Consequences of severe weather events on population health and economics across the United States
RLC Velhorst
25-11-2019
Synopsis
This report assessess the NOAA Storm Database to investigate the consequences of severe weather events on population health and economics across the United States. The cumulative effect is calculated per event type. It is concluded that tornadoes are the most harmful with respect to the population health in the U.S. Tornadoes result both in the most fatalaties and the most injuries. Hurricanes/typhoons have the greatest ecomic consequense, as its combined result of crop damage and property damage is the largest of all severe weather events.
Reproducible Research - Project Report Part 2
This is document is created to complete the course on Coursera: Reproducible Research by John Hopkins University. It is part of the Peer-graded Assignments. This report addresses the second assignment of the course, which is given in week 4.
Overview
The basic goal of this assignment is to explore the NOAA Storm Database and answer some basic questions about severe weather events. The two main questions which are adressed are:
- Across the United States, which types of events (as indicated in the variable) are most harmful with respect to population health?
- Across the United States, which types of events have the greatest economic consequences?
The report is structured in the following way:
- Data processing
- Results
- Appendix
Data processing
Data description
Storm Data is an official publication of the National Oceanic and Atmospheric Administration (NOAA) which documents:
+ The occurrence of storms and other significant weather phenomena having sufficient intensity to cause loss of life, injuries, significant property damage, and/or disruption to commerce;
+ Rare, unusual, weather phenomena that generate media attention, such as snow flurries in South Florida or the San Diego coastal area;
+ Other significant meteorological events, such as record maximum or minimum temperatures or precipitation that occur in connection with another event.
Loading data
# Load libraries
library(ggplot2)
library(dplyr)
# library(knitr)
# library(kableExtra)# Load data
fn <- "repdata_data_StormData.csv.bz2"
conn <- bzfile(fn)
# data_ <- readLines(conn)
data_ <- read.csv(conn)
# Add begin date in right format
tmp_DATE <- as.POSIXct(as.character(data_$BGN_DATE),format="%m/%d/%Y %H:%M:%OS")
data_ <- data_ %>% mutate(DATE=tmp_DATE) Compute consequences on population health
To evaluate the consequences of severe weather events on population health, two relevant parameters are identified: fatalities and injuries. For both parameters the average rate per day is computed per event type. By calculating the rate, it does not matter whether or not a parameter is measured for a long period of time. The rate is calculated as the total of fatalities or injuries divideded by the number of days between the first and last occurence. Only events that where witnessed in multiple years are considered to eliminate high rates due to a extremely small period of occurence.
# List the most severe individual events
head(data_[order(data_$FATALITIES,data_$INJURIES,decreasing = TRUE),
c("EVTYPE","FATALITIES","INJURIES")],10)## EVTYPE FATALITIES INJURIES
## 198704 HEAT 583 0
## 862634 TORNADO 158 1150
## 68670 TORNADO 116 785
## 148852 TORNADO 114 597
## 355128 EXCESSIVE HEAT 99 0
## 67884 TORNADO 90 1228
## 46309 TORNADO 75 270
## 371112 EXCESSIVE HEAT 74 135
## 230927 EXCESSIVE HEAT 67 0
## 78567 TORNADO 57 504head(data_[order(data_$INJURIES,data_$FATALITIES,decreasing = TRUE),
c("EVTYPE","FATALITIES","INJURIES")],10)## EVTYPE FATALITIES INJURIES
## 157885 TORNADO 42 1700
## 223449 ICE STORM 1 1568
## 67884 TORNADO 90 1228
## 862634 TORNADO 158 1150
## 116011 TORNADO 36 1150
## 860386 TORNADO 44 800
## 344159 FLOOD 2 800
## 68670 TORNADO 116 785
## 529351 HURRICANE/TYPHOON 7 780
## 344178 FLOOD 0 750# group by event type
by_EV <- data_ %>% group_by(EVTYPE)
# Fatality rate
data_fatalrate <- summarise(by_EV,totalfatal = sum(FATALITIES),
startdate = min(DATE), enddate = max(DATE),
daterange = difftime(enddate,startdate,units="days"),
fatalityrate = totalfatal/as.numeric(daterange)) %>%
filter(daterange>365) %>%
arrange(desc(fatalityrate)) %>%
head(10)
# Injury rate
data_injurerate <- summarise(by_EV,totalinjured = sum(INJURIES),
startdate = min(DATE), enddate = max(DATE),
daterange = difftime(enddate,startdate,units="days"),
injuryrate = totalinjured/as.numeric(daterange)) %>%
filter(daterange>365) %>%
arrange(desc(injuryrate)) %>%
head(10)Compute consequences on economics
To evaluate the consequences of severe weather events on economic damage, two relevant parameters are identified: property damage and crop damage. The total damage is computed as the sum of both property and crop damage. For the total damage, the average rate per day is computed per event type. By calculating the rate, it does not matter whether or not a parameter is measured for a long period of time. The rate is calculated as the cumulative total damage of an event type divideded by the number of days between the first and last occurence. Only events that where witnessed in multiple years are considered to eliminate high rates due to a extremely small period of occurence.
To be able to calculate the total damage, the raw data needs to be converted. The unit of the damage variables is stated in its own column as an exponent. Both exponents for property and crop damage are converted to a numerical factor.
# Compute total damage
# list property damage exponents
prpepx <- unique(data_$PROPDMGEXP)
prpepx## [1] K M B m + 0 5 6 ? 4 2 3 h 7 H - 1 8
## Levels: - ? + 0 1 2 3 4 5 6 7 8 B h H K m M# list crop damage exponents
crpepx <- unique(data_$CROPDMGEXP)
crpepx## [1] M K m B ? 0 k 2
## Levels: ? 0 2 B k K m M# convert damage exponent to numeric factor
exp2factor <- function(exp){
if (exp == "h" | exp == "H"){factor <- 1e2}
else if (exp == "k" | exp == "K"){factor <- 1e3}
else if (exp == "m" | exp == "M"){factor <- 1e6}
else if (exp == "b" | exp == "B"){factor <- 1e9}
else if (exp %in% c("0","1","2","3","4","5","6","7","8","9")){
factor <- 10.0^(as.numeric(as.character(exp)))
} else {factor <- 0.}
factor
}
# Calculate total damage
# Total damage = crop damage* crop exp + property damage * property exp
total_damage <- function(data_){
len <- dim(data_)[1]
out <- vector(mode = "numeric", length = len)
for (ix in 1:len){
# crop damage
crop_damage <- data_[ix,"CROPDMG"]
crop_exp <- data_[ix,"CROPDMGEXP"]
crop_factor <- exp2factor(crop_exp)
# property damage
prop_damage <- data_[ix,"PROPDMG"]
prop_exp <- data_[ix,"PROPDMGEXP"]
prop_factor <- exp2factor(prop_exp)
out[ix] <- crop_damage*crop_factor + prop_damage*prop_factor
}
out
}
# Create dataframe including total damage
data_2 <- mutate(data_,TOTDMG = total_damage(data_))
# List the most severe individual events
head(data_2[order(data_2$TOTDMG,decreasing = TRUE),
c("EVTYPE","TOTDMG", "PROPDMG", "PROPDMGEXP",
"CROPDMG", "CROPDMGEXP")] #,"REMARKS"
,10)## EVTYPE TOTDMG PROPDMG PROPDMGEXP CROPDMG
## 605953 FLOOD 115032500000 115.00 B 32.50
## 577676 STORM SURGE 31300000000 31.30 B 0.00
## 577675 HURRICANE/TYPHOON 16930000000 16.93 B 0.00
## 581535 STORM SURGE 11260000000 11.26 B 0.00
## 198389 RIVER FLOOD 10000000000 5.00 B 5.00
## 569308 HURRICANE/TYPHOON 10000000000 10.00 B 0.00
## 581537 HURRICANE/TYPHOON 7390000000 5.88 B 1.51
## 581533 HURRICANE/TYPHOON 7350000000 7.35 B 0.00
## 529351 HURRICANE/TYPHOON 5705000000 5.42 B 285.00
## 443782 TROPICAL STORM 5150000000 5.15 B 0.00
## CROPDMGEXP
## 605953 M
## 577676
## 577675
## 581535
## 198389 B
## 569308
## 581537 B
## 581533
## 529351 M
## 443782 # kable_styling(latex_options = c("scale_down"))The top severe individual event seems to unrealisticly high. The remark of this event states that the total damage in is the order one hunderd MILLION dollars instead of one hunderd BILLION. This flaw is manually corrected in the dataframe.
