366), df = 364)
## Model 2: ksgu_avgDPTemp20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.08
## 2 354 347.89 -353 -347.81 12.03 0.2267anova(ksgu_dp20_spline_m,ksgu_dp20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: ksgu_avgDPTemp20 ~ ns(c(1:366), df = 11)
## Model 2: ksgu_avgDPTemp20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 347.89
## 2 330 226.12 24 121.77 7.4049 < 0.00000000000000022 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ksgu_dp30_spline_d,ksgu_dp30_spline_10d)## Analysis of Variance Table
##
## Model 1: ksgu_avgDPTemp30 ~ ns(c(1:366), df = 364)
## Model 2: ksgu_avgDPTemp30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.072
## 2 330 156.240 -329 -156.17 6.623 0.3022anova(ksgu_dp30_spline_d,ksgu_dp30_spline_m)## Analysis of Variance Table
##
## Model 1: ksgu_avgDPTemp30 ~ ns(c(1:366), df = 364)
## Model 2: ksgu_avgDPTemp30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.072
## 2 354 280.250 -353 -280.18 11.074 0.236anova(ksgu_dp30_spline_m,ksgu_dp30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: ksgu_avgDPTemp30 ~ ns(c(1:366), df = 11)
## Model 2: ksgu_avgDPTemp30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 280.25
## 2 330 156.24 24 124.01 10.914 < 0.00000000000000022 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Wind Direction
anova(ksgu_wd10_spline_d,ksgu_wd10_spline_10d)## Analysis of Variance Table
##
## Model 1: ksgu_avgWDIR10 ~ ns(c(1:366), df = 364)
## Model 2: ksgu_avgWDIR10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 3
## 2 330 36405 -329 -36401 32.699 0.1387anova(ksgu_wd10_spline_d,ksgu_wd10_spline_m)## Analysis of Variance Table
##
## Model 1: ksgu_avgWDIR10 ~ ns(c(1:366), df = 364)
## Model 2: ksgu_avgWDIR10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 3
## 2 354 39647 -353 -39643 33.19 0.1377anova(ksgu_wd10_spline_m,ksgu_wd10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: ksgu_avgWDIR10 ~ ns(c(1:366), df = 11)
## Model 2: ksgu_avgWDIR10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 39647
## 2 330 36405 24 3242.1 1.2245 0.2172anova(ksgu_wd20_spline_d,ksgu_wd20_spline_10d)## Analysis of Variance Table
##
## Model 1: ksgu_avgWDIR20 ~ ns(c(1:366), df = 364)
## Model 2: ksgu_avgWDIR20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 16.5
## 2 330 20820.1 -329 -20804 3.8288 0.3903anova(ksgu_wd20_spline_d,ksgu_wd20_spline_m)## Analysis of Variance Table
##
## Model 1: ksgu_avgWDIR20 ~ ns(c(1:366), df = 364)
## Model 2: ksgu_avgWDIR20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 16.5
## 2 354 23471.8 -353 -23455 4.0233 0.3816anova(ksgu_wd20_spline_m,ksgu_wd20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: ksgu_avgWDIR20 ~ ns(c(1:366), df = 11)
## Model 2: ksgu_avgWDIR20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 23472
## 2 330 20820 24 2651.7 1.7513 0.01726 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ksgu_wd30_spline_d,ksgu_wd30_spline_10d)## Analysis of Variance Table
##
## Model 1: ksgu_avgWDIR30 ~ ns(c(1:366), df = 364)
## Model 2: ksgu_avgWDIR30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 11.8
## 2 330 14440.4 -329 -14429 3.7151 0.3958anova(ksgu_wd30_spline_d,ksgu_wd30_spline_m)## Analysis of Variance Table
##
## Model 1: ksgu_avgWDIR30 ~ ns(c(1:366), df = 364)
## Model 2: ksgu_avgWDIR30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 11.8
## 2 354 15796.6 -353 -15785 3.788 0.3923anova(ksgu_wd30_spline_m,ksgu_wd30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: ksgu_avgWDIR30 ~ ns(c(1:366), df = 11)
## Model 2: ksgu_avgWDIR30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 15797
## 2 330 14440 24 1356.1 1.2913 0.1658Wind Speed
anova(ksgu_ws10_spline_d,ksgu_ws10_spline_10d)## Analysis of Variance Table
##
## Model 1: ksgu_avgWSPD10 ~ ns(c(1:366), df = 364)
## Model 2: ksgu_avgWSPD10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.026
## 2 330 78.440 -329 -78.414 9.038 0.2604anova(ksgu_ws10_spline_d,ksgu_ws10_spline_m)## Analysis of Variance Table
##
## Model 1: ksgu_avgWSPD10 ~ ns(c(1:366), df = 364)
## Model 2: ksgu_avgWSPD10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.026
## 2 354 85.957 -353 -85.931 9.231 0.2578anova(ksgu_ws10_spline_m,ksgu_ws10_spline_10d)## Analysis of Variance Table
##
## Model 1: ksgu_avgWSPD10 ~ ns(c(1:366), df = 11)
## Model 2: ksgu_avgWSPD10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 85.957
## 2 330 78.440 24 7.5168 1.3176 0.1483anova(ksgu_ws20_spline_d,ksgu_ws20_spline_10d)## Analysis of Variance Table
##
## Model 1: ksgu_avgWSPD20 ~ ns(c(1:366), df = 364)
## Model 2: ksgu_avgWSPD20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.014
## 2 330 41.716 -329 -41.702 9.132 0.2591anova(ksgu_ws20_spline_d,ksgu_ws20_spline_m)## Analysis of Variance Table
##
## Model 1: ksgu_avgWSPD20 ~ ns(c(1:366), df = 364)
## Model 2: ksgu_avgWSPD20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.014
## 2 354 45.465 -353 -45.451 9.2764 0.2571anova(ksgu_ws20_spline_m,ksgu_ws20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: ksgu_avgWSPD20 ~ ns(c(1:366), df = 11)
## Model 2: ksgu_avgWSPD20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 45.465
## 2 330 41.716 24 3.7497 1.236 0.2076anova(ksgu_ws30_spline_d,ksgu_ws30_spline_10d)## Analysis of Variance Table
##
## Model 1: ksgu_avgWSPD30 ~ ns(c(1:366), df = 364)
## Model 2: ksgu_avgWSPD30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.0137
## 2 330 27.4675 -329 -27.454 6.0689 0.3149anova(ksgu_ws30_spline_d,ksgu_ws30_spline_m)## Analysis of Variance Table
##
## Model 1: ksgu_avgWSPD30 ~ ns(c(1:366), df = 364)
## Model 2: ksgu_avgWSPD30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.0137
## 2 354 30.0419 -353 -30.028 6.1867 0.3121anova(ksgu_ws30_spline_m,ksgu_ws30_spline_10d)## Analysis of Variance Table
##
## Model 1: ksgu_avgWSPD30 ~ ns(c(1:366), df = 11)
## Model 2: ksgu_avgWSPD30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 30.042
## 2 330 27.468 24 2.5745 1.2888 0.1676Wind Gust Speed
anova(ksgu_wg10_spline_d,ksgu_wg10_spline_10d)## Analysis of Variance Table
##
## Model 1: ksgu_avgWGSP10 ~ ns(c(1:366), df = 364)
## Model 2: ksgu_avgWGSP10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.602
## 2 330 104.342 -329 -103.74 0.5235 0.8321anova(ksgu_wg10_spline_d,ksgu_wg10_spline_m)## Analysis of Variance Table
##
## Model 1: ksgu_avgWGSP10 ~ ns(c(1:366), df = 364)
## Model 2: ksgu_avgWGSP10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.602
## 2 354 131.313 -353 -130.71 0.6147 0.797anova(ksgu_wg10_spline_m,ksgu_wg10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: ksgu_avgWGSP10 ~ ns(c(1:366), df = 11)
## Model 2: ksgu_avgWGSP10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 131.31
## 2 330 104.34 24 26.97 3.5541 0.0000001119 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ksgu_wg20_spline_d,ksgu_wg20_spline_10d)## Analysis of Variance Table
##
## Model 1: ksgu_avgWGSP20 ~ ns(c(1:366), df = 364)
## Model 2: ksgu_avgWGSP20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.020
## 2 330 64.104 -329 -64.084 9.6373 0.2524anova(ksgu_wg20_spline_d,ksgu_wg20_spline_m)## Analysis of Variance Table
##
## Model 1: ksgu_avgWGSP20 ~ ns(c(1:366), df = 364)
## Model 2: ksgu_avgWGSP20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.020
## 2 354 80.707 -353 -80.687 11.309 0.2336anova(ksgu_wg20_spline_m,ksgu_wg20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: ksgu_avgWGSP20 ~ ns(c(1:366), df = 11)
## Model 2: ksgu_avgWGSP20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 80.707
## 2 330 64.104 24 16.603 3.5612 0.0000001064 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ksgu_wg30_spline_d,ksgu_wg30_spline_10d)## Analysis of Variance Table
##
## Model 1: ksgu_avgWGSP30 ~ ns(c(1:366), df = 364)
## Model 2: ksgu_avgWGSP30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.007
## 2 330 45.416 -329 -45.409 19.541 0.1788anova(ksgu_wg30_spline_d,ksgu_wg30_spline_m)## Analysis of Variance Table
##
## Model 1: ksgu_avgWGSP30 ~ ns(c(1:366), df = 364)
## Model 2: ksgu_avgWGSP30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.007
## 2 354 58.565 -353 -58.558 23.486 0.1634anova(ksgu_wg30_spline_m,ksgu_wg30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: ksgu_avgWGSP30 ~ ns(c(1:366), df = 11)
## Model 2: ksgu_avgWGSP30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 58.565
## 2 330 45.416 24 13.149 3.981 0.000000005167 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Average Cloud Ceiling Height
anova(ksgu_cig10_spline_d,ksgu_cig10_spline_10d)## Analysis of Variance Table
##
## Model 1: ksgu_avgCCIG10 ~ ns(c(1:366), df = 364)
## Model 2: ksgu_avgCCIG10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 180108
## 2 330 789765324 -329 -789585216 13.325 0.2157anova(ksgu_cig10_spline_d,ksgu_cig10_spline_m)## Analysis of Variance Table
##
## Model 1: ksgu_avgCCIG10 ~ ns(c(1:366), df = 364)
## Model 2: ksgu_avgCCIG10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 180108
## 2 354 846938793 -353 -846758685 13.318 0.2158anova(ksgu_cig10_spline_m,ksgu_cig10_spline_10d)## Analysis of Variance Table
##
## Model 1: ksgu_avgCCIG10 ~ ns(c(1:366), df = 11)
## Model 2: ksgu_avgCCIG10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 846938793
## 2 330 789765324 24 57173469 0.9954 0.4717anova(ksgu_cig20_spline_d,ksgu_cig20_spline_10d)## Analysis of Variance Table
##
## Model 1: ksgu_avgCCIG20 ~ ns(c(1:366), df = 364)
## Model 2: ksgu_avgCCIG20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 38696
## 2 330 355326592 -329 -355287896 27.907 0.15anova(ksgu_cig20_spline_d,ksgu_cig20_spline_m)## Analysis of Variance Table
##
## Model 1: ksgu_avgCCIG20 ~ ns(c(1:366), df = 364)
## Model 2: ksgu_avgCCIG20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 38696
## 2 354 419269079 -353 -419230383 30.691 0.1431anova(ksgu_cig20_spline_m,ksgu_cig20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: ksgu_avgCCIG20 ~ ns(c(1:366), df = 11)
## Model 2: ksgu_avgCCIG20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 419269079
## 2 330 355326592 24 63942486 2.4744 0.0002027 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ksgu_cig30_spline_d,ksgu_cig30_spline_10d)## Analysis of Variance Table
##
## Model 1: ksgu_avgCCIG30 ~ ns(c(1:366), df = 364)
## Model 2: ksgu_avgCCIG30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 24205
## 2 330 238893584 -329 -238869379 29.996 0.1448anova(ksgu_cig30_spline_d,ksgu_cig30_spline_m)## Analysis of Variance Table
##
## Model 1: ksgu_avgCCIG30 ~ ns(c(1:366), df = 364)
## Model 2: ksgu_avgCCIG30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 24205
## 2 354 293487902 -353 -293463697 34.346 0.1354anova(ksgu_cig30_spline_m,ksgu_cig30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: ksgu_avgCCIG30 ~ ns(c(1:366), df = 11)
## Model 2: ksgu_avgCCIG30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 293487902
## 2 330 238893584 24 54594319 3.1423 0.000002089 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Average Visibility
anova(ksgu_vis10_spline_d,ksgu_vis10_spline_10d)## Analysis of Variance Table
