“ASOS”

Informacion del Automated Surface Observing System (ASOS)

Instalar paquetes y llamar librerias

# install.packages("riem")
library(riem)
# install.packages("tidyverse")
library(tidyverse)

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## ✔ 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)

## 
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# 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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Buscar la red (pais) - Ejemplo Mexico y copiar CODE

view(riem_networks())

Buscar la estacion (ciudad) - Ejemplo: Monterrey, y copiar ID

view(riem_stations("MX__ASOS"))

Obtener informacion

monterrey <- riem_measures("MMMY")

Entender la informacion

str(monterrey)

## tibble [77,961 × 32] (S3: tbl_df/tbl/data.frame)
##  $ station          : chr [1:77961] "MMMY" "MMMY" "MMMY" "MMMY" ...
##  $ valid            : POSIXct[1:77961], format: "2014-01-01 00:16:00" "2014-01-01 00:49:00" ...
##  $ lon              : num [1:77961] -100 -100 -100 -100 -100 ...
##  $ lat              : num [1:77961] 25.8 25.8 25.8 25.8 25.8 ...
##  $ tmpf             : num [1:77961] 48.2 48.2 48.2 46.4 46.4 46.4 46.4 46.4 46.4 46.4 ...
##  $ dwpf             : num [1:77961] 46.4 46.4 46.4 46.4 46.4 44.6 44.6 44.6 44.6 44.6 ...
##  $ relh             : num [1:77961] 93.5 93.5 93.5 100 100 ...
##  $ drct             : num [1:77961] 0 120 120 120 110 100 110 130 60 0 ...
##  $ sknt             : num [1:77961] 0 3 5 6 5 5 4 3 3 0 ...
##  $ p01i             : num [1:77961] 0 0 0 0 0 0 0 0 0 0 ...
##  $ alti             : num [1:77961] 30.3 30.3 30.3 30.3 30.3 ...
##  $ mslp             : num [1:77961] NA NA NA NA NA ...
##  $ vsby             : num [1:77961] 4 3 1 0.25 0.12 0.12 0.06 0.06 0.06 0.12 ...
##  $ gust             : num [1:77961] NA NA NA NA NA NA NA NA NA NA ...
##  $ skyc1            : chr [1:77961] "SCT" "SCT" "SCT" "VV " ...
##  $ skyc2            : chr [1:77961] "BKN" "BKN" "BKN" NA ...
##  $ skyc3            : chr [1:77961] "OVC" "OVC" "OVC" NA ...
##  $ skyc4            : chr [1:77961] NA NA NA NA ...
##  $ skyl1            : num [1:77961] 700 300 200 200 100 100 100 100 100 100 ...
##  $ skyl2            : num [1:77961] 1200 400 300 NA NA NA NA NA NA NA ...
##  $ skyl3            : num [1:77961] 4000 900 500 NA NA NA NA NA NA NA ...
##  $ skyl4            : num [1:77961] NA NA NA NA NA NA NA NA NA NA ...
##  $ wxcodes          : chr [1:77961] NA "BR" "BR" "FG" ...
##  $ ice_accretion_1hr: logi [1:77961] NA NA NA NA NA NA ...
##  $ ice_accretion_3hr: logi [1:77961] NA NA NA NA NA NA ...
##  $ ice_accretion_6hr: logi [1:77961] NA NA NA NA NA NA ...
##  $ peak_wind_gust   : logi [1:77961] NA NA NA NA NA NA ...
##  $ peak_wind_drct   : logi [1:77961] NA NA NA NA NA NA ...
##  $ peak_wind_time   : logi [1:77961] NA NA NA NA NA NA ...
##  $ feel             : num [1:77961] 48.2 47.2 45.6 42.9 43.5 ...
##  $ metar            : chr [1:77961] "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 [1:77961] NA NA NA NA NA NA ...

summary(monterrey)