# manual correction of NAPA flooding
data_2[605953,"TOTDMG"] = data_2[605953,"PROPDMG"]*1e6 +
data_2[605953,"CROPDMG"]*1e6Finally the data is stored in a proper format, now the cost rate is determined.
# cost rate
by_EV2 <- data_2 %>% group_by(EVTYPE)
data_damagerate <- summarise(by_EV2,totaldamage = sum(TOTDMG),
startdate = min(DATE), enddate = max(DATE),
daterange = difftime(enddate,startdate,units="days"),
damagerate = totaldamage/as.numeric(daterange)) %>%
filter(daterange>365) %>%
arrange(desc(damagerate)) %>%
head(10)
topcosts <- head(data_damagerate[order(data_damagerate$damagerate,
decreasing = TRUE),"EVTYPE"],5)Results
Consequences on population health
The result of the data processing are dataframes which contain the event types with the largest effect on population health. For both parameters fatality and injury the total of occurences and daily rate are calculated and shown below.
It can be concluded that tornados are the most harmful with respect to the population health in the U.S. Tornadoes result both in the most fatalaties and the most injuries. However, when the fatality rate is considered, excessive heat is ranked the highest. Eventhough tornadas have a higher total of fatalities, the fatality rate of tornados is lower than for excessive heat. This is because tornados are recorded over a longer period of time.
# Show highest fatality rates
data_fatalrate## # A tibble: 10 x 6
## EVTYPE totalfatal startdate enddate daterange
## <fct> <dbl> <dttm> <dttm> <drtn>
## 1 EXCES~ 1903 1994-06-27 00:00:00 2011-09-12 00:00:00 6286.00~
## 2 TORNA~ 5633 1950-01-03 00:00:00 2011-11-21 00:00:00 22602.00~
## 3 EXTRE~ 96 1994-06-01 00:00:00 1995-11-01 00:00:00 518.04~
## 4 FLASH~ 978 1993-01-04 00:00:00 2011-11-29 00:00:00 6903.00~
## 5 HEAT 937 1993-08-03 00:00:00 2011-11-15 00:00:00 6678.04~
## 6 LIGHT~ 816 1993-01-03 00:00:00 2011-11-21 00:00:00 6896.00~
## 7 HEAT ~ 172 1994-06-14 00:00:00 1998-08-22 00:00:00 1530.00~
## 8 RIP C~ 204 1995-08-13 00:00:00 2003-02-15 00:00:00 2743.04~
## 9 FLOOD 470 1993-01-01 00:00:00 2011-11-30 00:00:00 6907.00~
## 10 HURRI~ 64 2002-12-08 00:00:00 2005-10-24 00:00:00 1050.95~
## # ... with 1 more variable: fatalityrate <dbl># Show highest injury rates
data_injurerate## # A tibble: 10 x 6
## EVTYPE totalinjured startdate enddate daterange
## <fct> <dbl> <dttm> <dttm> <drtn>
## 1 TORNA~ 91346 1950-01-03 00:00:00 2011-11-21 00:00:00 22602.00~
## 2 HURRI~ 1275 2002-12-08 00:00:00 2005-10-24 00:00:00 1050.95~
## 3 EXCES~ 6525 1994-06-27 00:00:00 2011-09-12 00:00:00 6286.00~
## 4 FLOOD 6789 1993-01-01 00:00:00 2011-11-30 00:00:00 6907.00~
## 5 THUND~ 908 1993-01-04 00:00:00 1995-12-23 00:00:00 1083.00~
## 6 LIGHT~ 5230 1993-01-03 00:00:00 2011-11-21 00:00:00 6896.00~
## 7 TSTM ~ 6957 1955-02-01 00:00:00 2006-09-30 00:00:00 18868.95~
## 8 HEAT 2100 1993-08-03 00:00:00 2011-11-15 00:00:00 6678.04~
## 9 EXTRE~ 155 1994-06-01 00:00:00 1995-11-01 00:00:00 518.04~
## 10 ICE S~ 1975 1993-01-01 00:00:00 2011-11-17 00:00:00 6894.00~
## # ... with 1 more variable: injuryrate <dbl># Plot the fatalities of all severe weather events in time
# of the most severe evtypes, based on fatality rate
# Select top fatalities
topfatal <- head(data_fatalrate[order(data_fatalrate$fatalityrate,
decreasing = TRUE),"EVTYPE"],5)
subset_data <- data_ %>% filter(!is.na(FATALITIES)) %>%
filter(FATALITIES > 0) %>%
filter(EVTYPE %in% topfatal$EVTYPE)
# Plot Fatalities
q <- qplot(x = DATE, y = FATALITIES,
col = EVTYPE,
data = subset_data,
geom = 'point')
p <- q + scale_y_log10()
p
Description of figure
The figure shows the fatalities on logscale of all severe weather events in time. The five most severe types of events are selected to plot, based on fatality rate. The event type is distinquished by color.
It shows that tornadoes are recorded for a much longer period than other types of events.
Consequences on economics
The result of the data processing are dataframes which contain the event types with the largest effect on economics. Adding property damage and crop damage the total damage is calculated and shown below.
It can be concluded that hurricanes have the greatest ecomic consequense, as its combined result of crop damage and property damage is the largest of all severe weather events.
# Show highest injury rates
data_damagerate## # A tibble: 10 x 6
## EVTYPE totaldamage startdate enddate daterange
## <fct> <dbl> <dttm> <dttm> <drtn>
## 1 HURRI~ 7.19e10 2002-12-08 00:00:00 2005-10-24 00:00:00 1050.95~
## 2 STORM~ 4.33e10 1993-03-13 00:00:00 2006-02-01 00:00:00 4708.00~
## 3 FLOOD 3.54e10 1993-01-01 00:00:00 2011-11-30 00:00:00 6907.00~
## 4 RIVER~ 1.01e10 1993-03-05 00:00:00 1998-10-13 00:00:00 2047.95~
## 5 FLASH~ 1.82e10 1993-01-04 00:00:00 2011-11-29 00:00:00 6903.00~
## 6 TORNA~ 5.74e10 1950-01-03 00:00:00 2011-11-21 00:00:00 22602.00~
## 7 DROUG~ 1.50e10 1993-08-01 00:00:00 2011-11-21 00:00:00 6686.04~
## 8 HURRI~ 1.46e10 1993-01-30 00:00:00 2011-08-27 00:00:00 6782.95~
## 9 STORM~ 4.64e 9 2005-10-24 00:00:00 2011-11-25 00:00:00 2223.04~
## 10 THUND~ 2.14e 9 1993-01-04 00:00:00 1995-12-23 00:00:00 1083.00~
## # ... with 1 more variable: damagerate <dbl># Plot the damage of all severe weather events in time
# of the most severe evtypes, based on the total damage rate
subset_data <- data_2 %>% filter(!is.na(TOTDMG)) %>%
filter(TOTDMG > 1e6) %>%
filter(EVTYPE %in% topcosts$EVTYPE)
# Plot damage
q <- qplot(x = DATE, y = TOTDMG,
col = EVTYPE,
data = subset_data,
geom = 'point')
p <- q + scale_y_log10()
p
Description of figure
The figure shows the total damage on logscale of all severe weather events in time. The five most severe types of events are selected to plot, based on total damage rate. The event type is distinquished by color.