##
## Model 1: ksgu_avgVIS10 ~ ns(c(1:366), df = 364)
## Model 2: ksgu_avgVIS10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 4249
## 2 330 23208038 -329 -23203789 16.599 0.1937anova(ksgu_vis10_spline_d,ksgu_vis10_spline_m)## Analysis of Variance Table
##
## Model 1: ksgu_avgVIS10 ~ ns(c(1:366), df = 364)
## Model 2: ksgu_avgVIS10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 4249
## 2 354 27597477 -353 -27593228 18.396 0.1842anova(ksgu_vis10_spline_m,ksgu_vis10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: ksgu_avgVIS10 ~ ns(c(1:366), df = 11)
## Model 2: ksgu_avgVIS10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 27597477
## 2 330 23208038 24 4389439 2.6006 0.00008743 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ksgu_vis20_spline_d,ksgu_vis20_spline_10d)## Analysis of Variance Table
##
## Model 1: ksgu_avgVIS20 ~ ns(c(1:366), df = 364)
## Model 2: ksgu_avgVIS20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 855
## 2 330 8799757 -329 -8798902 31.268 0.1418anova(ksgu_vis20_spline_d,ksgu_vis20_spline_m)## Analysis of Variance Table
##
## Model 1: ksgu_avgVIS20 ~ ns(c(1:366), df = 364)
## Model 2: ksgu_avgVIS20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 855
## 2 354 10038808 -353 -10037953 33.246 0.1376anova(ksgu_vis20_spline_m,ksgu_vis20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: ksgu_avgVIS20 ~ ns(c(1:366), df = 11)
## Model 2: ksgu_avgVIS20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 10038808
## 2 330 8799757 24 1239051 1.9361 0.005992 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ksgu_vis30_spline_d,ksgu_vis30_spline_10d)## Analysis of Variance Table
##
## Model 1: ksgu_avgVIS30 ~ ns(c(1:366), df = 364)
## Model 2: ksgu_avgVIS30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 175
## 2 330 6010099 -329 -6009925 104.59 0.07783 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ksgu_vis30_spline_d,ksgu_vis30_spline_m)## Analysis of Variance Table
##
## Model 1: ksgu_avgVIS30 ~ ns(c(1:366), df = 364)
## Model 2: ksgu_avgVIS30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 175
## 2 354 6796005 -353 -6795830 110.23 0.07583 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ksgu_vis30_spline_m,ksgu_vis30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: ksgu_avgVIS30 ~ ns(c(1:366), df = 11)
## Model 2: ksgu_avgVIS30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 6796005
## 2 330 6010099 24 785906 1.798 0.0133 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Average Cloud Cover
anova(ksgu_cc10_spline_d,ksgu_cc10_spline_10d)## Analysis of Variance Table
##
## Model 1: ksgu_avgCCOV10 ~ ns(c(1:366), df = 364)
## Model 2: ksgu_avgCCOV10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.0089
## 2 330 26.9284 -329 -26.919 9.2308 0.2577anova(ksgu_cc10_spline_d,ksgu_cc10_spline_m)## Analysis of Variance Table
##
## Model 1: ksgu_avgCCOV10 ~ ns(c(1:366), df = 364)
## Model 2: ksgu_avgCCOV10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.009
## 2 354 31.797 -353 -31.788 10.159 0.2461anova(ksgu_cc10_spline_m,ksgu_cc10_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: ksgu_avgCCOV10 ~ ns(c(1:366), df = 11)
## Model 2: ksgu_avgCCOV10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 31.797
## 2 330 26.928 24 4.8684 2.4858 0.0001879 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ksgu_cc20_spline_d,ksgu_cc20_spline_10d)## Analysis of Variance Table
##
## Model 1: ksgu_avgCCOV20 ~ ns(c(1:366), df = 364)
## Model 2: ksgu_avgCCOV20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.007
## 2 330 42.063 -329 -42.055 17.849 0.187anova(ksgu_cc20_spline_d,ksgu_cc20_spline_m)## Analysis of Variance Table
##
## Model 1: ksgu_avgCCOV20 ~ ns(c(1:366), df = 364)
## Model 2: ksgu_avgCCOV20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.007
## 2 354 52.070 -353 -52.062 20.594 0.1743anova(ksgu_cc20_spline_m,ksgu_cc20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: ksgu_avgCCOV20 ~ ns(c(1:366), df = 11)
## Model 2: ksgu_avgCCOV20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 52.070
## 2 330 42.063 24 10.007 3.2712 0.0000008404 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ksgu_cc30_spline_d,ksgu_cc30_spline_10d)## Analysis of Variance Table
##
## Model 1: ksgu_avgCCOV30 ~ ns(c(1:366), df = 364)
## Model 2: ksgu_avgCCOV30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.0041
## 2 330 28.9134 -329 -28.909 21.561 0.1704anova(ksgu_cc30_spline_d,ksgu_cc30_spline_m)## Analysis of Variance Table
##
## Model 1: ksgu_avgCCOV30 ~ ns(c(1:366), df = 364)
## Model 2: ksgu_avgCCOV30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.004
## 2 354 36.409 -353 -36.405 25.306 0.1575anova(ksgu_cc30_spline_m,ksgu_cc30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: ksgu_avgCCOV30 ~ ns(c(1:366), df = 11)
## Model 2: ksgu_avgCCOV30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 36.409
## 2 330 28.913 24 7.496 3.5648 0.0000001036 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Average Altimeter Setting
anova(ksgu_alt10_spline_d,ksgu_alt10_spline_10d)## Analysis of Variance Table
##
## Model 1: ksgu_avgALTS10 ~ ns(c(1:366), df = 364)
## Model 2: ksgu_avgALTS10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.02
## 2 330 913.61 -329 -913.58 118.82 0.07304 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ksgu_alt10_spline_d,ksgu_alt10_spline_m)## Analysis of Variance Table
##
## Model 1: ksgu_avgALTS10 ~ ns(c(1:366), df = 364)
## Model 2: ksgu_avgALTS10 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.02
## 2 354 1011.05 -353 -1011 122.55 0.07193 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ksgu_alt10_spline_m,ksgu_alt10_spline_10d)## Analysis of Variance Table
##
## Model 1: ksgu_avgALTS10 ~ ns(c(1:366), df = 11)
## Model 2: ksgu_avgALTS10 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 1011.05
## 2 330 913.61 24 97.439 1.4665 0.07551 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ksgu_alt20_spline_d,ksgu_alt20_spline_10d)## Analysis of Variance Table
##
## Model 1: ksgu_avgALTS20 ~ ns(c(1:366), df = 364)
## Model 2: ksgu_avgALTS20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.00
## 2 330 415.76 -329 -415.76 3519.6 0.01344 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ksgu_alt20_spline_d,ksgu_alt20_spline_m)## Analysis of Variance Table
##
## Model 1: ksgu_avgALTS20 ~ ns(c(1:366), df = 364)
## Model 2: ksgu_avgALTS20 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.00
## 2 354 480.06 -353 -480.06 3787.6 0.01295 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ksgu_alt20_spline_m,ksgu_alt20_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: ksgu_avgALTS20 ~ ns(c(1:366), df = 11)
## Model 2: ksgu_avgALTS20 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 480.06
## 2 330 415.76 24 64.298 2.1265 0.001891 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ksgu_alt30_spline_d,ksgu_alt30_spline_10d)## Analysis of Variance Table
##
## Model 1: ksgu_avgALTS30 ~ ns(c(1:366), df = 364)
## Model 2: ksgu_avgALTS30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.001
## 2 330 262.496 -329 -262.5 994.71 0.02527 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ksgu_alt30_spline_d,ksgu_alt30_spline_m)## Analysis of Variance Table
##
## Model 1: ksgu_avgALTS30 ~ ns(c(1:366), df = 364)
## Model 2: ksgu_avgALTS30 ~ ns(c(1:366), df = 11)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 1 0.001
## 2 354 303.595 -353 -303.59 1072.2 0.02435 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(ksgu_alt30_spline_m,ksgu_alt30_spline_10d) # this one## Analysis of Variance Table
##
## Model 1: ksgu_avgALTS30 ~ ns(c(1:366), df = 11)
## Model 2: ksgu_avgALTS30 ~ ns(c(1:366), df = 35)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 354 303.6
## 2 330 262.5 24 41.098 2.1528 0.001605 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ANOVA Comparison Table
Note:
GREEN - Shows high statistical significance.
ORANGE - Shows marginal statistical significance. Approaching P(>F) ~ 5%
RED - Shows low/no statistical significance
| VARIABLE | KRDM | KBUF | KFOE | KMSN | KTRI | PAJN | KELP | KSGU |
|---|---|---|---|---|---|---|---|---|
| Temp (10-day/Monthly) | Y | Y | Y | Y | Y | Y | Y | Y |
| Temp (10-day/Daily) | N | N | N | N | M | N | N | M |
| Temp (Daily/Monthly) | N | N | N | N | M | N | N | M |
| DP Temp (10-day/Monthly) | Y | Y | Y | Y | Y | Y | Y | Y |
| DP Temp (10-day/Daily) | N | M | M | M | M | N | N | N |
| DP Temp (Daily/Monthly) | N | M | M | M | M | N | N | N |
| WD (10-day/Monthly) | Y | M | Y | Y | Y | Y | Y | M |
| WD (10-day/Daily) | N | N | N | N | N | N | N | N |
| WD (Daily/Monthly) | N | N | N | N | N | N | N | N |
| WS (10-day/Monthly) | Y | M | M | N | M | Y | N | N |
| WS (10-day/Daily) | N | N | N | N | M | N | N | N |
| WS (Daily/Monthly) | N | N | N | N | N | N | N | N |
| CIG (10-day/Monthly) | Y | Y | Y | M | Y | Y | Y | Y |
| CIG (10-day/Daily) | N | N | N | N | N | N | N | N |
| CIG (Daily/Monthly) | N | N | N | N | N | N | N | N |
| VIS (10-day/Monthly) | Y | M | Y | M | Y | Y | M | Y |
| VIS (10-day/Daily) | N | M | N | M | N | N | N | N |
| VIS (Daily/Monthly) | N | M | N | M | N | N | N | N |
| CC (10-day/Monthly) | Y | Y | Y | Y | Y | Y | Y | Y |
| CC (10-day/Daily) | M | N | N | N | N | N | N | N |
| CC (Daily/Monthly) | M | N | N | N | N | N | N | N |
| ALT (10-day/Monthly) | Y | Y | Y | Y | Y | Y | Y | M |
| ALT (10-day/Daily) | M | M | N | M | N | M | N | M |
| ALT (Daily/Monthly) | M | M | N | M | N | M | N | M |
Significant Spline Comparisons
KRDM
Temperature
10 year Monthly vs. 10-day Temperature
Spline
20 year Monthly vs. 10-day Temperature
Spline
30 year Monthly vs. 10-day Temperature
Spline
Dewpoint Temperature
10 year Monthly vs. 10-day Dewpoint Temperature
Spline
20 year Monthly vs. 10-day Dewpoint Temperature
Spline
30 year Monthly vs. 10-day Dewpoint Temperature
Spline
Wind Direction
10 year Monthly vs. 10-day Wind Direction
Spline
20 year Monthly vs. 10-day Wind Direction
Spline
30 year Monthly vs. 10-day Wind Direction
Spline
Wind Speed
10 year Monthly vs. 10-day Wind Speed
Spline
20 year Monthly vs. 10-day Wind Speed
Spline
30 year Monthly vs. 10-day Wind Speed
Spline
Wind Gust Speed
10 year Monthly vs. 10-day Wind Gust
Spline
20 year Monthly vs. 10-day Wind Gust
Spline
