Tools for working with Global Historical Climatology Network (GHCN) data.
devtools::install_github("rwright88/ghcn")Find a station based on a matching pattern:
library(ghcn)
stations <- ghcn_find_stations(pattern = "new york")
stations[1:6]#> # A tibble: 18 x 6
#> id latitude longitude elevation state name
#> <chr> <dbl> <dbl> <dbl> <chr> <chr>
#> 1 US1NYNY0074 40.8 -73.9 6.1 NY NEW YORK 8.8 N
#> 2 USC00215902 46.5 -95.4 428. MN NEW YORK MILLS
#> 3 USC00305798 40.6 -74.0 6.1 NY NEW YORK BENSONHURST
#> 4 USC00305799 40.9 -73.9 27.1 NY NEW YORK BOTANICAL GRD
#> 5 USC00305804 40.7 -73.9 3 NY NEW YORK LAUREL HILL
#> 6 USC00305806 40.8 -73.9 54.9 NY NEW YORK UNIV ST
#> 7 USC00305816 40.7 -74.0 3 NY NEW YORK WB CITY
#> 8 USC00308721 40.9 -72.9 7.9 NY UPTON COOP - NWSFO NEW Y~
#> 9 USR0000NGAN 42.1 -77.1 335. NY GANG MILLS NEW YORK
#> 10 USR0000NSAR 43.0 -73.7 114. NY SARA NEW YORK
#> 11 USR0000NSCH 43.8 -73.7 250. NY SCHROON LAKE NEW YORK
#> 12 USR0000NSHR 42.7 -75.5 335. NY SHERBURNE NEW YORK
#> 13 USR0000NSTO 41.5 -73.9 61 NY STONYKILL NEW YORK
#> 14 USW00014732 40.8 -73.9 3.4 NY NEW YORK LAGUARDIA AP
#> 15 USW00014786 40.6 -73.9 4.9 NY NEW YORK FLOYD BENNETT F~
#> 16 USW00093732 39.8 -72.7 25.9 NY NEW YORK SHOALS AFS
#> 17 USW00094728 40.8 -74.0 39.6 NY NEW YORK CNTRL PK TWR
#> 18 USW00094789 40.6 -73.8 3.4 NY NEW YORK JFK INTL AP
Read all daily data for one station, given the station ID:
dat <- ghcn_read(id = "USW00094728")
str(dat[1:20])#> Classes 'tbl_df', 'tbl' and 'data.frame': 15060 obs. of 20 variables:
#> $ id : chr "USW00094728" "USW00094728" "USW00094728" "USW00094728" ...
#> $ year : int 1869 1869 1869 1869 1869 1869 1869 1869 1869 1869 ...
#> $ month : int 1 1 1 1 2 2 2 2 3 3 ...
#> $ element: chr "TMAX" "TMIN" "PRCP" "SNOW" ...
#> $ value1 : int -17 -72 191 229 6 -39 0 0 -33 -156 ...
#> $ mflag1 : chr NA NA NA NA ...
#> $ qflag1 : chr NA NA NA NA ...
#> $ sflag1 : chr "Z" "Z" "Z" "Z" ...
#> $ value2 : int -28 -61 8 0 11 -56 0 0 22 -72 ...
#> $ mflag2 : chr NA NA NA NA ...
#> $ qflag2 : chr NA NA NA NA ...
#> $ sflag2 : chr "Z" "Z" "Z" "Z" ...
#> $ value3 : int 17 -28 0 0 22 17 0 0 61 -22 ...
#> $ mflag3 : chr NA NA "T" NA ...
#> $ qflag3 : chr NA NA NA NA ...
#> $ sflag3 : chr "Z" "Z" "Z" "Z" ...
#> $ value4 : int 28 11 46 0 33 -61 394 28 67 -72 ...
#> $ mflag4 : chr NA NA NA NA ...
#> $ qflag4 : chr NA NA NA NA ...
#> $ sflag4 : chr "Z" "Z" "Z" "Z" ...
Transform the data into an easier to use format, keeping only the five core statistics of daily precipitation, snow fall, snow depth, minimum temperature, and maximum temperature:
ghcn_clean(dat)#> # A tibble: 55,029 x 7
#> id date prcp snow snwd tmax tmin
#> <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 USW00094728 1869-01-01 19.1 229 NA -1.7 -7.2
#> 2 USW00094728 1869-01-02 0.8 0 NA -2.8 -6.1
#> 3 USW00094728 1869-01-03 0 0 NA 1.7 -2.8
#> 4 USW00094728 1869-01-04 4.6 0 NA 2.8 1.1
#> 5 USW00094728 1869-01-05 1.3 0 NA 6.1 2.8
#> 6 USW00094728 1869-01-06 0 0 NA 3.3 1.1
#> 7 USW00094728 1869-01-07 0 0 NA 8.9 1.7
#> 8 USW00094728 1869-01-08 0 0 NA 12.2 4.4
#> 9 USW00094728 1869-01-09 0 0 NA 8.9 3.3
#> 10 USW00094728 1869-01-10 0.3 0 NA 6.7 0.6
#> # ... with 55,019 more rows