Appendix
The appendix consists of:
* The structure od the data
* A summary of the data
* A list of all event types
Structure of data
str(data_)## 'data.frame': 902297 obs. of 38 variables:
## $ STATE__ : num 1 1 1 1 1 1 1 1 1 1 ...
## $ BGN_DATE : Factor w/ 16335 levels "1/1/1966 0:00:00",..: 6523 6523 4242 11116 2224 2224 2260 383 3980 3980 ...
## $ BGN_TIME : Factor w/ 3608 levels "00:00:00 AM",..: 272 287 2705 1683 2584 3186 242 1683 3186 3186 ...
## $ TIME_ZONE : Factor w/ 22 levels "ADT","AKS","AST",..: 7 7 7 7 7 7 7 7 7 7 ...
## $ COUNTY : num 97 3 57 89 43 77 9 123 125 57 ...
## $ COUNTYNAME: Factor w/ 29601 levels "","5NM E OF MACKINAC BRIDGE TO PRESQUE ISLE LT MI",..: 13513 1873 4598 10592 4372 10094 1973 23873 24418 4598 ...
## $ STATE : Factor w/ 72 levels "AK","AL","AM",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ EVTYPE : Factor w/ 985 levels " HIGH SURF ADVISORY",..: 834 834 834 834 834 834 834 834 834 834 ...
## $ BGN_RANGE : num 0 0 0 0 0 0 0 0 0 0 ...
## $ BGN_AZI : Factor w/ 35 levels ""," N"," NW",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ BGN_LOCATI: Factor w/ 54429 levels "","- 1 N Albion",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ END_DATE : Factor w/ 6663 levels "","1/1/1993 0:00:00",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ END_TIME : Factor w/ 3647 levels ""," 0900CST",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ COUNTY_END: num 0 0 0 0 0 0 0 0 0 0 ...
## $ COUNTYENDN: logi NA NA NA NA NA NA ...
## $ END_RANGE : num 0 0 0 0 0 0 0 0 0 0 ...
## $ END_AZI : Factor w/ 24 levels "","E","ENE","ESE",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ END_LOCATI: Factor w/ 34506 levels "","- .5 NNW",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ LENGTH : num 14 2 0.1 0 0 1.5 1.5 0 3.3 2.3 ...
## $ WIDTH : num 100 150 123 100 150 177 33 33 100 100 ...
## $ F : int 3 2 2 2 2 2 2 1 3 3 ...
## $ MAG : num 0 0 0 0 0 0 0 0 0 0 ...
## $ FATALITIES: num 0 0 0 0 0 0 0 0 1 0 ...
## $ INJURIES : num 15 0 2 2 2 6 1 0 14 0 ...
## $ PROPDMG : num 25 2.5 25 2.5 2.5 2.5 2.5 2.5 25 25 ...
## $ PROPDMGEXP: Factor w/ 19 levels "","-","?","+",..: 17 17 17 17 17 17 17 17 17 17 ...
## $ CROPDMG : num 0 0 0 0 0 0 0 0 0 0 ...
## $ CROPDMGEXP: Factor w/ 9 levels "","?","0","2",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ WFO : Factor w/ 542 levels ""," CI","$AC",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ STATEOFFIC: Factor w/ 250 levels "","ALABAMA, Central",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ ZONENAMES : Factor w/ 25112 levels ""," "| __truncated__,..: 1 1 1 1 1 1 1 1 1 1 ...
## $ LATITUDE : num 3040 3042 3340 3458 3412 ...
## $ LONGITUDE : num 8812 8755 8742 8626 8642 ...
## $ LATITUDE_E: num 3051 0 0 0 0 ...
## $ LONGITUDE_: num 8806 0 0 0 0 ...
## $ REMARKS : Factor w/ 436781 levels "","-2 at Deer Park\n",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ REFNUM : num 1 2 3 4 5 6 7 8 9 10 ...
## $ DATE : POSIXct, format: "1950-04-18" "1950-04-18" ...Summary of data
summary(data_)## STATE__ BGN_DATE BGN_TIME
## Min. : 1.0 5/25/2011 0:00:00: 1202 12:00:00 AM: 10163
## 1st Qu.:19.0 4/27/2011 0:00:00: 1193 06:00:00 PM: 7350
## Median :30.0 6/9/2011 0:00:00 : 1030 04:00:00 PM: 7261
## Mean :31.2 5/30/2004 0:00:00: 1016 05:00:00 PM: 6891
## 3rd Qu.:45.0 4/4/2011 0:00:00 : 1009 12:00:00 PM: 6703
## Max. :95.0 4/2/2006 0:00:00 : 981 03:00:00 PM: 6700
## (Other) :895866 (Other) :857229
## TIME_ZONE COUNTY COUNTYNAME STATE
## CST :547493 Min. : 0.0 JEFFERSON : 7840 TX : 83728
## EST :245558 1st Qu.: 31.0 WASHINGTON: 7603 KS : 53440
## MST : 68390 Median : 75.0 JACKSON : 6660 OK : 46802
## PST : 28302 Mean :100.6 FRANKLIN : 6256 MO : 35648
## AST : 6360 3rd Qu.:131.0 LINCOLN : 5937 IA : 31069
## HST : 2563 Max. :873.0 MADISON : 5632 NE : 30271
## (Other): 3631 (Other) :862369 (Other):621339
## EVTYPE BGN_RANGE BGN_AZI
## HAIL :288661 Min. : 0.000 :547332
## TSTM WIND :219940 1st Qu.: 0.000 N : 86752
## THUNDERSTORM WIND: 82563 Median : 0.000 W : 38446
## TORNADO : 60652 Mean : 1.484 S : 37558
## FLASH FLOOD : 54277 3rd Qu.: 1.000 E : 33178
## FLOOD : 25326 Max. :3749.000 NW : 24041
## (Other) :170878 (Other):134990
## BGN_LOCATI END_DATE END_TIME
## :287743 :243411 :238978
## COUNTYWIDE : 19680 4/27/2011 0:00:00: 1214 06:00:00 PM: 9802
## Countywide : 993 5/25/2011 0:00:00: 1196 05:00:00 PM: 8314
## SPRINGFIELD : 843 6/9/2011 0:00:00 : 1021 04:00:00 PM: 8104
## SOUTH PORTION: 810 4/4/2011 0:00:00 : 1007 12:00:00 PM: 7483
## NORTH PORTION: 784 5/30/2004 0:00:00: 998 11:59:00 PM: 7184
## (Other) :591444 (Other) :653450 (Other) :622432
## COUNTY_END COUNTYENDN END_RANGE END_AZI
## Min. :0 Mode:logical Min. : 0.0000 :724837
## 1st Qu.:0 NA's:902297 1st Qu.: 0.0000 N : 28082
## Median :0 Median : 0.0000 S : 22510
## Mean :0 Mean : 0.9862 W : 20119
## 3rd Qu.:0 3rd Qu.: 0.0000 E : 20047
## Max. :0 Max. :925.0000 NE : 14606
## (Other): 72096
## END_LOCATI LENGTH WIDTH
## :499225 Min. : 0.0000 Min. : 0.000
## COUNTYWIDE : 19731 1st Qu.: 0.0000 1st Qu.: 0.000
## SOUTH PORTION : 833 Median : 0.0000 Median : 0.000
## NORTH PORTION : 780 Mean : 0.2301 Mean : 7.503
## CENTRAL PORTION: 617 3rd Qu.: 0.0000 3rd Qu.: 0.000
## SPRINGFIELD : 575 Max. :2315.0000 Max. :4400.000
## (Other) :380536
## F MAG FATALITIES INJURIES
## Min. :0.0 Min. : 0.0 Min. : 0.0000 Min. : 0.0000
## 1st Qu.:0.0 1st Qu.: 0.0 1st Qu.: 0.0000 1st Qu.: 0.0000
## Median :1.0 Median : 50.0 Median : 0.0000 Median : 0.0000
## Mean :0.9 Mean : 46.9 Mean : 0.0168 Mean : 0.1557
## 3rd Qu.:1.0 3rd Qu.: 75.0 3rd Qu.: 0.0000 3rd Qu.: 0.0000
## Max. :5.0 Max. :22000.0 Max. :583.0000 Max. :1700.0000
## NA's :843563
## PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## Min. : 0.00 :465934 Min. : 0.000 :618413
## 1st Qu.: 0.00 K :424665 1st Qu.: 0.000 K :281832
## Median : 0.00 M : 11330 Median : 0.000 M : 1994
## Mean : 12.06 0 : 216 Mean : 1.527 k : 21
## 3rd Qu.: 0.50 B : 40 3rd Qu.: 0.000 0 : 19
## Max. :5000.00 5 : 28 Max. :990.000 B : 9
## (Other): 84 (Other): 9
## WFO STATEOFFIC
## :142069 :248769
## OUN : 17393 TEXAS, North : 12193
## JAN : 13889 ARKANSAS, Central and North Central: 11738
## LWX : 13174 IOWA, Central : 11345
## PHI : 12551 KANSAS, Southwest : 11212
## TSA : 12483 GEORGIA, North and Central : 11120
## (Other):690738 (Other) :595920
## ZONENAMES
## :594029
## :205988
## GREATER RENO / CARSON CITY / M - GREATER RENO / CARSON CITY / M : 639
## GREATER LAKE TAHOE AREA - GREATER LAKE TAHOE AREA : 592
## JEFFERSON - JEFFERSON : 303
## MADISON - MADISON : 302
## (Other) :100444
## LATITUDE LONGITUDE LATITUDE_E LONGITUDE_
## Min. : 0 Min. :-14451 Min. : 0 Min. :-14455
## 1st Qu.:2802 1st Qu.: 7247 1st Qu.: 0 1st Qu.: 0
## Median :3540 Median : 8707 Median : 0 Median : 0
## Mean :2875 Mean : 6940 Mean :1452 Mean : 3509
## 3rd Qu.:4019 3rd Qu.: 9605 3rd Qu.:3549 3rd Qu.: 8735
## Max. :9706 Max. : 17124 Max. :9706 Max. :106220
## NA's :47 NA's :40
## REMARKS REFNUM
## :287433 Min. : 1
## : 24013 1st Qu.:225575
## Trees down.\n : 1110 Median :451149
## Several trees were blown down.\n : 568 Mean :451149
## Trees were downed.\n : 446 3rd Qu.:676723
## Large trees and power lines were blown down.\n: 432 Max. :902297
## (Other) :588295
## DATE
## Min. :1950-01-03 00:00:00
## 1st Qu.:1995-04-20 00:00:00
## Median :2002-03-18 00:00:00
## Mean :1998-12-27 22:53:36
## 3rd Qu.:2007-07-28 00:00:00
## Max. :2011-11-30 00:00:00
## List of all event types
types <- unique(data_$EVTYPE)