30 year Monthly vs. 10-day Wind Gust
Spline
Average Cloud Ceiling Height
10 year Monthly vs. 10-day Cloud Ceiling
Spline
20 year Monthly vs. 10-day Cloud Ceiling
Spline
30 year Monthly vs. 10-day Cloud Ceiling
Spline
Average Visibility
10 year Monthly vs. 10-day Visibility
Spline
20 year Monthly vs. 10-day Visibility
Spline
30 year Monthly vs. 10-day Visibility
Spline
Average Cloud Cover
10 year Monthly vs. 10-day Cloud Cover
Spline
20 year Monthly vs. 10-day Cloud Cover
Spline
30 year Monthly vs. 10-day Cloud Cover
Spline
Average Altimeter Setting
10 year Monthly vs. 10-day Altimeter
Spline
KBUF
Temperature
10 year Monthly vs. 10-day Temperature
Spline
20 year Monthly vs. 10-day Temperature
Spline
30 year Monthly vs. 10-day Temperature
Spline
Dewpoint Temperature
10 year Monthly vs. 10-day Dewpoint Temperature
Spline
20 year Monthly vs. 10-day Dewpoint Temperature
Spline
30 year Monthly vs. 10-day Dewpoint Temperature
Spline
Wind Direction
10 year Monthly vs. 10-day Wind Direction
Spline
Wind Speed
10 year Monthly vs. 10-day Wind Speed
Spline
20 year Monthly vs. 10-day Wind Speed
Spline
Wind Gust Speed
10 year Monthly vs. 10-day Wind Gust
Spline
20 year Monthly vs. 10-day Wind Gust
Spline
30 year Monthly vs. 10-day Wind Gust
Spline
Average Cloud Ceiling Height
10 year Monthly vs. 10-day Cloud Ceiling
Spline
20 year Monthly vs. 10-day Cloud Ceiling
Spline
30 year Monthly vs. 10-day Cloud Ceiling
Spline
Average Visibility
20 year Monthly vs. 10-day Visibility
Spline
30 year Monthly vs. 10-day Visibility
Spline
Average Cloud Cover
10 year Monthly vs. 10-day Cloud Cover
Spline
20 year Monthly vs. 10-day Cloud Cover
Spline
30 year Monthly vs. 10-day Cloud Cover
Spline
Average Altimeter Setting
10 year Monthly vs. 10-day Altimeter
Spline
20 year Monthly vs. 10-day Altimeter
Spline
30 year Monthly vs. 10-day Altimeter
Spline
KFOE
Temperature
10 year Monthly vs. 10-day Temperature
Spline
20 year Monthly vs. 10-day Temperature
Spline
30 year Monthly vs. 10-day Temperature
Spline
Dewpoint Temperature
10 year Monthly vs. 10-day Dewpoint Temperature
Spline
20 year Monthly vs. 10-day Dewpoint Temperature
Spline
30 year Monthly vs. 10-day Dewpoint Temperature
Spline
Wind Direction
10 year Monthly vs. 10-day Wind Direction
Spline
20 year Monthly vs. 10-day Wind Direction
Spline
30 year Monthly vs. 10-day Wind Direction
Spline
Wind Speed
10 year Monthly vs. 10-day Wind Speed
Spline
30 year Monthly vs. 10-day Wind Speed
Spline
Wind Gust Speed
10 year Monthly vs. 10-day Wind Gust
Spline
20 year Monthly vs. 10-day Wind Gust
Spline
30 year Monthly vs. 10-day Wind Gust
Spline
Average Cloud Ceiling Height
10 year Monthly vs. 10-day Cloud Ceiling
Spline
20 year Monthly vs. 10-day Cloud Ceiling
Spline
30 year Monthly vs. 10-day Cloud Ceiling
Spline
Average Visibility
10 year Monthly vs. 10-day Visibility
Spline
20 year Monthly vs. 10-day Visibility
Spline
30 year Monthly vs. 10-day Visibility
Spline
Average Cloud Cover
10 year Monthly vs. 10-day Cloud Cover
Spline
20 year Monthly vs. 10-day Cloud Cover
Spline
30 year Monthly vs. 10-day Cloud Cover
Spline
Average Altimeter Setting
10 year Monthly vs. 10-day Altimeter
Spline
20 year Monthly vs. 10-day Altimeter
Spline
30 year Monthly vs. 10-day Altimeter
Spline
KMSN
Temperature
10 year Monthly vs. 10-day Temperature
Spline
20 year Monthly vs. 10-day Temperature
Spline
30 year Monthly vs. 10-day Temperature
Spline
Dewpoint Temperature
10 year Monthly vs. 10-day Dewpoint Temperature
Spline
20 year Monthly vs. 10-day Dewpoint Temperature
Spline
30 year Monthly vs. 10-day Dewpoint Temperature
Spline
Wind Direction
10 year Monthly vs. 10-day Wind Direction
Spline
20 year Monthly vs. 10-day Wind Direction
Spline
30 year Monthly vs. 10-day Wind Direction
Spline
Wind Speed
10 year Monthly vs. 10-day Wind Speed
Spline
20 year Monthly vs. 10-day Wind Speed
Spline
Wind Gust Speed
10 year Monthly vs. 10-day Wind Gust
Spline
20 year Monthly vs. 10-day Wind Gust
Spline
30 year Monthly vs. 10-day Wind Gust
Spline
Average Cloud Ceiling Height
10 year Monthly vs. 10-day Cloud Ceiling
Spline
20 year Monthly vs. 10-day Cloud Ceiling
Spline
30 year Monthly vs. 10-day Cloud Ceiling
Spline
Average Visibility
10 year Monthly vs. 10-day Visibility
Spline
20 year Monthly vs. Daily Visibility
Spline
30 year Monthly vs. 10-day Visibility
Spline
Average Cloud Cover
10 year Monthly vs. 10-day Cloud Cover
Spline
20 year Monthly vs. 10-day Cloud Cover
Spline
30 year Monthly vs. 10-day Cloud Cover
Spline
Average Altimeter Setting
10 year Monthly vs. 10-day Altimeter
Spline
20 year Monthly vs. 10-day Altimeter
Spline
30 year Monthly vs. 10-day Altimeter
Spline
KTRI
Temperature
10 year Monthly vs. 10-day Temperature
Spline
20 year Monthly vs. 10-day Temperature
Spline
30 year Monthly vs. 10-day Temperature
Spline
Dewpoint Temperature
10 year Monthly vs. 10-day Dewpoint Temperature
Spline
20 year Monthly vs. 10-day Dewpoint Temperature
Spline
30 year Monthly vs. 10-day Dewpoint Temperature
Spline
Wind Direction
10 year Monthly vs. 10-day Wind Direction
Spline
20 year Monthly vs. 10-day Wind Direction
Spline
30 year Monthly vs. 10-day Wind Direction
Spline
Wind Speed
20 year Monthly vs. 10-day Wind Speed
Spline
Wind Gust Speed
10 year Monthly vs. 10-day Wind Gust
Spline
20 year Monthly vs. 10-day Wind Gust
Spline
30 year Monthly vs. 10-day Wind Gust
Spline
Average Cloud Ceiling Height
10 year Monthly vs. 10-day Cloud Ceiling
Spline
20 year Monthly vs. 10-day Cloud Ceiling
Spline
30 year Monthly vs. 10-day Cloud Ceiling
Spline
Average Visibility
10 year Monthly vs. 10-day Visibility
Spline
20 year Monthly vs. 10-day Visibility
Spline
Average Cloud Cover
10 year Monthly vs. 10-day Cloud Cover
Spline
20 year Monthly vs. 10-day Cloud Cover
Spline
30 year Monthly vs. 10-day Cloud Cover
Spline
Average Altimeter Setting
10 year Monthly vs. 10-day Altimeter
Spline
20 year Monthly vs. 10-day Altimeter
Spline
30 year Monthly vs. 10-day Altimeter
Spline
PAJN
Temperature
10 year Monthly vs. 10-day Temperature
Spline
20 year Monthly vs. 10-day Temperature
Spline
30 year Monthly vs. 10-day Temperature
Spline
Dewpoint Temperature
10 year Monthly vs. 10-day Dewpoint Temperature
Spline
20 year Monthly vs. 10-day Dewpoint Temperature
Spline
30 year Monthly vs. 10-day Dewpoint Temperature
Spline
Wind Direction
10 year Monthly vs. 10-day Wind Direction
Spline
20 year Monthly vs. 10-day Wind Direction
Spline
30 year Monthly vs. 10-day Wind Direction
Spline
Wind Speed
10 year Monthly vs. 10-day Wind Speed
Spline
20 year Monthly vs. 10-day Wind Speed
Spline
30 year Monthly vs. 10-day Wind Speed
Spline
Wind Gust Speed
10 year Monthly vs. 10-day Wind Gust
Spline
20 year Monthly vs. 10-day Wind Gust
Spline
30 year Monthly vs. 10-day Wind Gust
Spline
Average Cloud Ceiling Height
10 year Monthly vs. 10-day Cloud Ceiling
Spline
20 year Monthly vs. 10-day Cloud Ceiling
Spline
30 year Monthly vs. 10-day Cloud Ceiling
Spline
Average Visibility
10 year Monthly vs. 10-day Visibility
Spline
20 year Monthly vs. 10-day Visibility
Spline
30 year Monthly vs. 10-day Visibility
Spline
Average Cloud Cover
10 year Monthly vs. 10-day Cloud Cover
Spline
20 year Monthly vs. 10-day Cloud Cover
Spline
30 year Monthly vs. 10-day Cloud Cover
Spline
Average Altimeter Setting
10 year Monthly vs. 10-day Altimeter
Spline
20 year Monthly vs. 10-day Altimeter
Spline
30 year Monthly vs. 10-day Altimeter
Spline
KELP
Temperature
10 year Monthly vs. 10-day Temperature
Spline
20 year Monthly vs. 10-day Temperature
Spline
30 year Monthly vs. 10-day Temperature
Spline
Dewpoint Temperature
10 year Monthly vs. 10-day Dewpoint Temperature
Spline
20 year Monthly vs. 10-day Dewpoint Temperature
Spline
30 year Monthly vs. 10-day Dewpoint Temperature
Spline
Wind Direction
10 year Monthly vs. 10-day Wind Direction
Spline
20 year Monthly vs. 10-day Wind Direction
Spline
30 year Monthly vs. 10-day Wind Direction
Spline
Wind Speed
30 year Monthly vs. 10-day Wind Speed
Spline
Wind Gust Speed
10 year Monthly vs. 10-day Wind Gust
Spline
Average Cloud Ceiling Height
10 year Monthly vs. 10-day Cloud Ceiling
Spline
20 year Monthly vs. 10-day Cloud Ceiling
Spline
30 year Monthly vs. 10-day Cloud Ceiling
Spline
Average Visibility
20 year Monthly vs. 10-day Visibility
Spline
30 year Monthly vs. 10-day Visibility
Spline
Average Cloud Cover
10 year Monthly vs. 10-day Cloud Cover
Spline
20 year Monthly vs. 10-day Cloud Cover
Spline
30 year Monthly vs. 10-day Cloud Cover
Spline
Average Altimeter Setting
10 year Monthly vs. 10-day Altimeter
Spline
20 year Monthly vs. 10-day Altimeter
Spline
30 year Monthly vs. 10-day Altimeter
Spline
KSGU
Temperature
10 year Monthly vs. 10-day Temperature
Spline
20 year Monthly vs. 10-day Temperature
Spline
30 year Monthly vs. 10-day Temperature
Spline
Dewpoint Temperature
10 year Monthly vs. 10-day Dewpoint Temperature
Spline
20 year Monthly vs. 10-day Dewpoint Temperature
Spline
30 year Monthly vs. 10-day Dewpoint Temperature
Spline
Wind Direction
10 year Monthly vs. 10-day Wind Direction
Spline
20 year Monthly vs. 10-day Wind Direction
Spline
30 year Monthly vs. 10-day Wind Direction
Spline
Wind Speed
20 year Monthly vs. 10-day Wind Speed
Spline
Wind Gust Speed
10 year Monthly vs. 10-day Wind Gust
Spline
20 year Monthly vs. 10-day Wind Gust
Spline
30 year Monthly vs. 10-day Wind Gust
Spline
Average Cloud Ceiling Height
20 year Monthly vs. 10-day Cloud Ceiling
Spline
30 year Monthly vs. 10-day Cloud Ceiling
Spline
Average Visibility
10 year Monthly vs. 10-day Visibility
Spline
20 year Monthly vs. 10-day Visibility
Spline
30 year Monthly vs. 10-day Visibility
Spline
Average Cloud Cover
10 year Monthly vs. 10-day Cloud Cover
Spline
20 year Monthly vs. 10-day Cloud Cover
Spline
30 year Monthly vs. 10-day Cloud Cover
Spline
Average Altimeter Setting
20 year Monthly vs. 10-day Altimeter
Spline
30 year Monthly vs. 10-day Altimeter
Spline
Domain 5
Modeling
Adjusted R-squared Comparison
Due to the amount of datapoints, the adjusted R-squared value is utilized in comparing the fit of the splines. We find that the Daily spline over-fit the data, and statistically, this is not as useful. Therefore, when comparing the 10-day vs. Monthly splines, we can see that the 10-day spline, typically outperforms the monthly in every metric.
It appears that 30-years of data is most accurate. However, this does not allow for rapid changes in data. 10-years of data may be the optimal selection. Additionally, it is seen that there are variables and locations that perform much better with 20 years of data than 10. Rather than saying 20 or 30 years should be used for all variables, for the under-performing variables, 20-years of data should be considered for additional data for better fitting.
