Automated Surface Observing System (ASOS)
Ericka Martinez - A01177017
2022-09-20
ASOS Info
Instalación de paquetes y llamado de librerías
#install.packages("riem")
library(riem)
#install.packages("tidyverse")
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.3.6 ✔ purrr 0.3.4
## ✔ tibble 3.1.8 ✔ dplyr 1.0.10
## ✔ tidyr 1.2.0 ✔ stringr 1.4.1
## ✔ readr 2.1.2 ✔ forcats 0.5.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()#install.packages("lubridate")
library(lubridate)##
## Attaching package: 'lubridate'
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## The following objects are masked from 'package:base':
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## date, intersect, setdiff, union#install.packages("ggplot2")
library(ggplot2)
#install.packages("plotly")
library(plotly)##
## Attaching package: 'plotly'
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## The following object is masked from 'package:ggplot2':
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## last_plot
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## The following object is masked from 'package:stats':
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## filter
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## The following object is masked from 'package:graphics':
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## layoutBuscar la red (país) - Ejemplo: México, y copiar CODE
# Buscar la red (pais) - Ejemplo Mexico, y copiar CODE
view(riem_networks())Buscar la estación (ciudad) - Ejemplo: Monterrey, y copiar ID
view(riem_stations("MX__ASOS"))Se tomo el ID del mas actual que fue en archive_begin de 1987
Obtener Info
monterrey <- riem_measures("MMMY")Entender información
str(monterrey)## tibble [77,985 × 32] (S3: tbl_df/tbl/data.frame)