types[order(types)]## [1] HIGH SURF ADVISORY COASTAL FLOOD
## [3] FLASH FLOOD LIGHTNING
## [5] TSTM WIND TSTM WIND (G45)
## [7] WATERSPOUT WIND
## [9] ? ABNORMAL WARMTH
## [11] ABNORMALLY DRY ABNORMALLY WET
## [13] ACCUMULATED SNOWFALL AGRICULTURAL FREEZE
## [15] APACHE COUNTY ASTRONOMICAL HIGH TIDE
## [17] ASTRONOMICAL LOW TIDE AVALANCE
## [19] AVALANCHE BEACH EROSIN
## [21] Beach Erosion BEACH EROSION
## [23] BEACH EROSION/COASTAL FLOOD BEACH FLOOD
## [25] BELOW NORMAL PRECIPITATION BITTER WIND CHILL
## [27] BITTER WIND CHILL TEMPERATURES Black Ice
## [29] BLACK ICE BLIZZARD
## [31] BLIZZARD AND EXTREME WIND CHIL BLIZZARD AND HEAVY SNOW
## [33] Blizzard Summary BLIZZARD WEATHER
## [35] BLIZZARD/FREEZING RAIN BLIZZARD/HEAVY SNOW
## [37] BLIZZARD/HIGH WIND BLIZZARD/WINTER STORM
## [39] BLOW-OUT TIDE BLOW-OUT TIDES
## [41] BLOWING DUST blowing snow
## [43] Blowing Snow BLOWING SNOW
## [45] BLOWING SNOW- EXTREME WIND CHI BLOWING SNOW & EXTREME WIND CH
## [47] BLOWING SNOW/EXTREME WIND CHIL BREAKUP FLOODING
## [49] BRUSH FIRE BRUSH FIRES
## [51] COASTAL FLOODING/EROSION COASTAL EROSION
## [53] Coastal Flood COASTAL FLOOD
## [55] coastal flooding Coastal Flooding
## [57] COASTAL FLOODING COASTAL FLOODING/EROSION
## [59] Coastal Storm COASTAL STORM
## [61] COASTAL SURGE COASTAL/TIDAL FLOOD
## [63] COASTALFLOOD COASTALSTORM
## [65] Cold COLD
## [67] COLD AIR FUNNEL COLD AIR FUNNELS
## [69] COLD AIR TORNADO Cold and Frost
## [71] COLD AND FROST COLD AND SNOW
## [73] COLD AND WET CONDITIONS Cold Temperature
## [75] COLD TEMPERATURES COLD WAVE
## [77] COLD WEATHER COLD WIND CHILL TEMPERATURES
## [79] COLD/WIND CHILL COLD/WINDS
## [81] COOL AND WET COOL SPELL
## [83] CSTL FLOODING/EROSION DAM BREAK
## [85] DAM FAILURE Damaging Freeze
## [87] DAMAGING FREEZE DEEP HAIL
## [89] DENSE FOG DENSE SMOKE
## [91] DOWNBURST DOWNBURST WINDS
## [93] DRIEST MONTH Drifting Snow
## [95] DROUGHT DROUGHT/EXCESSIVE HEAT
## [97] DROWNING DRY
## [99] DRY CONDITIONS DRY HOT WEATHER
## [101] DRY MICROBURST DRY MICROBURST 50
## [103] DRY MICROBURST 53 DRY MICROBURST 58
## [105] DRY MICROBURST 61 DRY MICROBURST 84
## [107] DRY MICROBURST WINDS DRY MIRCOBURST WINDS
## [109] DRY PATTERN DRY SPELL
## [111] DRY WEATHER DRYNESS
## [113] DUST DEVEL Dust Devil
## [115] DUST DEVIL DUST DEVIL WATERSPOUT
## [117] DUST STORM DUST STORM/HIGH WINDS
## [119] DUSTSTORM EARLY FREEZE
## [121] Early Frost EARLY FROST
## [123] EARLY RAIN EARLY SNOW
## [125] Early snowfall EARLY SNOWFALL
## [127] Erosion/Cstl Flood EXCESSIVE
## [129] Excessive Cold EXCESSIVE HEAT
## [131] EXCESSIVE HEAT/DROUGHT EXCESSIVE PRECIPITATION
## [133] EXCESSIVE RAIN EXCESSIVE RAINFALL
## [135] EXCESSIVE SNOW EXCESSIVE WETNESS
## [137] EXCESSIVELY DRY Extended Cold
## [139] Extreme Cold EXTREME COLD
## [141] EXTREME COLD/WIND CHILL EXTREME HEAT
## [143] EXTREME WIND CHILL EXTREME WIND CHILL/BLOWING SNO
## [145] EXTREME WIND CHILLS EXTREME WINDCHILL
## [147] EXTREME WINDCHILL TEMPERATURES EXTREME/RECORD COLD
## [149] EXTREMELY WET FALLING SNOW/ICE
## [151] FIRST FROST FIRST SNOW
## [153] FLASH FLOOD FLASH FLOOD - HEAVY RAIN
## [155] FLASH FLOOD FROM ICE JAMS FLASH FLOOD LANDSLIDES
## [157] FLASH FLOOD WINDS FLASH FLOOD/
## [159] FLASH FLOOD/ FLOOD FLASH FLOOD/ STREET
## [161] FLASH FLOOD/FLOOD FLASH FLOOD/HEAVY RAIN
## [163] FLASH FLOOD/LANDSLIDE FLASH FLOODING
## [165] FLASH FLOODING/FLOOD FLASH FLOODING/THUNDERSTORM WI
## [167] FLASH FLOODS FLASH FLOOODING
## [169] Flood FLOOD
## [171] FLOOD & HEAVY RAIN FLOOD FLASH
## [173] FLOOD FLOOD/FLASH FLOOD WATCH/
## [175] FLOOD/FLASH Flood/Flash Flood
## [177] FLOOD/FLASH FLOOD FLOOD/FLASH FLOODING
## [179] FLOOD/FLASH/FLOOD FLOOD/FLASHFLOOD
## [181] FLOOD/RAIN/WIND FLOOD/RAIN/WINDS
## [183] FLOOD/RIVER FLOOD Flood/Strong Wind
## [185] FLOODING FLOODING/HEAVY RAIN
## [187] FLOODS FOG
## [189] FOG AND COLD TEMPERATURES FOREST FIRES
## [191] Freeze FREEZE
## [193] Freezing drizzle Freezing Drizzle
## [195] FREEZING DRIZZLE FREEZING DRIZZLE AND FREEZING
## [197] Freezing Fog FREEZING FOG
## [199] Freezing rain Freezing Rain
## [201] FREEZING RAIN FREEZING RAIN AND SLEET
## [203] FREEZING RAIN AND SNOW FREEZING RAIN SLEET AND
## [205] FREEZING RAIN SLEET AND LIGHT FREEZING RAIN/SLEET
## [207] FREEZING RAIN/SNOW Freezing Spray
## [209] Frost FROST
## [211] Frost/Freeze FROST/FREEZE
## [213] FROST\\FREEZE FUNNEL
## [215] Funnel Cloud FUNNEL CLOUD
## [217] FUNNEL CLOUD. FUNNEL CLOUD/HAIL
## [219] FUNNEL CLOUDS FUNNELS
## [221] Glaze GLAZE
## [223] GLAZE ICE GLAZE/ICE STORM
## [225] gradient wind Gradient wind
## [227] GRADIENT WIND GRADIENT WINDS
## [229] GRASS FIRES GROUND BLIZZARD
## [231] GUSTNADO GUSTNADO AND
## [233] GUSTY LAKE WIND GUSTY THUNDERSTORM WIND
## [235] GUSTY THUNDERSTORM WINDS Gusty Wind
## [237] GUSTY WIND GUSTY WIND/HAIL
## [239] GUSTY WIND/HVY RAIN Gusty wind/rain
## [241] Gusty winds Gusty Winds
## [243] GUSTY WINDS HAIL
## [245] HAIL 0.75 HAIL 0.88
## [247] HAIL 075 HAIL 088
## [249] HAIL 1.00 HAIL 1.75
## [251] HAIL 1.75) HAIL 100
## [253] HAIL 125 HAIL 150
## [255] HAIL 175 HAIL 200
## [257] HAIL 225 HAIL 275
## [259] HAIL 450 HAIL 75
## [261] HAIL 80 HAIL 88
## [263] HAIL ALOFT HAIL