for(i in 1:length(r_squared.df$station)) {
# 10 year 10-day and monthly comparison
if(r_squared.df$ten_yr_monthly[i]>r_squared.df$ten_yr_10day[i]){
print(paste0(r_squared.df$station[i]," 10-year ",r_squared.df$vars[i]," monthly adjusted r-squared value better than 10-day: ", round(r_squared.df$ten_yr_monthly[i],4)))
} else {
print(paste0(r_squared.df$station[i]," 10-year ",r_squared.df$vars[i]," 10-day adjusted r-squared value better than monthly: ", round(r_squared.df$ten_yr_10day[i],4)))
}
# 20 year 10-day and monthly comparison
if(r_squared.df$twenty_yr_monthly[i]>r_squared.df$twenty_yr_10day[i]){
print(paste0(r_squared.df$station[i]," 20-year ",r_squared.df$vars[i]," monthly adjusted r-squared value better than 10-day: ", round(r_squared.df$twenty_yr_monthly[i],4)))
} else {
print(paste0(r_squared.df$station[i]," 20-year ",r_squared.df$vars[i]," 10-day adjusted r-squared value better than monthly: ", round(r_squared.df$twenty_yr_10day[i],4)))
}
# 30 year 10-day and monthly comparison
if(r_squared.df$thirty_yr_monthly[i]>r_squared.df$thirty_yr_10day[i]){
print(paste0(r_squared.df$station[i]," 30-year ",r_squared.df$vars[i]," monthly adjusted r-squared value better than 10-day: ", round(r_squared.df$thirty_yr_monthly[i],4)))
} else {
print(paste0(r_squared.df$station[i]," 30-year ",r_squared.df$vars[i]," 10-day adjusted r-squared value better than monthly: ", round(r_squared.df$thirty_yr_10day[i],4)))
}
}## [1] "KRDM 10-year Temperature 10-day adjusted r-squared value better than monthly: 0.9774"
## [1] "KRDM 20-year Temperature 10-day adjusted r-squared value better than monthly: 0.988"
## [1] "KRDM 30-year Temperature 10-day adjusted r-squared value better than monthly: 0.9916"
## [1] "KRDM 10-year Dewpoint Temperature 10-day adjusted r-squared value better than monthly: 0.9158"
## [1] "KRDM 20-year Dewpoint Temperature 10-day adjusted r-squared value better than monthly: 0.9513"
## [1] "KRDM 30-year Dewpoint Temperature 10-day adjusted r-squared value better than monthly: 0.9682"
## [1] "KRDM 10-year Wind Direction 10-day adjusted r-squared value better than monthly: 0.5491"
## [1] "KRDM 20-year Wind Direction 10-day adjusted r-squared value better than monthly: 0.6959"
## [1] "KRDM 30-year Wind Direction 10-day adjusted r-squared value better than monthly: 0.7417"
## [1] "KRDM 10-year Wind Speed 10-day adjusted r-squared value better than monthly: 0.3405"
## [1] "KRDM 20-year Wind Speed 10-day adjusted r-squared value better than monthly: 0.5418"
## [1] "KRDM 30-year Wind Speed 10-day adjusted r-squared value better than monthly: 0.5973"
## [1] "KRDM 10-year Wind Gust Speed 10-day adjusted r-squared value better than monthly: 0.5292"
## [1] "KRDM 20-year Wind Gust Speed 10-day adjusted r-squared value better than monthly: 0.5181"
## [1] "KRDM 30-year Wind Gust Speed 10-day adjusted r-squared value better than monthly: 0.5755"
## [1] "KRDM 10-year Cloud Ceiling 10-day adjusted r-squared value better than monthly: 0.7501"
## [1] "KRDM 20-year Cloud Ceiling 10-day adjusted r-squared value better than monthly: 0.8619"
## [1] "KRDM 30-year Cloud Ceiling 10-day adjusted r-squared value better than monthly: 0.9065"
## [1] "KRDM 10-year Visibility 10-day adjusted r-squared value better than monthly: 0.5538"
## [1] "KRDM 20-year Visibility 10-day adjusted r-squared value better than monthly: 0.7137"
## [1] "KRDM 30-year Visibility 10-day adjusted r-squared value better than monthly: 0.7852"
## [1] "KRDM 10-year Cloud Cover 10-day adjusted r-squared value better than monthly: 0.6812"
## [1] "KRDM 20-year Cloud Cover 10-day adjusted r-squared value better than monthly: 0.8517"
## [1] "KRDM 30-year Cloud Cover 10-day adjusted r-squared value better than monthly: 0.9098"
## [1] "KRDM 10-year Altimeter 10-day adjusted r-squared value better than monthly: 0.3371"
## [1] "KRDM 20-year Altimeter 10-day adjusted r-squared value better than monthly: 0.4607"
## [1] "KRDM 30-year Altimeter 10-day adjusted r-squared value better than monthly: 0.4812"
## [1] "KBUF 10-year Temperature 10-day adjusted r-squared value better than monthly: 0.9845"
## [1] "KBUF 20-year Temperature 10-day adjusted r-squared value better than monthly: 0.993"
## [1] "KBUF 30-year Temperature 10-day adjusted r-squared value better than monthly: 0.9948"
## [1] "KBUF 10-year Dewpoint Temperature 10-day adjusted r-squared value better than monthly: 0.9778"
## [1] "KBUF 20-year Dewpoint Temperature 10-day adjusted r-squared value better than monthly: 0.9892"
## [1] "KBUF 30-year Dewpoint Temperature 10-day adjusted r-squared value better than monthly: 0.9926"
## [1] "KBUF 10-year Wind Direction 10-day adjusted r-squared value better than monthly: 0.2192"
## [1] "KBUF 20-year Wind Direction 10-day adjusted r-squared value better than monthly: 0.3164"
## [1] "KBUF 30-year Wind Direction 10-day adjusted r-squared value better than monthly: 0.3796"
## [1] "KBUF 10-year Wind Speed 10-day adjusted r-squared value better than monthly: 0.536"
## [1] "KBUF 20-year Wind Speed 10-day adjusted r-squared value better than monthly: 0.6701"
## [1] "KBUF 30-year Wind Speed 10-day adjusted r-squared value better than monthly: 0.7399"
## [1] "KBUF 10-year Wind Gust Speed 10-day adjusted r-squared value better than monthly: 0.482"
## [1] "KBUF 20-year Wind Gust Speed 10-day adjusted r-squared value better than monthly: 0.5719"
## [1] "KBUF 30-year Wind Gust Speed 10-day adjusted r-squared value better than monthly: 0.2779"
## [1] "KBUF 10-year Cloud Ceiling 10-day adjusted r-squared value better than monthly: 0.6745"
## [1] "KBUF 20-year Cloud Ceiling 10-day adjusted r-squared value better than monthly: 0.7919"
## [1] "KBUF 30-year Cloud Ceiling 10-day adjusted r-squared value better than monthly: 0.8529"
## [1] "KBUF 10-year Visibility monthly adjusted r-squared value better than 10-day: 0.6091"
## [1] "KBUF 20-year Visibility 10-day adjusted r-squared value better than monthly: 0.7963"
## [1] "KBUF 30-year Visibility 10-day adjusted r-squared value better than monthly: 0.8166"
## [1] "KBUF 10-year Cloud Cover 10-day adjusted r-squared value better than monthly: 0.6871"
## [1] "KBUF 20-year Cloud Cover 10-day adjusted r-squared value better than monthly: 0.7839"
## [1] "KBUF 30-year Cloud Cover 10-day adjusted r-squared value better than monthly: 0.8427"
## [1] "KBUF 10-year Altimeter 10-day adjusted r-squared value better than monthly: 0.3421"
## [1] "KBUF 20-year Altimeter 10-day adjusted r-squared value better than monthly: 0.4727"
## [1] "KBUF 30-year Altimeter 10-day adjusted r-squared value better than monthly: 0.4619"
## [1] "KFOE 10-year Temperature 10-day adjusted r-squared value better than monthly: 0.9769"
## [1] "KFOE 20-year Temperature 10-day adjusted r-squared value better than monthly: 0.9895"
## [1] "KFOE 30-year Temperature 10-day adjusted r-squared value better than monthly: 0.9927"
## [1] "KFOE 10-year Dewpoint Temperature 10-day adjusted r-squared value better than monthly: 0.9745"
## [1] "KFOE 20-year Dewpoint Temperature 10-day adjusted r-squared value better than monthly: 0.9877"
## [1] "KFOE 30-year Dewpoint Temperature 10-day adjusted r-squared value better than monthly: 0.9919"
## [1] "KFOE 10-year Wind Direction 10-day adjusted r-squared value better than monthly: 0.4019"
## [1] "KFOE 20-year Wind Direction 10-day adjusted r-squared value better than monthly: 0.5716"
## [1] "KFOE 30-year Wind Direction 10-day adjusted r-squared value better than monthly: 0.6602"
## [1] "KFOE 10-year Wind Speed 10-day adjusted r-squared value better than monthly: 0.5213"
## [1] "KFOE 20-year Wind Speed 10-day adjusted r-squared value better than monthly: 0.6549"
## [1] "KFOE 30-year Wind Speed 10-day adjusted r-squared value better than monthly: 0.7486"
## [1] "KFOE 10-year Wind Gust Speed 10-day adjusted r-squared value better than monthly: 0.4024"
## [1] "KFOE 20-year Wind Gust Speed 10-day adjusted r-squared value better than monthly: 0.5537"
## [1] "KFOE 30-year Wind Gust Speed 10-day adjusted r-squared value better than monthly: 0.6183"
## [1] "KFOE 10-year Cloud Ceiling 10-day adjusted r-squared value better than monthly: 0.4505"
## [1] "KFOE 20-year Cloud Ceiling 10-day adjusted r-squared value better than monthly: 0.5906"
## [1] "KFOE 30-year Cloud Ceiling 10-day adjusted r-squared value better than monthly: 0.6789"
## [1] "KFOE 10-year Visibility 10-day adjusted r-squared value better than monthly: 0.2871"
## [1] "KFOE 20-year Visibility 10-day adjusted r-squared value better than monthly: 0.4619"
## [1] "KFOE 30-year Visibility 10-day adjusted r-squared value better than monthly: 0.5475"
## [1] "KFOE 10-year Cloud Cover 10-day adjusted r-squared value better than monthly: 0.254"
## [1] "KFOE 20-year Cloud Cover 10-day adjusted r-squared value better than monthly: 0.4247"
## [1] "KFOE 30-year Cloud Cover 10-day adjusted r-squared value better than monthly: 0.5275"
## [1] "KFOE 10-year Altimeter 10-day adjusted r-squared value better than monthly: 0.5007"
## [1] "KFOE 20-year Altimeter 10-day adjusted r-squared value better than monthly: 0.6962"
## [1] "KFOE 30-year Altimeter 10-day adjusted r-squared value better than monthly: 0.7777"
## [1] "KMSN 10-year Temperature 10-day adjusted r-squared value better than monthly: 0.9846"
## [1] "KMSN 20-year Temperature 10-day adjusted r-squared value better than monthly: 0.9937"
## [1] "KMSN 30-year Temperature 10-day adjusted r-squared value better than monthly: 0.9954"
## [1] "KMSN 10-year Dewpoint Temperature 10-day adjusted r-squared value better than monthly: 0.9775"
## [1] "KMSN 20-year Dewpoint Temperature 10-day adjusted r-squared value better than monthly: 0.9893"
## [1] "KMSN 30-year Dewpoint Temperature 10-day adjusted r-squared value better than monthly: 0.9925"
## [1] "KMSN 10-year Wind Direction 10-day adjusted r-squared value better than monthly: 0.3296"
## [1] "KMSN 20-year Wind Direction 10-day adjusted r-squared value better than monthly: 0.4307"
## [1] "KMSN 30-year Wind Direction 10-day adjusted r-squared value better than monthly: 0.5024"
## [1] "KMSN 10-year Wind Speed 10-day adjusted r-squared value better than monthly: 0.4616"
## [1] "KMSN 20-year Wind Speed 10-day adjusted r-squared value better than monthly: 0.6521"
## [1] "KMSN 30-year Wind Speed 10-day adjusted r-squared value better than monthly: 0.7511"
## [1] "KMSN 10-year Wind Gust Speed 10-day adjusted r-squared value better than monthly: 0.2561"
## [1] "KMSN 20-year Wind Gust Speed 10-day adjusted r-squared value better than monthly: 0.2957"
## [1] "KMSN 30-year Wind Gust Speed 10-day adjusted r-squared value better than monthly: 0.3657"
## [1] "KMSN 10-year Cloud Ceiling 10-day adjusted r-squared value better than monthly: 0.3871"
## [1] "KMSN 20-year Cloud Ceiling 10-day adjusted r-squared value better than monthly: 0.5812"
## [1] "KMSN 30-year Cloud Ceiling 10-day adjusted r-squared value better than monthly: 0.6893"
## [1] "KMSN 10-year Visibility 10-day adjusted r-squared value better than monthly: 0.355"
## [1] "KMSN 20-year Visibility 10-day adjusted r-squared value better than monthly: 0.579"
## [1] "KMSN 30-year Visibility 10-day adjusted r-squared value better than monthly: 0.619"
## [1] "KMSN 10-year Cloud Cover 10-day adjusted r-squared value better than monthly: 0.4217"
## [1] "KMSN 20-year Cloud Cover 10-day adjusted r-squared value better than monthly: 0.5584"
## [1] "KMSN 30-year Cloud Cover 10-day adjusted r-squared value better than monthly: 0.6523"
## [1] "KMSN 10-year Altimeter 10-day adjusted r-squared value better than monthly: 0.381"
## [1] "KMSN 20-year Altimeter 10-day adjusted r-squared value better than monthly: 0.5136"
## [1] "KMSN 30-year Altimeter 10-day adjusted r-squared value better than monthly: 0.5828"
## [1] "KTRI 10-year Temperature 10-day adjusted r-squared value better than monthly: 0.9731"
## [1] "KTRI 20-year Temperature 10-day adjusted r-squared value better than monthly: 0.9898"
## [1] "KTRI 30-year Temperature 10-day adjusted r-squared value better than monthly: 0.9938"
## [1] "KTRI 10-year Dewpoint Temperature 10-day adjusted r-squared value better than monthly: 0.9577"
## [1] "KTRI 20-year Dewpoint Temperature 10-day adjusted r-squared value better than monthly: 0.9824"
## [1] "KTRI 30-year Dewpoint Temperature 10-day adjusted r-squared value better than monthly: 0.9893"
## [1] "KTRI 10-year Wind Direction 10-day adjusted r-squared value better than monthly: 0.1942"
## [1] "KTRI 20-year Wind Direction 10-day adjusted r-squared value better than monthly: 0.2403"
## [1] "KTRI 30-year Wind Direction 10-day adjusted r-squared value better than monthly: 0.3067"
## [1] "KTRI 10-year Wind Speed 10-day adjusted r-squared value better than monthly: 0.5055"
## [1] "KTRI 20-year Wind Speed 10-day adjusted r-squared value better than monthly: 0.7122"
## [1] "KTRI 30-year Wind Speed 10-day adjusted r-squared value better than monthly: 0.7902"
## [1] "KTRI 10-year Wind Gust Speed 10-day adjusted r-squared value better than monthly: 0.5407"
## [1] "KTRI 20-year Wind Gust Speed 10-day adjusted r-squared value better than monthly: 0.586"
## [1] "KTRI 30-year Wind Gust Speed 10-day adjusted r-squared value