## $ station : chr [1:77985] "MMMY" "MMMY" "MMMY" "MMMY" ...
## $ valid : POSIXct[1:77985], format: "2014-01-01 00:16:00" "2014-01-01 00:49:00" ...
## $ lon : num [1:77985] -100 -100 -100 -100 -100 ...
## $ lat : num [1:77985] 25.8 25.8 25.8 25.8 25.8 ...
## $ tmpf : num [1:77985] 48.2 48.2 48.2 46.4 46.4 46.4 46.4 46.4 46.4 46.4 ...
## $ dwpf : num [1:77985] 46.4 46.4 46.4 46.4 46.4 44.6 44.6 44.6 44.6 44.6 ...
## $ relh : num [1:77985] 93.5 93.5 93.5 100 100 ...
## $ drct : num [1:77985] 0 120 120 120 110 100 110 130 60 0 ...
## $ sknt : num [1:77985] 0 3 5 6 5 5 4 3 3 0 ...
## $ p01i : num [1:77985] 0 0 0 0 0 0 0 0 0 0 ...
## $ alti : num [1:77985] 30.3 30.3 30.3 30.3 30.3 ...
## $ mslp : num [1:77985] NA NA NA NA NA ...
## $ vsby : num [1:77985] 4 3 1 0.25 0.12 0.12 0.06 0.06 0.06 0.12 ...
## $ gust : num [1:77985] NA NA NA NA NA NA NA NA NA NA ...
## $ skyc1 : chr [1:77985] "SCT" "SCT" "SCT" "VV " ...
## $ skyc2 : chr [1:77985] "BKN" "BKN" "BKN" NA ...
## $ skyc3 : chr [1:77985] "OVC" "OVC" "OVC" NA ...
## $ skyc4 : chr [1:77985] NA NA NA NA ...
## $ skyl1 : num [1:77985] 700 300 200 200 100 100 100 100 100 100 ...
## $ skyl2 : num [1:77985] 1200 400 300 NA NA NA NA NA NA NA ...
## $ skyl3 : num [1:77985] 4000 900 500 NA NA NA NA NA NA NA ...
## $ skyl4 : num [1:77985] NA NA NA NA NA NA NA NA NA NA ...
## $ wxcodes : chr [1:77985] NA "BR" "BR" "FG" ...
## $ ice_accretion_1hr: logi [1:77985] NA NA NA NA NA NA ...
## $ ice_accretion_3hr: logi [1:77985] NA NA NA NA NA NA ...
## $ ice_accretion_6hr: logi [1:77985] NA NA NA NA NA NA ...
## $ peak_wind_gust : logi [1:77985] NA NA NA NA NA NA ...
## $ peak_wind_drct : logi [1:77985] NA NA NA NA NA NA ...
## $ peak_wind_time : logi [1:77985] NA NA NA NA NA NA ...
## $ feel : num [1:77985] 48.2 47.2 45.6 42.9 43.5 ...
## $ metar : chr [1:77985] "MMMY 010016Z 00000KT 4SM SCT007 BKN012 OVC040 09/08 A3028 RMK 8/5// BR" "MMMY 010049Z 12003KT 3SM BR SCT003 BKN004 OVC009 09/08 A3028 RMK 8/5// -DZ OCNL" "MMMY 010116Z 12005KT 1SM BR SCT002 BKN003 OVC005 09/08 A3028 RMK 8/6// -DZ OCNL" "MMMY 010120Z 12006KT 1/4SM FG VV002 08/08 A3029 RMK 8//// BC FG MOV SE/NW" ...
## $ snowdepth : logi [1:77985] NA NA NA NA NA NA ...summary(monterrey)## station valid lon
## Length:77985 Min. :2014-01-01 00:16:00.00 Min. :-100.1
## Class :character 1st Qu.:2016-03-11 13:42:00.00 1st Qu.:-100.1
## Mode :character Median :2018-05-07 05:43:00.00 Median :-100.1
## Mean :2018-05-11 23:07:02.83 Mean :-100.1
## 3rd Qu.:2020-07-04 10:40:00.00 3rd Qu.:-100.1
## Max. :2022-09-20 23:40:00.00 Max. :-100.1
##
## lat tmpf dwpf relh
## Min. :25.78 Min. : 23.00 Min. :-5.80 Min. : 2.32
## 1st Qu.:25.78 1st Qu.: 64.40 1st Qu.:51.80 1st Qu.: 48.05
## Median :25.78 Median : 73.40 Median :62.60 Median : 69.14
## Mean :25.78 Mean : 72.49 Mean :57.98 Mean : 65.07
## 3rd Qu.:25.78 3rd Qu.: 80.60 3rd Qu.:68.00 3rd Qu.: 83.32
## Max. :25.78 Max. :111.20 Max. :86.00 Max. :163.20
## NA's :89 NA's :1686 NA's :1741
## drct sknt p01i alti mslp
## Min. : 0.0 Min. : 0.000 Min. :0 Min. : 0.04 Min. : 913.2
## 1st Qu.: 70.0 1st Qu.: 4.000 1st Qu.:0 1st Qu.:29.88 1st Qu.:1011.4
## Median :110.0 Median : 5.000 Median :0 Median :29.97 Median :1014.5
## Mean :130.7 Mean : 5.819 Mean :0 Mean :29.98 Mean :1015.3
## 3rd Qu.:160.0 3rd Qu.: 8.000 3rd Qu.:0 3rd Qu.:30.07 3rd Qu.:1018.4
## Max. :360.0 Max. :98.000 Max. :0 Max. :30.81 Max. :1103.4
## NA's :72 NA's :72 NA's :26 NA's :66986
## vsby gust skyc1 skyc2
## Min. : 0.000 Min. : 13.00 Length:77985 Length:77985
## 1st Qu.: 6.000 1st Qu.: 20.00 Class :character Class :character
## Median :10.000 Median : 24.00 Mode :character Mode :character
## Mean : 9.124 Mean : 24.65
## 3rd Qu.:12.000 3rd Qu.: 28.00
## Max. :40.000 Max. :210.00
## NA's :31 NA's :75529
## skyc3 skyc4 skyl1 skyl2
## Length:77985 Length:77985 Min. : 0 Min. : 0
## Class :character Class :character 1st Qu.: 1500 1st Qu.: 2000
## Mode :character Mode :character Median : 3000 Median : 6000
## Mean : 5387 Mean : 8016
## 3rd Qu.: 7000 3rd Qu.:10000
## Max. :37000 Max. :30000
## NA's :23057 NA's :51718
## skyl3 skyl4 wxcodes ice_accretion_1hr
## Min. : 400 Min. : 3000 Length:77985 Mode:logical
## 1st Qu.: 8000 1st Qu.:20000 Class :character NA's:77985
## Median :16000 Median :20000 Mode :character
## Mean :14779 Mean :20656
## 3rd Qu.:20000 3rd Qu.:25000
## Max. :30000 Max. :25000
## NA's :73252 NA's :77790
## ice_accretion_3hr ice_accretion_6hr peak_wind_gust peak_wind_drct
## Mode:logical Mode:logical Mode:logical Mode:logical
## NA's:77985 NA's:77985 NA's:77985 NA's:77985
##
##
##
##
##
## peak_wind_time feel metar snowdepth
## Mode:logical Min. : 9.11 Length:77985 Mode:logical
## NA's:77985 1st Qu.: 64.40 Class :character NA's:77985
## Median : 73.40 Mode :character
## Mean : 73.17
## 3rd Qu.: 83.29
## Max. :131.06
## NA's :1744Filtrar Información - Ejemplo: septiembre 2022
este_mes <- subset(monterrey, valid >= as.POSIXct("2022-09-01 00:00") & valid <= as.POSIXct("2022-09-07 23:59"))Graficar - Ejemplo: Humedad relativa durante septiembre 2022
plot(este_mes$valid,este_mes$relh)
Promediar información por día
promedio <- monterrey %>%
mutate(date= ymd_hms(valid), date= as.Date(date))%>%
group_by(date)%>%
summarize_if(is.numeric,~mean(.,na.rm=TRUE))
tibble(promedio)## # A tibble: 3,152 × 18
## date lon lat tmpf dwpf relh drct sknt p01i alti mslp vsby
## <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2014-01-01 -100. 25.8 50.7 47.5 90.2 90.3 2.42 0 30.2 1023. 2.29
## 2 2014-01-02 -100. 25.8 53.9 47.4 81.3 238. 8.13 0 30.2 1024. 8.48
## 3 2014-01-03 -100. 25.8 45.5 34.4 69.0 97.2 4.16 0 30.4 1030. 15
## 4 2014-01-04 -100. 25.8 44.8 36 71.7 78.9 2.22 0 30.1 1022. 15
## 5 2014-02-07 -100. 25.8 37.6 36.3 95.3 82.6 2.96 0 30.2 1026. 1.78
## 6 2014-02-08 -100. 25.8 44.8 41.5 90.0 100 2.74 0 30.2 1023. 1.81
## 7 2014-02-09 -100. 25.8 53.8 44.7 77.1 212. 5.38 0 30.1 1019. 6.46
## 8 2014-02-10 -100. 25.8 62.5 54.0 77.8 158. 7.68 0 29.9 1014. 7.27
## 9 2014-02-11 -100. 25.8 50.5 45.6 83.9 228. 7.58 0 30.0 1019. 6.81
## 10 2014-02-12 -100. 25.8 45.5 35.1 69.0 229. 8.85 0 30.2 1023. 9.56
## # … with 3,142 more rows, and 6 more variables: gust <dbl>, skyl1 <dbl>,
## # skyl2 <dbl>, skyl3 <dbl>, skyl4 <dbl>, feel <dbl>Agregar columnas de grados centigrados
centigrados<- promedio
centigrados$tmpc <-(centigrados$tmpf-32)/1.8
str(centigrados)## tibble [3,152 × 19] (S3: tbl_df/tbl/data.frame)
