Información del Automated Surface Observing Sytem (ASOS)

Instalar paquetes y llamar librerías

library(riem)
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   1.2.1      ✔ 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()
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
library(ggplot2)
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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##     layout

Paso 1.Buscar la red (país) Ejemplo: México, y copiar CODE

view(riem_networks())

Paso 2.Buscar la estación (ciudad) Ejemplo: Monterrey, y copiar ID

view(riem_stations("MX__ASOS"))

Escribir Id

monterrey<-riem_measures("MMMY")
str(monterrey)
## tibble [77,884 × 32] (S3: tbl_df/tbl/data.frame)
##  $ station          : chr [1:77884] "MMMY" "MMMY" "MMMY" "MMMY" ...
##  $ valid            : POSIXct[1:77884], format: "2014-01-01 00:16:00" "2014-01-01 00:49:00" ...
##  $ lon              : num [1:77884] -100 -100 -100 -100 -100 ...
##  $ lat              : num [1:77884] 25.8 25.8 25.8 25.8 25.8 ...
##  $ tmpf             : num [1:77884] 48.2 48.2 48.2 46.4 46.4 46.4 46.4 46.4 46.4 46.4 ...
##  $ dwpf             : num [1:77884] 46.4 46.4 46.4 46.4 46.4 44.6 44.6 44.6 44.6 44.6 ...
##  $ relh             : num [1:77884] 93.5 93.5 93.5 100 100 ...
##  $ drct             : num [1:77884] 0 120 120 120 110 100 110 130 60 0 ...
##  $ sknt             : num [1:77884] 0 3 5 6 5 5 4 3 3 0 ...
##  $ p01i             : num [1:77884] 0 0 0 0 0 0 0 0 0 0 ...
##  $ alti             : num [1:77884] 30.3 30.3 30.3 30.3 30.3 ...
##  $ mslp             : num [1:77884] NA NA NA NA NA ...
##  $ vsby             : num [1:77884] 4 3 1 0.25 0.12 0.12 0.06 0.06 0.06 0.12 ...
##  $ gust             : num [1:77884] NA NA NA NA NA NA NA NA NA NA ...
##  $ skyc1            : chr [1:77884] "SCT" "SCT" "SCT" "VV " ...
##  $ skyc2            : chr [1:77884] "BKN" "BKN" "BKN" NA ...
##  $ skyc3            : chr [1:77884] "OVC" "OVC" "OVC" NA ...
##  $ skyc4            : chr [1:77884] NA NA NA NA ...
##  $ skyl1            : num [1:77884] 700 300 200 200 100 100 100 100 100 100 ...
##  $ skyl2            : num [1:77884] 1200 400 300 NA NA NA NA NA NA NA ...
##  $ skyl3            : num [1:77884] 4000 900 500 NA NA NA NA NA NA NA ...
##  $ skyl4            : num [1:77884] NA NA NA NA NA NA NA NA NA NA ...
##  $ wxcodes          : chr [1:77884] NA "BR" "BR" "FG" ...
##  $ ice_accretion_1hr: logi [1:77884] NA NA NA NA NA NA ...
##  $ ice_accretion_3hr: logi [1:77884] NA NA NA NA NA NA ...
##  $ ice_accretion_6hr: logi [1:77884] NA NA NA NA NA NA ...
##  $ peak_wind_gust   : logi [1:77884] NA NA NA NA NA NA ...
##  $ peak_wind_drct   : logi [1:77884] NA NA NA NA NA NA ...
##  $ peak_wind_time   : logi [1:77884] NA NA NA NA NA NA ...
##  $ feel             : num [1:77884] 48.2 47.2 45.6 42.9 43.5 ...
##  $ metar            : chr [1:77884] "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 [1:77884] NA NA NA NA NA NA ...

Extraer información de este mes

este_mes<-subset(monterrey,valid>=as.POSIXct('2022-09-01 00:00')&valid<=as.POSIXct('2022-09-13 23:59'))

Graficas información

plot(este_mes$valid,este_mes$relh)