better than monthly: 0.7151"
## [1] "KTRI 10-year Cloud Ceiling 10-day adjusted r-squared value better than monthly: 0.3503"
## [1] "KTRI 20-year Cloud Ceiling 10-day adjusted r-squared value better than monthly: 0.473"
## [1] "KTRI 30-year Cloud Ceiling 10-day adjusted r-squared value better than monthly: 0.6294"
## [1] "KTRI 10-year Visibility 10-day adjusted r-squared value better than monthly: 0.2572"
## [1] "KTRI 20-year Visibility 10-day adjusted r-squared value better than monthly: 0.3982"
## [1] "KTRI 30-year Visibility 10-day adjusted r-squared value better than monthly: 0.6566"
## [1] "KTRI 10-year Cloud Cover 10-day adjusted r-squared value better than monthly: 0.3774"
## [1] "KTRI 20-year Cloud Cover 10-day adjusted r-squared value better than monthly: 0.355"
## [1] "KTRI 30-year Cloud Cover 10-day adjusted r-squared value better than monthly: 0.481"
## [1] "KTRI 10-year Altimeter 10-day adjusted r-squared value better than monthly: 0.4022"
## [1] "KTRI 20-year Altimeter 10-day adjusted r-squared value better than monthly: 0.5552"
## [1] "KTRI 30-year Altimeter 10-day adjusted r-squared value better than monthly: 0.6082"
## [1] "PAJN 10-year Temperature 10-day adjusted r-squared value better than monthly: 0.985"
## [1] "PAJN 20-year Temperature 10-day adjusted r-squared value better than monthly: 0.9924"
## [1] "PAJN 30-year Temperature 10-day adjusted r-squared value better than monthly: 0.9946"
## [1] "PAJN 10-year Dewpoint Temperature 10-day adjusted r-squared value better than monthly: 0.9719"
## [1] "PAJN 20-year Dewpoint Temperature 10-day adjusted r-squared value better than monthly: 0.9868"
## [1] "PAJN 30-year Dewpoint Temperature 10-day adjusted r-squared value better than monthly: 0.9892"
## [1] "PAJN 10-year Wind Direction 10-day adjusted r-squared value better than monthly: 0.3943"
## [1] "PAJN 20-year Wind Direction 10-day adjusted r-squared value better than monthly: 0.5582"
## [1] "PAJN 30-year Wind Direction 10-day adjusted r-squared value better than monthly: 0.6696"
## [1] "PAJN 10-year Wind Speed 10-day adjusted r-squared value better than monthly: 0.1329"
## [1] "PAJN 20-year Wind Speed 10-day adjusted r-squared value better than monthly: 0.1992"
## [1] "PAJN 30-year Wind Speed 10-day adjusted r-squared value better than monthly: 0.266"
## [1] "PAJN 10-year Wind Gust Speed 10-day adjusted r-squared value better than monthly: 0.5592"
## [1] "PAJN 20-year Wind Gust Speed 10-day adjusted r-squared value better than monthly: 0.5253"
## [1] "PAJN 30-year Wind Gust Speed 10-day adjusted r-squared value better than monthly: 0.5486"
## [1] "PAJN 10-year Cloud Ceiling 10-day adjusted r-squared value better than monthly: 0.4191"
## [1] "PAJN 20-year Cloud Ceiling 10-day adjusted r-squared value better than monthly: 0.4649"
## [1] "PAJN 30-year Cloud Ceiling 10-day adjusted r-squared value better than monthly: 0.5382"
## [1] "PAJN 10-year Visibility 10-day adjusted r-squared value better than monthly: 0.629"
## [1] "PAJN 20-year Visibility 10-day adjusted r-squared value better than monthly: 0.7956"
## [1] "PAJN 30-year Visibility 10-day adjusted r-squared value better than monthly: 0.8598"
## [1] "PAJN 10-year Cloud Cover 10-day adjusted r-squared value better than monthly: 0.4313"
## [1] "PAJN 20-year Cloud Cover 10-day adjusted r-squared value better than monthly: 0.4607"
## [1] "PAJN 30-year Cloud Cover 10-day adjusted r-squared value better than monthly: 0.5319"
## [1] "PAJN 10-year Altimeter 10-day adjusted r-squared value better than monthly: 0.589"
## [1] "PAJN 20-year Altimeter 10-day adjusted r-squared value better than monthly: 0.7329"
## [1] "PAJN 30-year Altimeter 10-day adjusted r-squared value better than monthly: 0.7849"
## [1] "KELP 10-year Temperature 10-day adjusted r-squared value better than monthly: 0.9818"
## [1] "KELP 20-year Temperature 10-day adjusted r-squared value better than monthly: 0.991"
## [1] "KELP 30-year Temperature 10-day adjusted r-squared value better than monthly: 0.9937"
## [1] "KELP 10-year Dewpoint Temperature 10-day adjusted r-squared value better than monthly: 0.9621"
## [1] "KELP 20-year Dewpoint Temperature 10-day adjusted r-squared value better than monthly: 0.9849"
## [1] "KELP 30-year Dewpoint Temperature 10-day adjusted r-squared value better than monthly: 0.9908"
## [1] "KELP 10-year Wind Direction 10-day adjusted r-squared value better than monthly: 0.4459"
## [1] "KELP 20-year Wind Direction 10-day adjusted r-squared value better than monthly: 0.6711"
## [1] "KELP 30-year Wind Direction 10-day adjusted r-squared value better than monthly: 0.7466"
## [1] "KELP 10-year Wind Speed 10-day adjusted r-squared value better than monthly: 0.573"
## [1] "KELP 20-year Wind Speed 10-day adjusted r-squared value better than monthly: 0.748"
## [1] "KELP 30-year Wind Speed 10-day adjusted r-squared value better than monthly: 0.8319"
## [1] "KELP 10-year Wind Gust Speed 10-day adjusted r-squared value better than monthly: 0.4083"
## [1] "KELP 20-year Wind Gust Speed 10-day adjusted r-squared value better than monthly: 0.5064"
## [1] "KELP 30-year Wind Gust Speed 10-day adjusted r-squared value better than monthly: 0.5262"
## [1] "KELP 10-year Cloud Ceiling 10-day adjusted r-squared value better than monthly: 0.3646"
## [1] "KELP 20-year Cloud Ceiling 10-day adjusted r-squared value better than monthly: 0.5856"
## [1] "KELP 30-year Cloud Ceiling 10-day adjusted r-squared value better than monthly: 0.6163"
## [1] "KELP 10-year Visibility monthly adjusted r-squared value better than 10-day: 0.0552"
## [1] "KELP 20-year Visibility 10-day adjusted r-squared value better than monthly: 0.1779"
## [1] "KELP 30-year Visibility 10-day adjusted r-squared value better than monthly: 0.7587"
## [1] "KELP 10-year Cloud Cover 10-day adjusted r-squared value better than monthly: 0.4521"
## [1] "KELP 20-year Cloud Cover 10-day adjusted r-squared value better than monthly: 0.6775"
## [1] "KELP 30-year Cloud Cover 10-day adjusted r-squared value better than monthly: 0.6916"
## [1] "KELP 10-year Altimeter 10-day adjusted r-squared value better than monthly: 0.6931"
## [1] "KELP 20-year Altimeter 10-day adjusted r-squared value better than monthly: 0.8355"
## [1] "KELP 30-year Altimeter 10-day adjusted r-squared value better than monthly: 0.8928"
## [1] "KSGU 10-year Temperature 10-day adjusted r-squared value better than monthly: 0.9901"
## [1] "KSGU 20-year Temperature 10-day adjusted r-squared value better than monthly: 0.9949"
## [1] "KSGU 30-year Temperature 10-day adjusted r-squared value better than monthly: 0.9962"
## [1] "KSGU 10-year Dewpoint Temperature 10-day adjusted r-squared value better than monthly: 0.9133"
## [1] "KSGU 20-year Dewpoint Temperature 10-day adjusted r-squared value better than monthly: 0.9668"
## [1] "KSGU 30-year Dewpoint Temperature 10-day adjusted r-squared value better than monthly: 0.9752"
## [1] "KSGU 10-year Wind Direction 10-day adjusted r-squared value better than monthly: 0.123"
## [1] "KSGU 20-year Wind Direction 10-day adjusted r-squared value better than monthly: 0.4246"
## [1] "KSGU 30-year Wind Direction 10-day adjusted r-squared value better than monthly: 0.6096"
## [1] "KSGU 10-year Wind Speed 10-day adjusted r-squared value better than monthly: 0.7286"
## [1] "KSGU 20-year Wind Speed 10-day adjusted r-squared value better than monthly: 0.8454"
## [1] "KSGU 30-year Wind Speed 10-day adjusted r-squared value better than monthly: 0.8949"
## [1] "KSGU 10-year Wind Gust Speed 10-day adjusted r-squared value better than monthly: 0.2727"
## [1] "KSGU 20-year Wind Gust Speed 10-day adjusted r-squared value better than monthly: 0.41"
## [1] "KSGU 30-year Wind Gust Speed 10-day adjusted r-squared value better than monthly: 0.4607"
## [1] "KSGU 10-year Cloud Ceiling monthly adjusted r-squared value better than 10-day: 0.4792"
## [1] "KSGU 20-year Cloud Ceiling 10-day adjusted r-squared value better than monthly: 0.6413"
## [1] "KSGU 30-year Cloud Ceiling 10-day adjusted r-squared value better than monthly: 0.7356"
## [1] "KSGU 10-year Visibility 10-day adjusted r-squared value better than monthly: 0.1417"
## [1] "KSGU 20-year Visibility 10-day adjusted r-squared value better than monthly: 0.2776"
## [1] "KSGU 30-year Visibility 10-day adjusted r-squared value better than monthly: 0.3198"
## [1] "KSGU 10-year Cloud Cover 10-day adjusted r-squared value better than monthly: 0.4079"
## [1] "KSGU 20-year Cloud Cover 10-day adjusted r-squared value better than monthly: 0.5783"
## [1] "KSGU 30-year Cloud Cover 10-day adjusted r-squared value better than monthly: 0.6944"
## [1] "KSGU 10-year Altimeter 10-day adjusted r-squared value better than monthly: 0.7538"
## [1] "KSGU 20-year Altimeter 10-day adjusted r-squared value better than monthly: 0.8763"
## [1] "KSGU 30-year Altimeter 10-day adjusted r-squared value better than monthly: 0.9181"To visualize this in chart form, the following is a tabulated representation that provides an optimal chart of which time-frame to use (10-, 20-, or 30-year) for which respective variable. Notice that for most variables, the 20-year average is an optimal amount of data to use. This is assessed by looking at the adjusted r2 values for the data and looking at how much of an improvement exists by adding more data. If the improvement is minimal, then a lower r2 value is used. If there is a greater improvement in the r2 by adding more data, then we highlight the larger dataset.
| Location/Variable | 10-year | 20-year | 30-year |
|---|---|---|---|
| KRDM/Temperature | 0.9774 | 0.988 | 0.9916 |
| KRDM/Dewpoint Temperature | 0.9158 | 0.9513 | 0.9682 |
| KRDM/Wind Direction | 0.5491 | 0.6959 | 0.7417 |
| KRDM/Wind Speed | 0.3405 | 0.5418 | 0.5973 |
| KRDM/Wind Gust Speed | 0.5292 | 0.5181 | 0.5755 |
| KRDM/Cloud Ceiling | 0.7501 | 0.8619 | 0.9065 |
| KRDM/Visibility | 0.5538 | 0.7137 | 0.7852 |
| KRDM/Cloud Cover | 0.6812 | 0.8517 | 0.9098 |
| KRDM/Altimeter | 0.3371 | 0.4607 | 0.4812 |
| KBUF/Temperature | 0.9845 | 0.993 | 0.9948 |
| KBUF/Dewpoint Temperature | 0.9778 | 0.9892 | 0.9926 |
| KBUF/Wind Direction | 0.2192 | 0.3164 | 0.3796 |
| KBUF/Wind Speed | 0.536 | 0.6701 | 0.7399 |
| KBUF/Wind Gust Speed | 0.482 | 0.5719 | 0.2779 |
| KBUF/Cloud Ceiling | 0.6745 | 0.7919 | 0.8529 |
| KBUF/Visibility | 0.6025 | 0.7963 | 0.8166 |
| KBUF/Cloud Cover | 0.6871 | 0.7839 | 0.8427 |
| KBUF/Altimeter | 0.3421 | 0.4727 | 0.4619 |
| KFOE/Temperature | 0.9769 | 0.9895 | 0.9927 |
| KFOE/Dewpoint Temperature | 0.9745 | 0.9877 | 0.9919 |
| KFOE/Wind Direction | 0.4019 | 0.5716 | 0.6602 |
| KFOE/Wind Speed | 0.5213 | 0.6549 | 0.7486 |
| KFOE/Wind Gust Speed | 0.4024 | 0.5537 | 0.6183 |
| KFOE/Cloud Ceiling | 0.4505 | 0.5906 | 0.6789 |
| KFOE/Visibility | 0.2871 | 0.4619 | 0.5475 |
| KFOE/Cloud Cover | 0.254 | 0.4247 | 0.5275 |
| KFOE/Altimeter | 0.5007 | 0.6962 | 0.7777 |
| KMSN/Temperature | 0.9846 | 0.9937 | 0.9954 |
| KMSN/Dewpoint Temperature | 0.9775 | 0.9893 | 0.9925 |
| KMSN/Wind Direction | 0.3296 | 0.4307 | 0.5024 |
| KMSN/Wind Speed | 0.4616 | 0.6521 | 0.7511 |
| KMSN/Wind Gust Speed | 0.2561 | 0.2957 | 0.3657 |
| KMSN/Cloud Ceiling | 0.3871 | 0.5812 | 0.6893 |
| KMSN/Visibility | 0.355 | 0.579 | 0.619 |
| KMSN/Cloud Cover | 0.4217 | 0.5584 | 0.6523 |
| KMSN/Altimeter | 0.381 | 0.5136 | 0.5828 |
| KTRI/Temperature | 0.9731 | 0.9898 | 0.9938 |
| KTRI/Dewpoint Temperature | 0.9577 | 0.9824 | 0.9893 |
| KTRI/Wind Direction | 0.1942 | 0.2403 | 0.3067 |
| KTRI/Wind Speed | 0.5055 | 0.7122 | 0.7902 |
| KTRI/Wind Gust Speed | 0.5407 | 0.586 | 0.7151 |
| KTRI/Cloud Ceiling | 0.3503 | 0.473 | 0.6294 |
| KTRI/Visibility | 0.2572 | 0.3982 | 0.6566 |
| KTRI/Cloud Cover | 0.3774 | 0.355 | 0.481 |
| KTRI/Altimeter | 0.4022 | 0.5552 | 0.6082 |
| PAJN/Temperature | 0.985 | 0.9924 | 0.9946 |
| PAJN/Dewpoint Temperature | 0.9719 | 0.9868 | 0.9892 |
| PAJN/Wind Direction | 0.3943 | 0.5582 | 0.6696 |
| PAJN/Wind Speed | 0.1329 | 0.1992 | 0.266 |
| PAJN/Wind Gust Speed | 0.5592 | 0.5253 | 0.5486 |
| PAJN/Cloud Ceiling | 0.4191 | 0.4649 | 0.5382 |
| PAJN/Visibility | 0.629 | 0.7956 | 0.8598 |
| PAJN/Cloud Cover | 0.4313 | 0.4607 | 0.5319 |
| PAJN/Altimeter | 0.589 | 0.7329 | 0.7849 |
| KELP/Temperature | 0.9818 | 0.991 | 0.9937 |
| KELP/Dewpoint Temperature | 0.9621 | 0.9849 | 0.9908 |
| KELP/Wind Direction | 0.4459 | 0.6711 | 0.7466 |
| KELP/Wind Speed | 0.573 | 0.748 | 0.8319 |
| KELP/Wind Gust Speed | 0.4083 | 0.5064 | 0.5262 |
| KELP/Cloud Ceiling | 0.3646 | 0.5856 | 0.6163 |
| KELP/Visibility | 0.0298 | 0.1779 | 0.7587 |
| KELP/Cloud Cover | 0.4521 | 0.6775 | 0.6916 |
| KELP/Altimeter | 0.6931 | 0.8355 | 0.8928 |
| KSGU/Temperature | 0.9901 | 0.9949 | 0.9962 |
| KSGU/Dewpoint Temperature | 0.9133 | 0.9668 | 0.9752 |
| KSGU/Wind Direction | 0.123 | 0.4246 | 0.6096 |
| KSGU/Wind Speed | 0.7286 | 0.8454 | 0.8949 |
| KSGU/Wind Gust Speed | 0.2727 | 0.41 | 0.4607 |
| KSGU/Cloud Ceiling | 0.479 | 0.6413 | 0.7356 |
| KSGU/Visibility | 0.1417 | 0.2776 | 0.3198 |
| KSGU/Cloud Cover | 0.4079 | 0.5783 | 0.6944 |
| KSGU/Altimeter | 0.7538 | 0.8763 | 0.9181 |
Note that the data suggests that 20-years of data provides an overall, better fit to the data.