## $ date : Date[1:3152], format: "2014-01-01" "2014-01-02" ...
## $ lon : num [1:3152] -100 -100 -100 -100 -100 ...
## $ lat : num [1:3152] 25.8 25.8 25.8 25.8 25.8 ...
## $ tmpf : num [1:3152] 50.7 53.9 45.5 44.8 37.6 ...
## $ dwpf : num [1:3152] 47.5 47.4 34.4 36 36.3 ...
## $ relh : num [1:3152] 90.2 81.3 69 71.7 95.3 ...
## $ drct : num [1:3152] 90.3 238.3 97.2 78.9 82.6 ...
## $ sknt : num [1:3152] 2.42 8.13 4.16 2.22 2.96 ...
## $ p01i : num [1:3152] 0 0 0 0 0 0 0 0 0 0 ...
## $ alti : num [1:3152] 30.2 30.2 30.4 30.1 30.2 ...
## $ mslp : num [1:3152] 1023 1024 1030 1022 1026 ...
## $ vsby : num [1:3152] 2.29 8.48 15 15 1.78 ...
## $ gust : num [1:3152] NaN 27.1 NaN NaN NaN ...
## $ skyl1: num [1:3152] 1527 7150 12000 1700 348 ...
## $ skyl2: num [1:3152] 8400 10812 NaN NaN 580 ...
## $ skyl3: num [1:3152] 9080 20000 NaN NaN NaN ...
## $ skyl4: num [1:3152] NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ...
## $ feel : num [1:3152] 49.8 53.6 44.1 43.4 34.9 ...
## $ tmpc : num [1:3152] 10.36 12.17 7.52 7.11 3.09 ...centigrados$feelc <- (centigrados$feel-32)/1.8
str(centigrados)## tibble [3,152 × 20] (S3: tbl_df/tbl/data.frame)
## $ date : Date[1:3152], format: "2014-01-01" "2014-01-02" ...
## $ lon : num [1:3152] -100 -100 -100 -100 -100 ...
## $ lat : num [1:3152] 25.8 25.8 25.8 25.8 25.8 ...
## $ tmpf : num [1:3152] 50.7 53.9 45.5 44.8 37.6 ...
## $ dwpf : num [1:3152] 47.5 47.4 34.4 36 36.3 ...
## $ relh : num [1:3152] 90.2 81.3 69 71.7 95.3 ...
## $ drct : num [1:3152] 90.3 238.3 97.2 78.9 82.6 ...
## $ sknt : num [1:3152] 2.42 8.13 4.16 2.22 2.96 ...
## $ p01i : num [1:3152] 0 0 0 0 0 0 0 0 0 0 ...
## $ alti : num [1:3152] 30.2 30.2 30.4 30.1 30.2 ...
## $ mslp : num [1:3152] 1023 1024 1030 1022 1026 ...
## $ vsby : num [1:3152] 2.29 8.48 15 15 1.78 ...
## $ gust : num [1:3152] NaN 27.1 NaN NaN NaN ...
## $ skyl1: num [1:3152] 1527 7150 12000 1700 348 ...
## $ skyl2: num [1:3152] 8400 10812 NaN NaN 580 ...
## $ skyl3: num [1:3152] 9080 20000 NaN NaN NaN ...
## $ skyl4: num [1:3152] NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ...
## $ feel : num [1:3152] 49.8 53.6 44.1 43.4 34.9 ...
## $ tmpc : num [1:3152] 10.36 12.17 7.52 7.11 3.09 ...
## $ feelc: num [1:3152] 9.87 12.01 6.71 6.36 1.6 ...Filtrar información - Ejemplo: 2022
este_año<- centigrados[centigrados$date>= "2022-01-01"& centigrados$date<= "2022-09-07",]Graficar - Ejemplo: Temperatura promedio septiembre 2022
plot(este_año$date, este_año$tmpc, type = "l", main = "Temperatura promedio en Monterrey durante 2022", xlab= "Fecha", ylab = "Grados Celsius")
Conclusiones
R nos permite acceder a distintas fuentes de información de manera gratuita, en esta actividad nos permitió trabajar con información para poder ver la temperatura promedio que hay en alguna ciudad, nosotros decidimos investigar la de Monterrey.
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