##    station              valid                             lon        
##  Length:77961       Min.   :2014-01-01 00:16:00.00   Min.   :-100.1  
##  Class :character   1st Qu.:2016-03-11 07:40:00.00   1st Qu.:-100.1  
##  Mode  :character   Median :2018-05-06 17:40:00.00   Median :-100.1  
##                     Mean   :2018-05-11 11:21:04.49   Mean   :-100.1  
##                     3rd Qu.:2020-07-03 16:41:00.00   3rd Qu.:-100.1  
##                     Max.   :2022-09-19 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   : 65.06  
##  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   :66962   
##       vsby             gust           skyc1              skyc2          
##  Min.   : 0.000   Min.   : 13.00   Length:77961       Length:77961      
##  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   :75505                                         
##     skyc3              skyc4               skyl1           skyl2      
##  Length:77961       Length:77961       Min.   :    0   Min.   :    0  
##  Class :character   Class :character   1st Qu.: 1500   1st Qu.: 2000  
##  Mode  :character   Mode  :character   Median : 3000   Median : 6000  
##                                        Mean   : 5388   Mean   : 8015  
##                                        3rd Qu.: 7000   3rd Qu.:10000  
##                                        Max.   :37000   Max.   :30000  
##                                        NA's   :23051   NA's   :51698  
##      skyl3           skyl4         wxcodes          ice_accretion_1hr
##  Min.   :  400   Min.   : 3000   Length:77961       Mode:logical     
##  1st Qu.: 8000   1st Qu.:20000   Class :character   NA's:77961       
##  Median :16000   Median :20000   Mode  :character                    
##  Mean   :14779   Mean   :20656                                       
##  3rd Qu.:20000   3rd Qu.:25000                                       
##  Max.   :30000   Max.   :25000                                       
##  NA's   :73228   NA's   :77766                                       
##  ice_accretion_3hr ice_accretion_6hr peak_wind_gust peak_wind_drct
##  Mode:logical      Mode:logical      Mode:logical   Mode:logical  
##  NA's:77961        NA's:77961        NA's:77961     NA's:77961    
##                                                                   
##                                                                   
##                                                                   
##                                                                   
##                                                                   
##  peak_wind_time      feel           metar           snowdepth     
##  Mode:logical   Min.   :  9.11   Length:77961       Mode:logical  
##  NA's:77961     1st Qu.: 64.40   Class :character   NA's:77961    
##                 Median : 73.40   Mode  :character                 
##                 Mean   : 73.16                                    
##                 3rd Qu.: 83.29                                    
##                 Max.   :131.06                                    
##                 NA's   :1744

Filtar la informacion- Ejemplos 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 informacion por dia

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: 3,151 × 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 3,141 more rows, and 6 more variables: gust <dbl>, skyl1 <dbl>,
## #   skyl2 <dbl>, skyl3 <dbl>, skyl4 <dbl>, feel <dbl>

Agregar columna de grados centrigrados

centigrados <- promedio
centigrados$tmpc<-(centigrados$tmpf-32)/1.8
str(centigrados)

## tibble [3,151 × 19] (S3: tbl_df/tbl/data.frame)
##  $ date : Date[1:3151], format: "2014-01-01" "2014-01-02" ...
##  $ lon  : num [1:3151] -100 -100 -100 -100 -100 ...
##  $ lat  : num [1:3151] 25.8 25.8 25.8 25.8 25.8 ...
##  $ tmpf : num [1:3151] 50.7 53.9 45.5 44.8 37.6 ...
##  $ dwpf : num [1:3151] 47.5 47.4 34.4 36 36.3 ...
##  $ relh : num [1:3151] 90.2 81.3 69 71.7 95.3 ...
##  $ drct : num [1:3151] 90.3 238.3 97.2 78.9 82.6 ...
##  $ sknt : num [1:3151] 2.42 8.13 4.16 2.22 2.96 ...
##  $ p01i : num [1:3151] 0 0 0 0 0 0 0 0 0 0 ...
##  $ alti : num [1:3151] 30.2 30.2 30.4 30.1 30.2 ...
##  $ mslp : num [1:3151] 1023 1024 1030 1022 1026 ...
##  $ vsby : num [1:3151] 2.29 8.48 15 15 1.78 ...
##  $ gust : num [1:3151] NaN 27.1 NaN NaN NaN ...
##  $ skyl1: num [1:3151] 1527 7150 12000 1700 348 ...
##  $ skyl2: num [1:3151] 8400 10812 NaN NaN 580 ...
##  $ skyl3: num [1:3151] 9080 20000 NaN NaN NaN ...
##  $ skyl4: num [1:3151] NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ...
##  $ feel : num [1:3151] 49.8 53.6 44.1 43.4 34.9 ...
##  $ tmpc : num [1:3151] 10.36 12.17 7.52 7.11 3.09 ...