Spline Comparison
We will compare the spline models generated by doing the following:
Create subsets of the data: 1994-2003, 2004-2013, and 2014-2023
Generate linear regressions for each Julian Day for each of the date ranges. This will calculate the trend for each 10 year span.
Of note, a positive number is the slope, or a positive trend in the data. For temperature, this would indicate a warming trend for that portion of the year. A negative number would indicate a cooling trend for that portion of the year.
Heavy Snow Winter
Warm Summer, Cold Winter
Arid
Adjusted R-squared with adjusted 10-year intervals
Notice below that previous decades of splines outperformed the latest 10 years of data. This implies that using 10 years of data is changing in a couple of ways:
The sensors could be reporting better observations than they previously reported.
The conditions are changing more quickly than previously observed.
With time, as the human element has been removed from observation transmission, the quality of observations has decreased which has introduced more errors into the observations.
ten = 0
twenty = 0
thirty = 0
for(i in 1:length(r_squared_revised.df$station)) {
vals = c(r_squared_revised.df$ten_yr_10day[i],r_squared_revised.df$twenty_yr_rvsd_10day[i],r_squared_revised.df$thirty_yr_rvsd_10day[i])
maximum = max(vals)
if(r_squared_revised.df$ten_yr_10day[i]==maximum) {
print(paste0(r_squared_revised.df$station[i]," 10-year ",r_squared_revised.df$vars[i]," adjusted r-squared outperforms others: ",round(r_squared_revised.df$ten_yr_10day[i],4)))
ten = ten + 1
}
if(r_squared_revised.df$twenty_yr_rvsd_10day[i]==maximum) {
print(paste0(r_squared_revised.df$station[i]," 20-year ",r_squared_revised.df$vars[i]," adjusted r-squared outperforms others: ",round(r_squared_revised.df$twenty_yr_rvsd_10day[i],4)))
twenty = twenty + 1
}
if(r_squared_revised.df$thirty_yr_rvsd_10day[i]==maximum) {
print(paste0(r_squared_revised.df$station[i]," 30-year ",r_squared_revised.df$vars[i]," adjusted r-squared outperforms others: ",round(r_squared_revised.df$thirty_yr_rvsd_10day[i],4)))
thirty = thirty + 1
}
}## [1] "KRDM 20-year Temperature adjusted r-squared outperforms others: 0.9791"
## [1] "KRDM 30-year Dewpoint Temperature adjusted r-squared outperforms others: 0.9682"
## [1] "KRDM 30-year Wind Direction adjusted r-squared outperforms others: 0.7417"
## [1] "KRDM 30-year Wind Speed adjusted r-squared outperforms others: 0.5973"
## [1] "KRDM 30-year Wind Gust Speed adjusted r-squared outperforms others: 0.5755"
## [1] "KRDM 30-year Cloud Ceiling adjusted r-squared outperforms others: 0.9065"
## [1] "KRDM 30-year Visibility adjusted r-squared outperforms others: 0.7852"
## [1] "KRDM 30-year Cloud Cover adjusted r-squared outperforms others: 0.9098"
## [1] "KRDM 30-year Altimeter adjusted r-squared outperforms others: 0.4812"
## [1] "KBUF 20-year Temperature adjusted r-squared outperforms others: 0.9869"
## [1] "KBUF 30-year Dewpoint Temperature adjusted r-squared outperforms others: 0.9926"
## [1] "KBUF 30-year Wind Direction adjusted r-squared outperforms others: 0.3796"
## [1] "KBUF 30-year Wind Speed adjusted r-squared outperforms others: 0.7399"
## [1] "KBUF 20-year Wind Gust Speed adjusted r-squared outperforms others: 0.5719"
## [1] "KBUF 30-year Cloud Ceiling adjusted r-squared outperforms others: 0.8529"
## [1] "KBUF 30-year Visibility adjusted r-squared outperforms others: 0.8166"
## [1] "KBUF 30-year Cloud Cover adjusted r-squared outperforms others: 0.8427"
## [1] "KBUF 20-year Altimeter adjusted r-squared outperforms others: 0.4727"
## [1] "KFOE 20-year Temperature adjusted r-squared outperforms others: 0.9844"
## [1] "KFOE 30-year Dewpoint Temperature adjusted r-squared outperforms others: 0.9919"
## [1] "KFOE 30-year Wind Direction adjusted r-squared outperforms others: 0.6602"
## [1] "KFOE 30-year Wind Speed adjusted r-squared outperforms others: 0.7486"
## [1] "KFOE 30-year Wind Gust Speed adjusted r-squared outperforms others: 0.6183"
## [1] "KFOE 30-year Cloud Ceiling adjusted r-squared outperforms others: 0.6789"
## [1] "KFOE 30-year Visibility adjusted r-squared outperforms others: 0.5475"
## [1] "KFOE 30-year Cloud Cover adjusted r-squared outperforms others: 0.5275"
## [1] "KFOE 30-year Altimeter adjusted r-squared outperforms others: 0.7777"
## [1] "KMSN 20-year Temperature adjusted r-squared outperforms others: 0.9876"
## [1] "KMSN 30-year Dewpoint Temperature adjusted r-squared outperforms others: 0.9925"
## [1] "KMSN 30-year Wind Direction adjusted r-squared outperforms others: 0.5024"
## [1] "KMSN 30-year Wind Speed adjusted r-squared outperforms others: 0.7511"
## [1] "KMSN 30-year Wind Gust Speed adjusted r-squared outperforms others: 0.3657"
## [1] "KMSN 30-year Cloud Ceiling adjusted r-squared outperforms others: 0.6893"
## [1] "KMSN 30-year Visibility adjusted r-squared outperforms others: 0.619"
## [1] "KMSN 30-year Cloud Cover adjusted r-squared outperforms others: 0.6523"
## [1] "KMSN 30-year Altimeter adjusted r-squared outperforms others: 0.5828"
## [1] "KTRI 20-year Temperature adjusted r-squared outperforms others: 0.9865"
## [1] "KTRI 30-year Dewpoint Temperature adjusted r-squared outperforms others: 0.9893"
## [1] "KTRI 30-year Wind Direction adjusted r-squared outperforms others: 0.3067"
## [1] "KTRI 30-year Wind Speed adjusted r-squared outperforms others: 0.7902"
## [1] "KTRI 30-year Wind Gust Speed adjusted r-squared outperforms others: 0.7151"
## [1] "KTRI 30-year Cloud Ceiling adjusted r-squared outperforms others: 0.6294"
## [1] "KTRI 30-year Visibility adjusted r-squared outperforms others: 0.6566"
## [1] "KTRI 30-year Cloud Cover adjusted r-squared outperforms others: 0.481"
## [1] "KTRI 30-year Altimeter adjusted r-squared outperforms others: 0.6082"
## [1] "PAJN 10-year Temperature adjusted r-squared outperforms others: 0.985"
## [1] "PAJN 30-year Dewpoint Temperature adjusted r-squared outperforms others: 0.9892"
## [1] "PAJN 30-year Wind Direction adjusted r-squared outperforms others: 0.6696"
## [1] "PAJN 30-year Wind Speed adjusted r-squared outperforms others: 0.266"
## [1] "PAJN 10-year Wind Gust Speed adjusted r-squared outperforms others: 0.5592"
## [1] "PAJN 30-year Cloud Ceiling adjusted r-squared outperforms others: 0.5382"
## [1] "PAJN 30-year Visibility adjusted r-squared outperforms others: 0.8598"
## [1] "PAJN 30-year Cloud Cover adjusted r-squared outperforms others: 0.5319"
## [1] "PAJN 30-year Altimeter adjusted r-squared outperforms others: 0.7849"
## [1] "KELP 20-year Temperature adjusted r-squared outperforms others: 0.9856"
## [1] "KELP 30-year Dewpoint Temperature adjusted r-squared outperforms others: 0.9908"
## [1] "KELP 30-year Wind Direction adjusted r-squared outperforms others: 0.7466"
## [1] "KELP 30-year Wind Speed adjusted r-squared outperforms others: 0.8319"
## [1] "KELP 30-year Wind Gust Speed adjusted r-squared outperforms others: 0.5262"
## [1] "KELP 30-year Cloud Ceiling adjusted r-squared outperforms others: 0.6163"
## [1] "KELP 30-year Visibility adjusted r-squared outperforms others: 0.7587"
## [1] "KELP 30-year Cloud Cover adjusted r-squared outperforms others: 0.6916"
## [1] "KELP 30-year Altimeter adjusted r-squared outperforms others: 0.8928"
## [1] "KSGU 20-year Temperature adjusted r-squared outperforms others: 0.9922"
## [1] "KSGU 30-year Dewpoint Temperature adjusted r-squared outperforms others: 0.9752"
## [1] "KSGU 30-year Wind Direction adjusted r-squared outperforms others: 0.6096"
## [1] "KSGU 30-year Wind Speed adjusted r-squared outperforms others: 0.8949"
## [1] "KSGU 30-year Wind Gust Speed adjusted r-squared outperforms others: 0.4607"
## [1] "KSGU 30-year Cloud Ceiling adjusted r-squared outperforms others: 0.7356"
## [1] "KSGU 30-year Visibility adjusted r-squared outperforms others: 0.3198"
## [1] "KSGU 30-year Cloud Cover adjusted r-squared outperforms others: 0.6944"
## [1] "KSGU 30-year Altimeter adjusted r-squared outperforms others: 0.9181"print(paste0("Number of times 10-year was the best adjusted r-squared: ",ten))## [1] "Number of times 10-year was the best adjusted r-squared: 2"print(paste0("Number of times 10-year was the best adjusted r-squared: ",twenty))## [1] "Number of times 10-year was the best adjusted r-squared: 9"print(paste0("Number of times 10-year was the best adjusted r-squared: ",thirty))## [1] "Number of times 10-year was the best adjusted r-squared: 61"Trend Analysis
Here we perform an analysis of the previous spline trends. As mentioned - the positive numbers mean a positive slope, the negative numbers are a negative slope. We will sum the numbers - if the result is positive, there is a positive trend over the years and, if the sum is negative, there is a negative trend over the years.