DAMAGE
## [265] HAIL FLOODING HAIL STORM
## [267] Hail(0.75) HAIL/ICY ROADS
## [269] HAIL/WIND HAIL/WINDS
## [271] HAILSTORM HAILSTORMS
## [273] HARD FREEZE HAZARDOUS SURF
## [275] HEAT HEAT DROUGHT
## [277] Heat Wave HEAT WAVE
## [279] HEAT WAVE DROUGHT HEAT WAVES
## [281] HEAT/DROUGHT Heatburst
## [283] HEAVY LAKE SNOW HEAVY MIX
## [285] HEAVY PRECIPATATION Heavy Precipitation
## [287] HEAVY PRECIPITATION Heavy rain
## [289] Heavy Rain HEAVY RAIN
## [291] HEAVY RAIN AND FLOOD Heavy Rain and Wind
## [293] HEAVY RAIN EFFECTS HEAVY RAIN/FLOODING
## [295] Heavy Rain/High Surf HEAVY RAIN/LIGHTNING
## [297] HEAVY RAIN/MUDSLIDES/FLOOD HEAVY RAIN/SEVERE WEATHER
## [299] HEAVY RAIN/SMALL STREAM URBAN HEAVY RAIN/SNOW
## [301] HEAVY RAIN/URBAN FLOOD HEAVY RAIN/WIND
## [303] HEAVY RAIN; URBAN FLOOD WINDS; HEAVY RAINFALL
## [305] HEAVY RAINS HEAVY RAINS/FLOODING
## [307] HEAVY SEAS HEAVY SHOWER
## [309] HEAVY SHOWERS HEAVY SNOW
## [311] HEAVY SNOW-SQUALLS HEAVY SNOW FREEZING RAIN
## [313] HEAVY SNOW & ICE HEAVY SNOW AND
## [315] HEAVY SNOW AND HIGH WINDS HEAVY SNOW AND ICE
## [317] HEAVY SNOW AND ICE STORM HEAVY SNOW AND STRONG WINDS
## [319] HEAVY SNOW ANDBLOWING SNOW Heavy snow shower
## [321] HEAVY SNOW SQUALLS HEAVY SNOW/BLIZZARD
## [323] HEAVY SNOW/BLIZZARD/AVALANCHE HEAVY SNOW/BLOWING SNOW
## [325] HEAVY SNOW/FREEZING RAIN HEAVY SNOW/HIGH
## [327] HEAVY SNOW/HIGH WIND HEAVY SNOW/HIGH WINDS
## [329] HEAVY SNOW/HIGH WINDS & FLOOD HEAVY SNOW/HIGH WINDS/FREEZING
## [331] HEAVY SNOW/ICE HEAVY SNOW/ICE STORM
## [333] HEAVY SNOW/SLEET HEAVY SNOW/SQUALLS
## [335] HEAVY SNOW/WIND HEAVY SNOW/WINTER STORM
## [337] HEAVY SNOWPACK Heavy Surf
## [339] HEAVY SURF Heavy surf and wind
## [341] HEAVY SURF COASTAL FLOODING HEAVY SURF/HIGH SURF
## [343] HEAVY SWELLS HEAVY WET SNOW
## [345] HIGH HIGH SWELLS
## [347] HIGH WINDS HIGH SEAS
## [349] High Surf HIGH SURF
## [351] HIGH SURF ADVISORIES HIGH SURF ADVISORY
## [353] HIGH SWELLS HIGH TEMPERATURE RECORD
## [355] HIGH TIDES HIGH WATER
## [357] HIGH WAVES High Wind
## [359] HIGH WIND HIGH WIND (G40)
## [361] HIGH WIND 48 HIGH WIND 63
## [363] HIGH WIND 70 HIGH WIND AND HEAVY SNOW
## [365] HIGH WIND AND HIGH TIDES HIGH WIND AND SEAS
## [367] HIGH WIND DAMAGE HIGH WIND/ BLIZZARD
## [369] HIGH WIND/BLIZZARD HIGH WIND/BLIZZARD/FREEZING RA
## [371] HIGH WIND/HEAVY SNOW HIGH WIND/LOW WIND CHILL
## [373] HIGH WIND/SEAS HIGH WIND/WIND CHILL
## [375] HIGH WIND/WIND CHILL/BLIZZARD HIGH WINDS
## [377] HIGH WINDS 55 HIGH WINDS 57
## [379] HIGH WINDS 58 HIGH WINDS 63
## [381] HIGH WINDS 66 HIGH WINDS 67
## [383] HIGH WINDS 73 HIGH WINDS 76
## [385] HIGH WINDS 80 HIGH WINDS 82
## [387] HIGH WINDS AND WIND CHILL HIGH WINDS DUST STORM
## [389] HIGH WINDS HEAVY RAINS HIGH WINDS/
## [391] HIGH WINDS/COASTAL FLOOD HIGH WINDS/COLD
## [393] HIGH WINDS/FLOODING HIGH WINDS/HEAVY RAIN
## [395] HIGH WINDS/SNOW HIGHWAY FLOODING
## [397] Hot and Dry HOT PATTERN
## [399] HOT SPELL HOT WEATHER
## [401] HOT/DRY PATTERN HURRICANE
## [403] HURRICANE-GENERATED SWELLS Hurricane Edouard
## [405] HURRICANE EMILY HURRICANE ERIN
## [407] HURRICANE FELIX HURRICANE GORDON
## [409] HURRICANE OPAL HURRICANE OPAL/HIGH WINDS
## [411] HURRICANE/TYPHOON HVY RAIN
## [413] HYPERTHERMIA/EXPOSURE HYPOTHERMIA
## [415] Hypothermia/Exposure HYPOTHERMIA/EXPOSURE
## [417] ICE ICE AND SNOW
## [419] ICE FLOES Ice Fog
## [421] ICE JAM Ice jam flood (minor
## [423] ICE JAM FLOODING ICE ON ROAD
## [425] ICE PELLETS ICE ROADS
## [427] ICE STORM ICE STORM AND SNOW
## [429] ICE STORM/FLASH FLOOD Ice/Snow
## [431] ICE/SNOW ICE/STRONG WINDS
## [433] Icestorm/Blizzard Icy Roads
## [435] ICY ROADS LACK OF SNOW
## [437] LAKE-EFFECT SNOW Lake Effect Snow
## [439] LAKE EFFECT SNOW LAKE FLOOD
## [441] LAKESHORE FLOOD LANDSLIDE
## [443] LANDSLIDE/URBAN FLOOD LANDSLIDES
## [445] Landslump LANDSLUMP
## [447] LANDSPOUT LARGE WALL CLOUD
## [449] Late-season Snowfall LATE FREEZE
## [451] LATE SEASON HAIL LATE SEASON SNOW
## [453] Late Season Snowfall LATE SNOW
## [455] LIGHT FREEZING RAIN Light snow
## [457] Light Snow LIGHT SNOW
## [459] LIGHT SNOW AND SLEET Light Snow/Flurries
## [461] LIGHT SNOW/FREEZING PRECIP Light Snowfall
## [463] LIGHTING LIGHTNING
## [465] LIGHTNING WAUSEON LIGHTNING AND HEAVY RAIN
## [467] LIGHTNING AND THUNDERSTORM WIN LIGHTNING AND WINDS
## [469] LIGHTNING DAMAGE LIGHTNING FIRE
## [471] LIGHTNING INJURY LIGHTNING THUNDERSTORM WINDS
## [473] LIGHTNING THUNDERSTORM WINDSS LIGHTNING.
