Clima

## Teoría La información del clima se obtiene del ASOS (Automated Surface Observing System) ubicados en los aeropuertos de todas las ciudades del mundo.

Instalar paquetes y llamar librerías

#install.packages("plotly")
library(riem)
library(ggplot2)
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library(plotly)
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library(tidyverse)
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Paso 1. Buscar la red del país (México) y copiar CODE

view(riem_networks())
# MX__ASOS

Paso 2. Buscar la estación o ciudad (Monterrey) y copiar ID

view(riem_stations("MX__ASOS"))
# MMMY

Paso 3. Obtener los datos del clima

clima_mty <- riem_measures("MMMY")

Ejercicio 1. Obtener datos del clima de Monterrey de Febrero 2024

clima_mty_feb <- subset(clima_mty, valid >= as.POSIXct("2024-02-01 00:00") & valid <= as.POSIXct("2024-02-29 23:59"))

Ejercicio 2. Graficar la Humedad Relativa en Monterrey durante Febrero 2024

plot(clima_mty_feb$valid,clima_mty_feb$relh,type="l",main="Humedad Relativa en Monterrey durante Febrero 2024", xlab="Fecha", ylab="Humedad Relativa (%)")

Promediar Humedad Relativa diaria

clima_mty_feb$fecha <- as.Date(clima_mty_feb$valid)
str(clima_mty_feb)
## tibble [713 × 33] (S3: tbl_df/tbl/data.frame)
##  $ station          : chr [1:713] "MMMY" "MMMY" "MMMY" "MMMY" ...
##  $ valid            : POSIXct[1:713], format: "2024-02-01 06:40:00" "2024-02-01 07:04:00" ...
##  $ lon              : num [1:713] -100 -100 -100 -100 -100 ...
##  $ lat              : num [1:713] 25.8 25.8 25.8 25.8 25.8 ...
##  $ tmpf             : num [1:713] 57.2 55.4 57.2 59 64.4 66.2 69.8 73.4 75.2 77 ...
##  $ dwpf             : num [1:713] 55.4 55.4 55.4 57.2 57.2 57.2 57.2 57.2 57.2 57.2 ...
##  $ relh             : num [1:713] 93.7 100 93.7 93.7 77.5 ...
##  $ drct             : num [1:713] 110 0 0 210 0 30 120 110 150 130 ...
##  $ sknt             : num [1:713] 3 0 0 3 0 4 4 8 5 8 ...
##  $ p01i             : num [1:713] 0 0 0 0 0 0 0 0 0 0 ...
##  $ alti             : num [1:713] 30.1 30.1 30.1 30.1 30.1 ...
##  $ mslp             : num [1:713] NA NA NA NA NA NA NA NA NA NA ...
##  $ vsby             : num [1:713] 8 2 1.5 2 4 4 5 5 5 7 ...
##  $ gust             : num [1:713] NA NA NA NA NA NA NA NA NA NA ...
##  $ skyc1            : chr [1:713] "BKN" "BKN" "BKN" "BKN" ...
##  $ skyc2            : chr [1:713] NA NA "OVC" "OVC" ...
##  $ skyc3            : chr [1:713] NA NA NA NA ...
##  $ skyc4            : chr [1:713] NA NA NA NA ...
##  $ skyl1            : num [1:713] 2000 500 500 500 7000 3500 NA NA NA NA ...
##  $ skyl2            : num [1:713] NA NA 1000 1000 NA NA NA NA NA NA ...
##  $ skyl3            : num [1:713] NA NA NA NA NA NA NA NA NA NA ...
##  $ skyl4            : num [1:713] NA NA NA NA NA NA NA NA NA NA ...
##  $ wxcodes          : chr [1:713] NA "BR" "BR" "BR" ...
##  $ ice_accretion_1hr: logi [1:713] NA NA NA NA NA NA ...
##  $ ice_accretion_3hr: logi [1:713] NA NA NA NA NA NA ...
##  $ ice_accretion_6hr: logi [1:713] NA NA NA NA NA NA ...
##  $ peak_wind_gust   : logi [1:713] NA NA NA NA NA NA ...
##  $ peak_wind_drct   : logi [1:713] NA NA NA NA NA NA ...
##  $ peak_wind_time   : logi [1:713] NA NA NA NA NA NA ...
##  $ feel             : num [1:713] 57.2 55.4 57.2 59 64.4 66.2 69.8 73.4 75.2 77 ...
##  $ metar            : chr [1:713] "MMMY 011240Z 11003KT 8SM BKN020 14/13 A3007 RMK 8/500" "MMMY 011304Z 00000KT 2SM BR BKN005 13/13 A3007 RMK 8/500" "MMMY 011340Z 00000KT 1 1/2SM BR BKN005 OVC010 14/13 A3007 RMK 8/5//" "MMMY 011445Z 21003KT 2SM BR BKN005 OVC010 15/14 A3008 RMK SLP/// 5//// 9// 8/5// BINOVC" ...
##  $ snowdepth        : logi [1:713] NA NA NA NA NA NA ...
##  $ fecha            : Date[1:713], format: "2024-02-01" "2024-02-01" ...
cmdf <- aggregate(clima_mty_feb, by=list(date = clima_mty_feb$fecha), FUN=mean)
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Graficar Humedad relativa promedio por día

plot(cmdf$fecha,cmdf$relh,type="l",main="Promedio Diario de Humedad Relativa en Monterrey durante Febrero 2024", xlab="Fecha", ylab="Humedad Relativa (%)")

Ejercicio 3. Graficar la temperatura (°C) promedio diaria durante febrero 2024 en ____ 

view(riem_stations("MX__ASOS"))
# MMPB
clima_pue <- riem_measures("MMPB")
clima_pue_feb <- subset(clima_pue, valid >= as.POSIXct("2024-02-01 00:00") & valid <= as.POSIXct("2024-02-29 23:59"))
clima_pue_feb$tmpf <- (clima_pue_feb$tmpf - 32) * 5/9
plot(clima_pue_feb$valid,clima_pue_feb$relh,type="l",main="Humedad Relativa en Puebla durante Febrero 2024 °C", xlab="Fecha", ylab="Humedad Relativa (%)")

Conclusión

En este código accedimos a datos climáticos de las estaciones de los estados de Monterrey y Puebla, haciendo uso de riem, la cual hace que sea posible acceder a los datos del Sistema Automatizado de Observación Superficial (ASOS). También se calcularon los promedios diarios de la humedad relativa y la temperatura en ambas ciudades, todo esto siendo útil para ver en qué estaciones ha habido mas humedad y en qué días lo que nos ayudará a poder predecir de una mejor manera el clima en esas áreas en el futuro.

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