KRDM
print(paste0("krdm temperature slope trend: ",round(sum(krdm_t_fv),1),if(sum(krdm_t_fv)>0){" --> WARMING"} else {" --> COOLING"}))## [1] "krdm temperature slope trend: 145.5 --> WARMING"print(paste0("krdm dewpoint temperature slope trend: ",round(sum(krdm_dp_fv),1),if(sum(krdm_dp_fv)>0){" --> MOISTENING"} else {" --> DRYING"}))## [1] "krdm dewpoint temperature slope trend: 4.8 --> MOISTENING"print(paste0("krdm wind direction slope trend: ",round(sum(krdm_wd_fv),1),if(sum(krdm_wd_fv)>0){" --> BECOMING MORE WESTERLY/NORTHERLY"} else {" --> BECOMING MORE SOUTHERLY/EASTERLY"}))## [1] "krdm wind direction slope trend: -62.7 --> BECOMING MORE SOUTHERLY/EASTERLY"print(paste0("krdm wind speed slope trend: ",round(sum(krdm_ws_fv),1),if(sum(krdm_ws_fv)>0){" --> SPEED INCREASING"} else {" --> SPEED DECREASING"}))## [1] "krdm wind speed slope trend: 21.2 --> SPEED INCREASING"print(paste0("krdm wind gust slope trend: ",round(sum(krdm_wg_fv),1),if(sum(krdm_wg_fv)>0){" --> SPEED INCREASING"} else {" --> SPEED DECREASING"}))## [1] "krdm wind gust slope trend: 86.4 --> SPEED INCREASING"print(paste0("krdm cloud ceilings slope trend: ",round(sum(krdm_cig_fv),1),if(sum(krdm_cig_fv)>0){" --> CLOUD DECKS RAISING"} else {" --> CLOUD DECKS DROPPING"}))## [1] "krdm cloud ceilings slope trend: -6561.8 --> CLOUD DECKS DROPPING"print(paste0("krdm visibility slope trend: ",round(sum(krdm_vis_fv),1),if(sum(krdm_vis_fv)>0){" --> VISIBILITY INCREASING"} else {" --> VISIBILITY DECREASING"}))## [1] "krdm visibility slope trend: -49441.9 --> VISIBILITY DECREASING"print(paste0("krdm cloud cover slope trend: ",round(sum(krdm_cc_fv),1),if(sum(krdm_cc_fv)>0){" --> MORE CLOUDY"} else {" --> LESS CLOUDY"}))## [1] "krdm cloud cover slope trend: -184.1 --> LESS CLOUDY"print(paste0("krdm altimeter slope trend: ",round(sum(krdm_alt_fv),1),if(sum(krdm_alt_fv)>0){" --> PRESSURE RISING"} else {" --> PRESSURE DROPPING"}))## [1] "krdm altimeter slope trend: 27.2 --> PRESSURE RISING"print(paste0("krdm precipitation slope trend: ",round(sum(krdm_p_fv),1),if(sum(krdm_p_fv)>0){" --> INCREASING PRECIPITATION"} else {" --> DECREASING PRECIPITATION"}))## [1] "krdm precipitation slope trend: 135.2 --> INCREASING PRECIPITATION"KBUF
print(paste0("kbuf temperature slope trend: ",round(sum(kbuf_t_fv),1),if(sum(kbuf_t_fv)>0){" --> WARMING"} else {" --> COOLING"}))## [1] "kbuf temperature slope trend: 137.9 --> WARMING"print(paste0("kbuf dewpoint temperature slope trend: ",round(sum(kbuf_dp_fv),1),if(sum(kbuf_dp_fv)>0){" --> MOISTENING"} else {" --> DRYING"}))## [1] "kbuf dewpoint temperature slope trend: -9.6 --> DRYING"print(paste0("kbuf wind direction slope trend: ",round(sum(kbuf_wd_fv),1),if(sum(kbuf_wd_fv)>0){" --> BECOMING MORE WESTERLY/NORTHERLY"} else {" --> BECOMING MORE SOUTHERLY/EASTERLY"}))## [1] "kbuf wind direction slope trend: -149.5 --> BECOMING MORE SOUTHERLY/EASTERLY"print(paste0("kbuf wind speed slope trend: ",round(sum(kbuf_ws_fv),1),if(sum(kbuf_ws_fv)>0){" --> SPEED INCREASING"} else {" --> SPEED DECREASING"}))## [1] "kbuf wind speed slope trend: 0.4 --> SPEED INCREASING"print(paste0("kbuf wind gust slope trend: ",round(sum(kbuf_wg_fv),1),if(sum(kbuf_wg_fv)>0){" --> SPEED INCREASING"} else {" --> SPEED DECREASING"}))## [1] "kbuf wind gust slope trend: 60.1 --> SPEED INCREASING"print(paste0("kbuf cloud ceilings slope trend: ",round(sum(kbuf_cig_fv),1),if(sum(kbuf_cig_fv)>0){" --> CLOUD DECKS RAISING"} else {" --> CLOUD DECKS DROPPING"}))## [1] "kbuf cloud ceilings slope trend: -28329.8 --> CLOUD DECKS DROPPING"print(paste0("kbuf visibility slope trend: ",round(sum(kbuf_vis_fv),1),if(sum(kbuf_vis_fv)>0){" --> VISIBILITY INCREASING"} else {" --> VISIBILITY DECREASING"}))## [1] "kbuf visibility slope trend: 35858.6 --> VISIBILITY INCREASING"print(paste0("kbuf cloud cover slope trend: ",round(sum(kbuf_cc_fv),1),if(sum(kbuf_cc_fv)>0){" --> MORE CLOUDY"} else {" --> LESS CLOUDY"}))## [1] "kbuf cloud cover slope trend: -54.5 --> LESS CLOUDY"print(paste0("kbuf altimeter slope trend: ",round(sum(kbuf_alt_fv),1),if(sum(kbuf_alt_fv)>0){" --> PRESSURE RISING"} else {" --> PRESSURE DROPPING"}))## [1] "kbuf altimeter slope trend: 77 --> PRESSURE RISING"print(paste0("kbuf precipitation slope trend: ",round(sum(kbuf_p_fv),1),if(sum(kbuf_p_fv)>0){" --> INCREASING PRECIPITATION"} else {" --> DECREASING PRECIPITATION"}))## [1] "kbuf precipitation slope trend: 1471.2 --> INCREASING PRECIPITATION"KFOE
print(paste0("kfoe temperature slope trend: ",round(sum(kfoe_t_fv),1),if(sum(kfoe_t_fv)>0){" --> WARMING"} else {" --> COOLING"}))## [1] "kfoe temperature slope trend: 67.7 --> WARMING"print(paste0("kfoe dewpoint temperature slope trend: ",round(sum(kfoe_dp_fv),1),if(sum(kfoe_dp_fv)>0){" --> MOISTENING"} else {" --> DRYING"}))## [1] "kfoe dewpoint temperature slope trend: 42.2 --> MOISTENING"print(paste0("kfoe wind direction slope trend: ",round(sum(kfoe_wd_fv),1),if(sum(kfoe_wd_fv)>0){" --> BECOMING MORE WESTERLY/NORTHERLY"} else {" --> BECOMING MORE SOUTHERLY/EASTERLY"}))## [1] "kfoe wind direction slope trend: 95.7 --> BECOMING MORE WESTERLY/NORTHERLY"print(paste0("kfoe wind speed slope trend: ",round(sum(kfoe_ws_fv),1),if(sum(kfoe_ws_fv)>0){" --> SPEED INCREASING"} else {" --> SPEED DECREASING"}))## [1] "kfoe wind speed slope trend: -16.4 --> SPEED DECREASING"print(paste0("kfoe wind gust slope trend: ",round(sum(kfoe_wg_fv),1),if(sum(kfoe_wg_fv)>0){" --> SPEED INCREASING"} else {" --> SPEED DECREASING"}))## [1] "kfoe wind gust slope trend: 12.4 --> SPEED INCREASING"print(paste0("kfoe cloud ceilings slope trend: ",round(sum(kfoe_cig_fv),1),if(sum(kfoe_cig_fv)>0){" --> CLOUD DECKS RAISING"} else {" --> CLOUD DECKS DROPPING"}))## [1] "kfoe cloud ceilings slope trend: -41229.8 --> CLOUD DECKS DROPPING"print(paste0("kfoe visibility slope trend: ",round(sum(kfoe_vis_fv),1),if(sum(kfoe_vis_fv)>0){" --> VISIBILITY INCREASING"} else {" --> VISIBILITY DECREASING"}))## [1] "kfoe visibility slope trend: -22757.6 --> VISIBILITY DECREASING"print(paste0("kfoe cloud cover slope trend: ",round(sum(kfoe_cc_fv),1),if(sum(kfoe_cc_fv)>0){" --> MORE CLOUDY"} else {" --> LESS CLOUDY"}))## [1] "kfoe cloud cover slope trend: -220.3 --> LESS CLOUDY"print(paste0("kfoe altimeter slope trend: ",round(sum(kfoe_alt_fv),1),if(sum(kfoe_alt_fv)>0){" --> PRESSURE RISING"} else {" --> PRESSURE DROPPING"}))## [1] "kfoe altimeter slope trend: 1.9 --> PRESSURE RISING"print(paste0("kfoe precipitation slope trend: ",round(sum(kfoe_p_fv),1),if(sum(kfoe_p_fv)>0){" --> INCREASING PRECIPITATION"} else {" --> DECREASING PRECIPITATION"}))## [1] "kfoe precipitation slope trend: 1349.1 --> INCREASING PRECIPITATION"KMSN
print(paste0("kmsn temperature slope trend: ",round(sum(kmsn_t_fv),1),if(sum(kmsn_t_fv)>0){" --> WARMING"} else {" --> COOLING"}))## [1] "kmsn temperature slope trend: -37.5 --> COOLING"print(paste0("kmsn dewpoint temperature slope trend: ",round(sum(kmsn_dp_fv),1),if(sum(kmsn_dp_fv)>0){" --> MOISTENING"} else {" --> DRYING"}))## [1] "kmsn dewpoint temperature slope trend: -22.5 --> DRYING"print(paste0("kmsn wind direction slope trend: ",round(sum(kmsn_wd_fv),1),if(sum(kmsn_wd_fv)>0){" --> BECOMING MORE WESTERLY/NORTHERLY"} else {" --> BECOMING MORE SOUTHERLY/EASTERLY"}))## [1] "kmsn wind direction slope trend: 684.1 --> BECOMING MORE WESTERLY/NORTHERLY"print(paste0("kmsn wind speed slope trend: ",round(sum(kmsn_ws_fv),1),if(sum(kmsn_ws_fv)>0){" --> SPEED INCREASING"} else {" --> SPEED DECREASING"}))## [1] "kmsn wind speed slope trend: -20.5 --> SPEED DECREASING"print(paste0("kmsn wind gust slope trend: ",round(sum(kmsn_wg_fv),1),if(sum(kmsn_wg_fv)>0){" --> SPEED INCREASING"} else {" --> SPEED DECREASING"}))## [1] "kmsn wind gust slope trend: 30 --> SPEED INCREASING"print(paste0("kmsn cloud ceilings slope trend: ",round(sum(kmsn_cig_fv),1),if(sum(kmsn_cig_fv)>0){" --> CLOUD DECKS RAISING"} else {" --> CLOUD DECKS DROPPING"}))## [1] "kmsn cloud ceilings slope trend: -282231.5 --> CLOUD DECKS DROPPING"print(paste0("kmsn visibility slope trend: ",round(sum(kmsn_vis_fv),1),if(sum(kmsn_vis_fv)>0){" --> VISIBILITY INCREASING"} else {" --> VISIBILITY DECREASING"}))## [1] "kmsn visibility slope trend: 144219.5 --> VISIBILITY INCREASING"print(paste0("kmsn cloud cover slope trend: ",round(sum(kmsn_cc_fv),1),if(sum(kmsn_cc_fv)>0){" --> MORE CLOUDY"} else {" --> LESS CLOUDY"}))## [1] "kmsn cloud cover slope trend: 45.7 --> MORE CLOUDY"print(paste0("kmsn altimeter slope trend: ",round(sum(kmsn_alt_fv),1),if(sum(kmsn_alt_fv)>0){" --> PRESSURE RISING"} else {" --> PRESSURE DROPPING"}))## [1] "kmsn altimeter slope trend: 26.7 --> PRESSURE RISING"print(paste0("kmsn precipitation slope trend: ",round(sum(kmsn_p_fv),1),if(sum(kmsn_p_fv)>0){" --> INCREASING PRECIPITATION"} else {" --> DECREASING PRECIPITATION"}))## [1] "kmsn precipitation slope trend: 1108.8 --> INCREASING PRECIPITATION"KTRI
print(paste0("ktri temperature slope trend: ",round(sum(ktri_t_fv),1),if(sum(ktri_t_fv)>0){" --> WARMING"} else {" --> COOLING"}))## [1] "ktri temperature slope trend: 218.6 --> WARMING"print(paste0("ktri dewpoint temperature slope trend: ",round(sum(ktri_dp_fv),1),if(sum(ktri_dp_fv)>0){" --> MOISTENING"} else {" --> DRYING"}))## [1] "ktri dewpoint temperature slope trend: 71.6 --> MOISTENING"print(paste0("ktri wind direction slope trend: ",round(sum(ktri_wd_fv),1),if(sum(ktri_wd_fv)>0){" --> BECOMING MORE WESTERLY/NORTHERLY"} else {" --> BECOMING MORE SOUTHERLY/EASTERLY"}))## [1] "ktri wind direction slope trend: 1009.8 --> BECOMING MORE WESTERLY/NORTHERLY"print(paste0("ktri wind speed slope trend: ",round(sum(ktri_ws_fv),1),if(sum(ktri_ws_fv)>0){" --> SPEED INCREASING"} else {" --> SPEED DECREASING"}))## [1] "ktri wind speed slope trend: -11.9 --> SPEED DECREASING"print(paste0("ktri wind gust slope trend: ",round(sum(ktri_wg_fv),1),if(sum(ktri_wg_fv)>0){" --> SPEED INCREASING"} else {" --> SPEED DECREASING"}))## [1] "ktri wind gust slope trend: -1.4 --> SPEED DECREASING"print(paste0("ktri cloud ceilings slope trend: ",round(sum(ktri_cig_fv),1),if(sum(ktri_cig_fv)>0){" --> CLOUD DECKS RAISING"} else {" --> CLOUD DECKS DROPPING"}))## [1] "ktri cloud ceilings slope trend: -113176.8 --> CLOUD DECKS DROPPING"print(paste0("ktri visibility slope trend: ",round(sum(ktri_vis_fv),1),if(sum(ktri_vis_fv)>0){" --> VISIBILITY INCREASING"} else {" --> VISIBILITY DECREASING"}))## [1] "ktri visibility slope trend: 130862.1 --> VISIBILITY INCREASING"print(paste0("ktri cloud cover slope trend: ",round(sum(ktri_cc_fv),1),if(sum(ktri_cc_fv)>0){" --> MORE CLOUDY"} else {" --> LESS CLOUDY"}))## [1] "ktri cloud cover slope trend: -181.7 --> LESS CLOUDY"print(paste0("ktri altimeter slope trend: ",round(sum(ktri_alt_fv),1),if(sum(ktri_alt_fv)>0){" --> PRESSURE RISING"} else {" --> PRESSURE DROPPING"}))## [1] "ktri altimeter slope trend: 73.2 --> PRESSURE RISING"print(paste0("ktri precipitation slope trend: ",round(sum(ktri_p_fv),1),if(sum(ktri_p_fv)>0){" --> INCREASING PRECIPITATION"} else {" --> DECREASING PRECIPITATION"}))## [1] "ktri precipitation slope trend: 605.6 --> INCREASING PRECIPITATION"PAJN