## [475] LIGHTNING/HEAVY RAIN LIGNTNING
## [477] LOCAL FLASH FLOOD LOCAL FLOOD
## [479] LOCALLY HEAVY RAIN LOW TEMPERATURE
## [481] LOW TEMPERATURE RECORD LOW WIND CHILL
## [483] MAJOR FLOOD Marine Accident
## [485] MARINE HAIL MARINE HIGH WIND
## [487] MARINE MISHAP MARINE STRONG WIND
## [489] MARINE THUNDERSTORM WIND MARINE TSTM WIND
## [491] Metro Storm, May 26 Microburst
## [493] MICROBURST MICROBURST WINDS
## [495] Mild and Dry Pattern MILD PATTERN
## [497] MILD/DRY PATTERN MINOR FLOOD
## [499] Minor Flooding MINOR FLOODING
## [501] MIXED PRECIP Mixed Precipitation
## [503] MIXED PRECIPITATION MODERATE SNOW
## [505] MODERATE SNOWFALL MONTHLY PRECIPITATION
## [507] Monthly Rainfall MONTHLY RAINFALL
## [509] Monthly Snowfall MONTHLY SNOWFALL
## [511] MONTHLY TEMPERATURE Mountain Snows
## [513] MUD SLIDE MUD SLIDES
## [515] MUD SLIDES URBAN FLOODING MUD/ROCK SLIDE
## [517] Mudslide MUDSLIDE
## [519] MUDSLIDE/LANDSLIDE Mudslides
## [521] MUDSLIDES NEAR RECORD SNOW
## [523] No Severe Weather NON-SEVERE WIND DAMAGE
## [525] NON-TSTM WIND NON SEVERE HAIL
## [527] NON TSTM WIND NONE
## [529] NORMAL PRECIPITATION NORTHERN LIGHTS
## [531] Other OTHER
## [533] PATCHY DENSE FOG PATCHY ICE
## [535] Prolong Cold PROLONG COLD
## [537] PROLONG COLD/SNOW PROLONG WARMTH
## [539] PROLONGED RAIN RAIN
## [541] RAIN (HEAVY) RAIN AND WIND
## [543] Rain Damage RAIN/SNOW
## [545] RAIN/WIND RAINSTORM
## [547] RAPIDLY RISING WATER RECORD COLD
## [549] Record Cold RECORD COLD
## [551] RECORD COLD AND HIGH WIND RECORD COLD/FROST
## [553] RECORD COOL Record dry month
## [555] RECORD DRYNESS Record Heat
## [557] RECORD HEAT RECORD HEAT WAVE
## [559] Record High RECORD HIGH
## [561] RECORD HIGH TEMPERATURE RECORD HIGH TEMPERATURES
## [563] RECORD LOW RECORD LOW RAINFALL
## [565] Record May Snow RECORD PRECIPITATION
## [567] RECORD RAINFALL RECORD SNOW
## [569] RECORD SNOW/COLD RECORD SNOWFALL
## [571] Record temperature RECORD TEMPERATURE
## [573] Record Temperatures RECORD TEMPERATURES
## [575] RECORD WARM RECORD WARM TEMPS.
## [577] Record Warmth RECORD WARMTH
## [579] Record Winter Snow RECORD/EXCESSIVE HEAT
## [581] RECORD/EXCESSIVE RAINFALL RED FLAG CRITERIA
## [583] RED FLAG FIRE WX REMNANTS OF FLOYD
## [585] RIP CURRENT RIP CURRENTS
## [587] RIP CURRENTS HEAVY SURF RIP CURRENTS/HEAVY SURF
## [589] RIVER AND STREAM FLOOD RIVER FLOOD
## [591] River Flooding RIVER FLOODING
## [593] ROCK SLIDE ROGUE WAVE
## [595] ROTATING WALL CLOUD ROUGH SEAS
## [597] ROUGH SURF RURAL FLOOD
## [599] Saharan Dust SAHARAN DUST
## [601] Seasonal Snowfall SEICHE
## [603] SEVERE COLD SEVERE THUNDERSTORM
## [605] SEVERE THUNDERSTORM WINDS SEVERE THUNDERSTORMS
## [607] SEVERE TURBULENCE SLEET
## [609] SLEET & FREEZING RAIN SLEET STORM
## [611] SLEET/FREEZING RAIN SLEET/ICE STORM
## [613] SLEET/RAIN/SNOW SLEET/SNOW
## [615] small hail Small Hail
## [617] SMALL HAIL SMALL STREAM
## [619] SMALL STREAM AND SMALL STREAM AND URBAN FLOOD
## [621] SMALL STREAM AND URBAN FLOODIN SMALL STREAM FLOOD
## [623] SMALL STREAM FLOODING SMALL STREAM URBAN FLOOD
## [625] SMALL STREAM/URBAN FLOOD Sml Stream Fld
## [627] SMOKE Snow
## [629] SNOW SNOW- HIGH WIND- WIND CHILL
## [631] Snow Accumulation SNOW ACCUMULATION
## [633] SNOW ADVISORY SNOW AND COLD
## [635] SNOW AND HEAVY SNOW Snow and Ice
## [637] SNOW AND ICE SNOW AND ICE STORM
## [639] Snow and sleet SNOW AND SLEET
## [641] SNOW AND WIND SNOW DROUGHT
## [643] SNOW FREEZING RAIN SNOW SHOWERS
## [645] SNOW SLEET SNOW SQUALL
## [647] Snow squalls Snow Squalls
## [649] SNOW SQUALLS SNOW/ BITTER COLD
## [651] SNOW/ ICE SNOW/BLOWING SNOW
## [653] SNOW/COLD SNOW/FREEZING RAIN
## [655] SNOW/HEAVY SNOW SNOW/HIGH WINDS
## [657] SNOW/ICE SNOW/ICE STORM
## [659] SNOW/RAIN SNOW/RAIN/SLEET
## [661] SNOW/SLEET SNOW/SLEET/FREEZING RAIN
## [663] SNOW/SLEET/RAIN SNOW\\COLD
## [665] SNOWFALL RECORD SNOWMELT FLOODING
## [667] SNOWSTORM SOUTHEAST
## [669] STORM FORCE WINDS STORM SURGE
## [671] STORM SURGE/TIDE STREAM FLOODING
## [673] STREET FLOOD STREET FLOODING
## [675] Strong Wind STRONG WIND
## [677] STRONG WIND GUST Strong winds
## [679] Strong Winds STRONG WINDS
## [681] Summary August 10 Summary August 11
## [683] Summary August 17 Summary August 2-3
## [685] Summary August 21 Summary August 28
## [687] Summary August 4 Summary August 7
## [689] Summary August 9 Summary Jan 17
## [691] Summary July 23-24 Summary June 18-19
## [693] Summary June 5-6 Summary June 6
## [695] Summary of April 12 Summary of April 13
## [697] Summary of April 21 Summary of April 27
## [699] Summary of April 3rd Summary of August 1
## [701] Summary of July 11 Summary of July 2
## [703] Summary of July 22 Summary of July 26
## [705] Summary of July 29 Summary of July 3
## [707] Summary of June 10 Summary of June 11
## [709] Summary of June 12 Summary of June 13
## [711] Summary of June 15 Summary of June 16
## [713] Summary of June 18 Summary of June 23
## [715] Summary of June 24 Summary of June 3
## [717] Summary of June 30 Summary of June 4
## [719] Summary of June 6 Summary of March 14
## [721] Summary of March 23 Summary of March 24
## [723] SUMMARY OF MARCH 24-25 SUMMARY OF MARCH 27
## [725] SUMMARY OF MARCH 29 Summary of May 10
## [727] Summary of May 13 Summary of May 14
## [729] Summary of May 22 Summary of May 22 am
## [731] Summary of May 22 pm Summary of May 26 am
## [733] Summary of May 26 pm Summary of May 31 am
## [735] Summary of May 31 pm Summary of May 9-10
## [737] Summary Sept. 25-26 Summary September 20
## [739] Summary September 23 Summary September 3
## [741] Summary September 4 Summary: Nov. 16
## [743] Summary: Nov. 6-7 Summary: Oct. 20-21
## [745] Summary: October 31 Summary: Sept. 18
## [747] Temperature record THUDERSTORM WINDS
## [749] THUNDEERSTORM WINDS THUNDERESTORM WINDS
## [751] THUNDERSNOW Thundersnow shower
## [753] THUNDERSTORM THUNDERSTORM WINDS
## [755] THUNDERSTORM DAMAGE THUNDERSTORM DAMAGE TO
## [757] THUNDERSTORM HAIL THUNDERSTORM W INDS
## [759] Thunderstorm Wind THUNDERSTORM WIND
## [761] THUNDERSTORM WIND (G40) THUNDERSTORM WIND 50
## [763] THUNDERSTORM WIND 52 THUNDERSTORM WIND 56
## [765] THUNDERSTORM WIND 59 THUNDERSTORM WIND 59 MPH
## [767] THUNDERSTORM WIND 59 MPH. THUNDERSTORM WIND 60 MPH
## [769] THUNDERSTORM WIND 65 MPH THUNDERSTORM WIND 65MPH
## [771] THUNDERSTORM WIND 69 THUNDERSTORM WIND 98 MPH
## [773] THUNDERSTORM WIND G50 THUNDERSTORM WIND G51
## [775] THUNDERSTORM WIND G52 THUNDERSTORM WIND G55
## [777] THUNDERSTORM WIND G60 THUNDERSTORM WIND G61
## [779] THUNDERSTORM WIND TREES THUNDERSTORM WIND.