centigrados$feelc <-(centigrados$feel-32)/1.8
str(centigrados)

## tibble [3,151 × 20] (S3: tbl_df/tbl/data.frame)
##  $ date : Date[1:3151], format: "2014-01-01" "2014-01-02" ...
##  $ lon  : num [1:3151] -100 -100 -100 -100 -100 ...
##  $ lat  : num [1:3151] 25.8 25.8 25.8 25.8 25.8 ...
##  $ tmpf : num [1:3151] 50.7 53.9 45.5 44.8 37.6 ...
##  $ dwpf : num [1:3151] 47.5 47.4 34.4 36 36.3 ...
##  $ relh : num [1:3151] 90.2 81.3 69 71.7 95.3 ...
##  $ drct : num [1:3151] 90.3 238.3 97.2 78.9 82.6 ...
##  $ sknt : num [1:3151] 2.42 8.13 4.16 2.22 2.96 ...
##  $ p01i : num [1:3151] 0 0 0 0 0 0 0 0 0 0 ...
##  $ alti : num [1:3151] 30.2 30.2 30.4 30.1 30.2 ...
##  $ mslp : num [1:3151] 1023 1024 1030 1022 1026 ...
##  $ vsby : num [1:3151] 2.29 8.48 15 15 1.78 ...
##  $ gust : num [1:3151] NaN 27.1 NaN NaN NaN ...
##  $ skyl1: num [1:3151] 1527 7150 12000 1700 348 ...
##  $ skyl2: num [1:3151] 8400 10812 NaN NaN 580 ...
##  $ skyl3: num [1:3151] 9080 20000 NaN NaN NaN ...
##  $ skyl4: num [1:3151] NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ...
##  $ feel : num [1:3151] 49.8 53.6 44.1 43.4 34.9 ...
##  $ tmpc : num [1:3151] 10.36 12.17 7.52 7.11 3.09 ...
##  $ feelc: num [1:3151] 9.87 12.01 6.71 6.36 1.6 ...

Filtrar informacion - Ejemplo 2022

este_ano <- centigrados[centigrados$date >= "2022-01-01" & centigrados$date <= "2022-09-07", ]

Graficar

plot(este_ano$date, este_ano$tmpc, type= "l", main= "Temperatura Promedio en Monterrey durante 2022", xlab= "Fecha", ylab = "c")

Conclusiones

El ASOS es un programa en conjunto entre el Servicio Meteorológico Nacional (NWS), la Administración Federal de Aviación (FAA) y el Departamento de Defensa (DOD). El sistema ASOS sirve como la principal red de observación meteorológica de superficie del país.

En este ejercicio, lo utilizamos para observar la temperatura de Monterrey. Primero buscamos el código de monterrey “MMMY”, indicando las temperaturas desde el día 2022-09-01 hasta el 2022-09-07. Al obtenerlos observamos la humedad relativa durante Septiembre de este año. Continuando, cambiamos los grados a centigrados ya que los datos los muestra en fahrenheit. Teniendo como resultado, una gráfica que nos muestra la temperatura promedio de Monterrey. Se observa claramente como en los meses de Julio y Agosto tuvo su máxima aproximadamente 30 grados centígrados.