print(paste0("pajn temperature slope trend: ",round(sum(pajn_t_fv),1),if(sum(pajn_t_fv)>0){" --> WARMING"} else {" --> COOLING"}))## [1] "pajn temperature slope trend: 124.8 --> WARMING"print(paste0("pajn dewpoint temperature slope trend: ",round(sum(pajn_dp_fv),1),if(sum(pajn_dp_fv)>0){" --> MOISTENING"} else {" --> DRYING"}))## [1] "pajn dewpoint temperature slope trend: 58.5 --> MOISTENING"print(paste0("pajn wind direction slope trend: ",round(sum(pajn_wd_fv),1),if(sum(pajn_wd_fv)>0){" --> BECOMING MORE WESTERLY/NORTHERLY"} else {" --> BECOMING MORE SOUTHERLY/EASTERLY"}))## [1] "pajn wind direction slope trend: -327.5 --> BECOMING MORE SOUTHERLY/EASTERLY"print(paste0("pajn wind speed slope trend: ",round(sum(pajn_ws_fv),1),if(sum(pajn_ws_fv)>0){" --> SPEED INCREASING"} else {" --> SPEED DECREASING"}))## [1] "pajn wind speed slope trend: 14.3 --> SPEED INCREASING"print(paste0("pajn wind gust slope trend: ",round(sum(pajn_wg_fv),1),if(sum(pajn_wg_fv)>0){" --> SPEED INCREASING"} else {" --> SPEED DECREASING"}))## [1] "pajn wind gust slope trend: 88.8 --> SPEED INCREASING"print(paste0("pajn cloud ceilings slope trend: ",round(sum(pajn_cig_fv),1),if(sum(pajn_cig_fv)>0){" --> CLOUD DECKS RAISING"} else {" --> CLOUD DECKS DROPPING"}))## [1] "pajn cloud ceilings slope trend: 6640.3 --> CLOUD DECKS RAISING"print(paste0("pajn visibility slope trend: ",round(sum(pajn_vis_fv),1),if(sum(pajn_vis_fv)>0){" --> VISIBILITY INCREASING"} else {" --> VISIBILITY DECREASING"}))## [1] "pajn visibility slope trend: -250493.3 --> VISIBILITY DECREASING"print(paste0("pajn cloud cover slope trend: ",round(sum(pajn_cc_fv),1),if(sum(pajn_cc_fv)>0){" --> MORE CLOUDY"} else {" --> LESS CLOUDY"}))## [1] "pajn cloud cover slope trend: -40.3 --> LESS CLOUDY"print(paste0("pajn altimeter slope trend: ",round(sum(pajn_alt_fv),1),if(sum(pajn_alt_fv)>0){" --> PRESSURE RISING"} else {" --> PRESSURE DROPPING"}))## [1] "pajn altimeter slope trend: 105.8 --> PRESSURE RISING"print(paste0("pajn precipitation slope trend: ",round(sum(pajn_p_fv),1),if(sum(pajn_p_fv)>0){" --> INCREASING PRECIPITATION"} else {" --> DECREASING PRECIPITATION"}))## [1] "pajn precipitation slope trend: 1310.7 --> INCREASING PRECIPITATION"KELP
print(paste0("kelp temperature slope trend: ",round(sum(kelp_t_fv),1),if(sum(kelp_t_fv)>0){" --> WARMING"} else {" --> COOLING"}))## [1] "kelp temperature slope trend: 197.2 --> WARMING"print(paste0("kelp dewpoint temperature slope trend: ",round(sum(kelp_dp_fv),1),if(sum(kelp_dp_fv)>0){" --> MOISTENING"} else {" --> DRYING"}))## [1] "kelp dewpoint temperature slope trend: -53.5 --> DRYING"print(paste0("kelp wind direction slope trend: ",round(sum(kelp_wd_fv),1),if(sum(kelp_wd_fv)>0){" --> BECOMING MORE WESTERLY/NORTHERLY"} else {" --> BECOMING MORE SOUTHERLY/EASTERLY"}))## [1] "kelp wind direction slope trend: 55.5 --> BECOMING MORE WESTERLY/NORTHERLY"print(paste0("kelp wind speed slope trend: ",round(sum(kelp_ws_fv),1),if(sum(kelp_ws_fv)>0){" --> SPEED INCREASING"} else {" --> SPEED DECREASING"}))## [1] "kelp wind speed slope trend: -7.1 --> SPEED DECREASING"print(paste0("kelp wind gust slope trend: ",round(sum(kelp_wg_fv),1),if(sum(kelp_wg_fv)>0){" --> SPEED INCREASING"} else {" --> SPEED DECREASING"}))## [1] "kelp wind gust slope trend: 37.3 --> SPEED INCREASING"print(paste0("kelp cloud ceilings slope trend: ",round(sum(kelp_cig_fv),1),if(sum(kelp_cig_fv)>0){" --> CLOUD DECKS RAISING"} else {" --> CLOUD DECKS DROPPING"}))## [1] "kelp cloud ceilings slope trend: -49254.3 --> CLOUD DECKS DROPPING"print(paste0("kelp visibility slope trend: ",round(sum(kelp_vis_fv),1),if(sum(kelp_vis_fv)>0){" --> VISIBILITY INCREASING"} else {" --> VISIBILITY DECREASING"}))## [1] "kelp visibility slope trend: -258348.8 --> VISIBILITY DECREASING"print(paste0("kelp cloud cover slope trend: ",round(sum(kelp_cc_fv),1),if(sum(kelp_cc_fv)>0){" --> MORE CLOUDY"} else {" --> LESS CLOUDY"}))## [1] "kelp cloud cover slope trend: -0.3 --> LESS CLOUDY"print(paste0("kelp altimeter slope trend: ",round(sum(kelp_alt_fv),1),if(sum(kelp_alt_fv)>0){" --> PRESSURE RISING"} else {" --> PRESSURE DROPPING"}))## [1] "kelp altimeter slope trend: -1.5 --> PRESSURE DROPPING"print(paste0("kelp precipitation slope trend: ",round(sum(kelp_p_fv),1),if(sum(kelp_p_fv)>0){" --> INCREASING PRECIPITATION"} else {" --> DECREASING PRECIPITATION"}))## [1] "kelp precipitation slope trend: 140.1 --> INCREASING PRECIPITATION"KSGU
print(paste0("ksgu temperature slope trend: ",round(sum(ksgu_t_fv),1),if(sum(ksgu_t_fv)>0){" --> WARMING"} else {" --> COOLING"}))## [1] "ksgu temperature slope trend: -38.7 --> COOLING"print(paste0("ksgu dewpoint temperature slope trend: ",round(sum(ksgu_dp_fv),1),if(sum(ksgu_dp_fv)>0){" --> MOISTENING"} else {" --> DRYING"}))## [1] "ksgu dewpoint temperature slope trend: 65.9 --> MOISTENING"print(paste0("ksgu wind direction slope trend: ",round(sum(ksgu_wd_fv),1),if(sum(ksgu_wd_fv)>0){" --> BECOMING MORE WESTERLY/NORTHERLY"} else {" --> BECOMING MORE SOUTHERLY/EASTERLY"}))## [1] "ksgu wind direction slope trend: 3075.8 --> BECOMING MORE WESTERLY/NORTHERLY"print(paste0("ksgu wind speed slope trend: ",round(sum(ksgu_ws_fv),1),if(sum(ksgu_ws_fv)>0){" --> SPEED INCREASING"} else {" --> SPEED DECREASING"}))## [1] "ksgu wind speed slope trend: 10.8 --> SPEED INCREASING"print(paste0("ksgu wind gust slope trend: ",round(sum(ksgu_wg_fv),1),if(sum(ksgu_wg_fv)>0){" --> SPEED INCREASING"} else {" --> SPEED DECREASING"}))## [1] "ksgu wind gust slope trend: 1.3 --> SPEED INCREASING"print(paste0("ksgu cloud ceilings slope trend: ",round(sum(ksgu_cig_fv),1),if(sum(ksgu_cig_fv)>0){" --> CLOUD DECKS RAISING"} else {" --> CLOUD DECKS DROPPING"}))## [1] "ksgu cloud ceilings slope trend: -13107.7 --> CLOUD DECKS DROPPING"print(paste0("ksgu visibility slope trend: ",round(sum(ksgu_vis_fv),1),if(sum(ksgu_vis_fv)>0){" --> VISIBILITY INCREASING"} else {" --> VISIBILITY DECREASING"}))## [1] "ksgu visibility slope trend: -2087.2 --> VISIBILITY DECREASING"print(paste0("ksgu cloud cover slope trend: ",round(sum(ksgu_cc_fv),1),if(sum(ksgu_cc_fv)>0){" --> MORE CLOUDY"} else {" --> LESS CLOUDY"}))## [1] "ksgu cloud cover slope trend: -78.9 --> LESS CLOUDY"print(paste0("ksgu altimeter slope trend: ",round(sum(ksgu_alt_fv),1),if(sum(ksgu_alt_fv)>0){" --> PRESSURE RISING"} else {" --> PRESSURE DROPPING"}))## [1] "ksgu altimeter slope trend: 48.9 --> PRESSURE RISING"print(paste0("ksgu precipitation slope trend: ",round(sum(ksgu_p_fv),1),if(sum(ksgu_p_fv)>0){" --> INCREASING PRECIPITATION"} else {" --> DECREASING PRECIPITATION"}))## [1] "ksgu precipitation slope trend: -212.9 --> DECREASING PRECIPITATION"Trend Recap
In the following table, red-orange represents an increasing number (like increasing temperature) with time. This could also mean a wind direction coming from a more westerly or northwesterly direction instead of a southerly direction. The blue color represents a decreasing number with time (like decreasing temperature). This could also mean a wind direction coming from a more southerly, or southeasterly direction instead of a westerly direction.
| VARIABLE | KRDM | KBUF | KFOE | KMSN | KTRI | PAJN | KELP | KSGU |
|---|---|---|---|---|---|---|---|---|
| Temperature Slope Trend | 145.5 | 137.9 | 67.7 | -37.5 | 218.6 | 124.8 | 197.2 | -38.7 |
| Dewpoint Temperature Slope Trend | 4.8 | -9.6 | 42.2 | -22.5 | 71.6 | 58.5 | -53.5 | 65.9 |
| Wind Direction Slope Trend | -62.7 | -149.5 | 95.7 | 684.1 | 1009.8 | -327.5 | 55.5 | 3075.8 |
| Wind Speed Slope Trend | 21.2 | 0.4 | -16.4 | -20.5 | -11.9 | 14.3 | -7.1 | 10.8 |
| Wind Gust Speed Slope Trend | 86.4 | 60.1 | 12.4 | 30 | -1.4 | 88.8 | 37.3 | 1.3 |
| Cloud Ceiling Slope Trend | -6561.8 | -28329.8 | -41229.8 | -282231.5 | -113176.8 | 6640.3 | -49254.3 | -13107.7 |
| Visibility Slope Trend | -49441.9 | 35858.6 | -22757.6 | 144219.5 | 130862.1 | -250493.3 | -258348.8 | -2087.2 |
| Cloud Cover Slope Trend | -184.1 | -54.5 | -220.3 | 45.7 | -181.7 | -40.3 | -0.3 | -78.9 |
| Altimeter Slope Trend | 27.2 | 77 | 1.9 | 26.7 | 73.2 | 105.8 | -1.5 | 48.9 |
| Precipitation Slope Trend | 135.2 | 1471.2 | 1349.1 | 1108.8 | 605.6 | 1310.7 | 140.1 | -212.9 |
Domain 6
Summary and Deployment
Summary
Observations
Temperatures are warming throughout all eight areas for most of the periods of time. This is anticipated as we use a shorter dataset we can expect to see fluctuations more rapidly.
Dewpoint Temperatures stayed similar over time for all locations.
Wind Direction varies DRASTICALLY by location and time of year.
Aside from KRDM and PAJN, Wind Speeds are fairly similar by each location in magnitude and with peaks and troughs.
Wind Gust Speed varies DRASTICALLY by location and time of year.
Cloud Ceilings vary DRASTICALLY by location and time of year, however, it appears that as we use less data (generally) we see a slight lowering of the ceilings.
Visibility seems to be decreasing (generally) with time as well. However, visibility seems to be worse overall in the wintertime, as you might expect.
Cloud Cover is decreasing with time for all locations.This makes sense as the warming atmosphere will decrease the amount of cloud cover.
Altimeter Setting was difficult to tell the trend with time, however, all locations except PAJN showed a strong drop in the spring-time
Results from Analysis
10-day analysis is statistically significant for all locations and almost all variables.
Wind Speed is the variable that has the worst correlation
Comparing trends of the 10-year periods through time:
There appears to be a consistent warming trend in the summer and a cooling trend in February, meaning, February is getting colder and the summer months are getting warmer than normal.
The transition months of Spring and Fall tend to be more moist (higher dewpoint temperatures), while summer and winter tend to have decreasing dewpoint temperatures (drier)
Wind direction seems to be shifting more to the west (increasing) throughout the year for most locations
Wind speed is not changing much - the slope is nearly zero for all locations
Wind gusts seem to be increasing, although this is a slow rate of change
Cloud ceilings change frequently, however, many locations are showing a general decrease in the cloud ceilings with time
Visibility is not consistent across locations, but there seems to be a trend of slightly worsening (decreasing) visibility over time
Aside from KMSN, all locations are showing a decrease in cloud cover with time
We are seeing frequent oscillations in Altimeter setting for all locations. This means that the low-pressure systems are either getting stronger (lower pressure) or certain days/weeks of the year tend to get more storms, which cause the average pressure to drop more. It doesn’t take a large pressure change to make a storm stronger.
Due to the radically changing conditions, it’s impossible to say for how long these 10-day OCDSs would be valid. However, as the current process is to produce them as data changes, they would be produced until the need no longer exists.
Daily average precipitation seems to be increasing with time.
Deployment
Recommendations
10-years of data is “good enough” - it allows us to visualize the data we want in trend-time we want to have it effective for. Statistically, more years of data weighs the data towards a false normal and doesn’t allow it to change as rapidly. However, 20-years of data may be preferential for calculating average wind speed, cloud cover, visibility and altimeter setting.
10-day intervals for data analysis could prove useful for many reasons - perhaps, the most important being it would assist customers in planning efforts long into the future.
There are not enough similarities to simplify model running by season for each location. If there is an operational need for sub-monthly time periods for an OCDS, then an entire product should be created as the trends vary widely by variable. However, it appears, generally, there is little change during the middle of summer between the various datasets.
There doesn’t appear to be a consistent enough “rate of change” that could forecast out the lifespan of the 10-day OCDS. Thus, the recommendation is to produce them ad hoc for customers on a case by case basis. Ad hoc production allows the product to be created without much additional strain of resources - that is, it doesn’t over-task computing or personnel resources in product creation.
Recommend modifying the Python code within the Data Quality section that produces the OCDS to allow for the 10-day interval as necessary. These could be produced for “high-quality” sites that already have a history of good quality OCDSs being produced.