## [781] THUNDERSTORM WIND/ TREE THUNDERSTORM WIND/ TREES
## [783] THUNDERSTORM WIND/AWNING THUNDERSTORM WIND/HAIL
## [785] THUNDERSTORM WIND/LIGHTNING THUNDERSTORM WINDS
## [787] THUNDERSTORM WINDS LE CEN THUNDERSTORM WINDS 13
## [789] THUNDERSTORM WINDS 2 THUNDERSTORM WINDS 50
## [791] THUNDERSTORM WINDS 52 THUNDERSTORM WINDS 53
## [793] THUNDERSTORM WINDS 60 THUNDERSTORM WINDS 61
## [795] THUNDERSTORM WINDS 62 THUNDERSTORM WINDS 63 MPH
## [797] THUNDERSTORM WINDS AND THUNDERSTORM WINDS FUNNEL CLOU
## [799] THUNDERSTORM WINDS G THUNDERSTORM WINDS G60
## [801] THUNDERSTORM WINDS HAIL THUNDERSTORM WINDS HEAVY RAIN
## [803] THUNDERSTORM WINDS LIGHTNING THUNDERSTORM WINDS SMALL STREA
## [805] THUNDERSTORM WINDS URBAN FLOOD THUNDERSTORM WINDS.
## [807] THUNDERSTORM WINDS/ FLOOD THUNDERSTORM WINDS/ HAIL
## [809] THUNDERSTORM WINDS/FLASH FLOOD THUNDERSTORM WINDS/FLOODING
## [811] THUNDERSTORM WINDS/FUNNEL CLOU THUNDERSTORM WINDS/HAIL
## [813] THUNDERSTORM WINDS/HEAVY RAIN THUNDERSTORM WINDS53
## [815] THUNDERSTORM WINDSHAIL THUNDERSTORM WINDSS
## [817] THUNDERSTORM WINS THUNDERSTORMS
## [819] THUNDERSTORMS WIND THUNDERSTORMS WINDS
## [821] THUNDERSTORMW THUNDERSTORMW 50
## [823] THUNDERSTORMW WINDS THUNDERSTORMWINDS
## [825] THUNDERSTROM WIND THUNDERSTROM WINDS
## [827] THUNDERTORM WINDS THUNDERTSORM WIND
## [829] THUNDESTORM WINDS THUNERSTORM WINDS
## [831] TIDAL FLOOD Tidal Flooding
## [833] TIDAL FLOODING TORNADO
## [835] TORNADO DEBRIS TORNADO F0
## [837] TORNADO F1 TORNADO F2
## [839] TORNADO F3 TORNADO/WATERSPOUT
## [841] TORNADOES TORNADOES, TSTM WIND, HAIL
## [843] TORNADOS TORNDAO
## [845] TORRENTIAL RAIN Torrential Rainfall
## [847] TROPICAL DEPRESSION TROPICAL STORM
## [849] TROPICAL STORM ALBERTO TROPICAL STORM DEAN
## [851] TROPICAL STORM GORDON TROPICAL STORM JERRY
## [853] TSTM TSTM HEAVY RAIN
## [855] Tstm Wind TSTM WIND
## [857] TSTM WIND (G45) TSTM WIND (41)
## [859] TSTM WIND (G35) TSTM WIND (G40)
## [861] TSTM WIND (G45) TSTM WIND 40
## [863] TSTM WIND 45 TSTM WIND 50
## [865] TSTM WIND 51 TSTM WIND 52
## [867] TSTM WIND 55 TSTM WIND 65)
## [869] TSTM WIND AND LIGHTNING TSTM WIND DAMAGE
## [871] TSTM WIND G45 TSTM WIND G58
## [873] TSTM WIND/HAIL TSTM WINDS
## [875] TSTM WND TSTMW
## [877] TSUNAMI TUNDERSTORM WIND
## [879] TYPHOON Unseasonable Cold
## [881] UNSEASONABLY COLD UNSEASONABLY COOL
## [883] UNSEASONABLY COOL & WET UNSEASONABLY DRY
## [885] UNSEASONABLY HOT UNSEASONABLY WARM
## [887] UNSEASONABLY WARM & WET UNSEASONABLY WARM AND DRY
## [889] UNSEASONABLY WARM YEAR UNSEASONABLY WARM/WET
## [891] UNSEASONABLY WET UNSEASONAL LOW TEMP
## [893] UNSEASONAL RAIN UNUSUAL WARMTH
## [895] UNUSUAL/RECORD WARMTH UNUSUALLY COLD
## [897] UNUSUALLY LATE SNOW UNUSUALLY WARM
## [899] URBAN AND SMALL URBAN AND SMALL STREAM
## [901] URBAN AND SMALL STREAM FLOOD URBAN AND SMALL STREAM FLOODIN
## [903] Urban flood Urban Flood
## [905] URBAN FLOOD URBAN FLOOD LANDSLIDE
## [907] Urban Flooding URBAN FLOODING
## [909] URBAN FLOODS URBAN SMALL
## [911] URBAN SMALL STREAM FLOOD URBAN/SMALL
## [913] URBAN/SMALL FLOODING URBAN/SMALL STREAM
## [915] URBAN/SMALL STREAM FLOOD URBAN/SMALL STREAM FLOOD
## [917] URBAN/SMALL STREAM FLOODING URBAN/SMALL STRM FLDG
## [919] URBAN/SML STREAM FLD URBAN/SML STREAM FLDG
## [921] URBAN/STREET FLOODING VERY DRY
## [923] VERY WARM VOG
## [925] Volcanic Ash VOLCANIC ASH
## [927] Volcanic Ash Plume VOLCANIC ASHFALL
## [929] VOLCANIC ERUPTION WAKE LOW WIND
## [931] WALL CLOUD WALL CLOUD/FUNNEL CLOUD
## [933] WARM DRY CONDITIONS WARM WEATHER
## [935] WATER SPOUT WATERSPOUT
## [937] WATERSPOUT- WATERSPOUT-TORNADO
## [939] WATERSPOUT FUNNEL CLOUD WATERSPOUT TORNADO
## [941] WATERSPOUT/ WATERSPOUT/ TORNADO
## [943] WATERSPOUT/TORNADO WATERSPOUTS
## [945] WAYTERSPOUT wet micoburst
## [947] WET MICROBURST Wet Month
## [949] WET SNOW WET WEATHER
## [951] Wet Year Whirlwind
## [953] WHIRLWIND WILD FIRES
## [955] WILD/FOREST FIRE WILD/FOREST FIRES
## [957] WILDFIRE WILDFIRES
## [959] Wind WIND
## [961] WIND ADVISORY WIND AND WAVE
## [963] WIND CHILL WIND CHILL/HIGH WIND
## [965] Wind Damage WIND DAMAGE
## [967] WIND GUSTS WIND STORM
## [969] WIND/HAIL WINDS
## [971] WINTER MIX WINTER STORM
## [973] WINTER STORM HIGH WINDS WINTER STORM/HIGH WIND
## [975] WINTER STORM/HIGH WINDS WINTER STORMS
## [977] Winter Weather WINTER WEATHER
## [979] WINTER WEATHER MIX WINTER WEATHER/MIX
## [981] WINTERY MIX Wintry mix
## [983] Wintry Mix WINTRY MIX
## [985] WND
## 985 Levels: HIGH SURF ADVISORY COASTAL FLOOD ... WND# close(conn)