Clima Markdown
Paul Ramos Martinez - A01028886
2022-09-19
Obtener datos del clima por medio del sistema ASOS
Crear un modelo de precipitación
#Informacion del Automated Surface Observing system (ASOS)
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## C:\Users\paulr\AppData\Local\Temp\Rtmpods3ro\downloaded_packageslibrary(riem)
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## layout#Paso 1. Buscar la red (pais) Ejemplo: Mexico y copiar el CODE (MX__ASOS)
view(riem_networks())
#Paso 2. Buscar la estacion (ciudad) Ejemplo: Monterrey y copiar el ID (MMMY)
view(riem_stations("MX__ASOS"))
#Paso 3. Obtener datos del clima
monterrey <- riem_measures("MMMY")
str(monterrey) #Si en wxcodes sale RA eso significa que va a llover## tibble [77,908 × 32] (S3: tbl_df/tbl/data.frame)
## $ station : chr [1:77908] "MMMY" "MMMY" "MMMY" "MMMY" ...
## $ valid : POSIXct[1:77908], format: "2014-01-01 00:16:00" "2014-01-01 00:49:00" ...
## $ lon : num [1:77908] -100 -100 -100 -100 -100 ...
## $ lat : num [1:77908] 25.8 25.8 25.8 25.8 25.8 ...
## $ tmpf : num [1:77908] 48.2 48.2 48.2 46.4 46.4 46.4 46.4 46.4 46.4 46.4 ...
## $ dwpf : num [1:77908] 46.4 46.4 46.4 46.4 46.4 44.6 44.6 44.6 44.6 44.6 ...
## $ relh : num [1:77908] 93.5 93.5 93.5 100 100 ...
## $ drct : num [1:77908] 0 120 120 120 110 100 110 130 60 0 ...
## $ sknt : num [1:77908] 0 3 5 6 5 5 4 3 3 0 ...
## $ p01i : num [1:77908] 0 0 0 0 0 0 0 0 0 0 ...
## $ alti : num [1:77908] 30.3 30.3 30.3 30.3 30.3 ...
## $ mslp : num [1:77908] NA NA NA NA NA ...
## $ vsby : num [1:77908] 4 3 1 0.25 0.12 0.12 0.06 0.06 0.06 0.12 ...
## $ gust : num [1:77908] NA NA NA NA NA NA NA NA NA NA ...
## $ skyc1 : chr [1:77908] "SCT" "SCT" "SCT" "VV " ...
## $ skyc2 : chr [1:77908] "BKN" "BKN" "BKN" NA ...
## $ skyc3 : chr [1:77908] "OVC" "OVC" "OVC" NA ...
## $ skyc4 : chr [1:77908] NA NA NA NA ...
## $ skyl1 : num [1:77908] 700 300 200 200 100 100 100 100 100 100 ...
## $ skyl2 : num [1:77908] 1200 400 300 NA NA NA NA NA NA NA ...
## $ skyl3 : num [1:77908] 4000 900 500 NA NA NA NA NA NA NA ...
## $ skyl4 : num [1:77908] NA NA NA NA NA NA NA NA NA NA ...
## $ wxcodes : chr [1:77908] NA "BR" "BR" "FG" ...
## $ ice_accretion_1hr: logi [1:77908] NA NA NA NA NA NA ...
## $ ice_accretion_3hr: logi [1:77908] NA NA NA NA NA NA ...
## $ ice_accretion_6hr: logi [1:77908] NA NA NA NA NA NA ...
## $ peak_wind_gust : logi [1:77908] NA NA NA NA NA NA ...
## $ peak_wind_drct : logi [1:77908] NA NA NA NA NA NA ...
## $ peak_wind_time : logi [1:77908] NA NA NA NA NA NA ...
## $ feel : num [1:77908] 48.2 47.2 45.6 42.9 43.5 ...
## $ metar : chr [1:77908] "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 [1:77908] NA NA NA NA NA NA ...#Extraccion de informacion de est mes
este_mes <- subset(monterrey, valid >= as.POSIXct("2022-09-01 00:00") & valid <= as.POSIXct("2022-09-13 25:59")) #Subset, agarra unos datos de la base; PSIXct es un formato de dia con hora
#Graficar la informacion
plot(este_mes$valid, este_mes$relh)
Conclusión Esta técnica nos permite recopilar datos factuales sobre estadísticas de clima en tiempo real; lo que nos permite tener acceso a información valiosa para la toma de decisiones de una empresa, por ejemplo: como en el programa encontramos el patrón d lluvia de septiembre, podemos utilizar un promedio de los útlimos años para que en el 2023 podamos adelantarnos a los patrones clímaticos para poder estar siempre preparados a responder a las demandas de la compañía durante emergencias como las inundaciones.