Access Canadian Historical Climate Data from R. The Government of Canada's Historical Climate Data website provides access to hourly, daily, and monthly weather records for stations throughout Canada.
These are raw data that have undergone some quality control, but issues such as changes in station location are unmanged; the data for the original stationID stops at a certain point and a new stationID continues recording. For a more curated data set for climate change research at broad spatial and temporal scales see the Adjusted and Homogenized Canadian Climate Data (AHCCD).
canadaHCD is still under active development towards a 0.1 release. In the meantime, if you wish to use the package, please install it from this github repo, which is most easily achieved using Hadley Wickham's devtools package:
## install.packages("devtools")
devtools::install_github("gavinsimpson/canadaHCD")Say I'm interested in climate data for stations in Yellowknife, I can search for all known stationIDs with "Yellowknife" in their name using find_station()
library("canadaHCD")
find_station("Yellowknife")
#> # A tibble: 7 × 5
#> Name Province StationID LatitudeDD
#> <fctr> <fctr> <fctr> <dbl>
#> 1 YELLOWKNIFE A Northwest Territories 1706 62.46
#> 2 YELLOWKNIFE A Northwest Territories 51058 62.46
#> 3 YELLOWKNIFE OVERLAP Northwest Territories 53898 62.46
#> 4 YELLOWKNIFE-HENDERSON Northwest Territories 45467 62.45
#> 5 YELLOWKNIFE CON MINE Northwest Territories 8949 62.47
#> 6 YELLOWKNIFE CS Northwest Territories 27338 62.47
#> 7 YELLOWKNIFE HYDRO Northwest Territories 1707 62.67
#> LongitudeDD
#> <dbl>
#> 1 -114.44
#> 2 -114.44
#> 3 -114.44
#> 4 -114.38
#> 5 -114.33
#> 6 -114.45
#> 7 -114.25To download the monthly HCD from YELLOWKNIFE HYDRO I can use hcd_monthly(), providing it with the StationID for that particular weather station
yh <- hcd_monthly(1707)The data are returned as a tibble (a tbl_df), which shows a compact version of the data frame.
yh
#> # A tibble: 690 × 13
#> Station Date MaxTemp MinTemp MeanTemp ExtremeHigh ExtremeLow
#> <dbl> <S3: yearmon> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1707 Jan 1943 NA NA NA NA NA
#> 2 1707 Feb 1943 -16.2 -26.4 -21.3 1.1 -44.4
#> 3 1707 Mar 1943 -14.4 -29.4 -21.9 -3.3 -40.6
#> 4 1707 Apr 1943 1.2 -12.3 -5.6 12.2 -31.7
#> 5 1707 May 1943 9.3 -3.6 2.9 20.0 -11.7
#> 6 1707 Jun 1943 17.6 2.4 10.0 27.2 -1.7
#> 7 1707 Jul 1943 20.6 9.6 15.1 27.2 4.4
#> 8 1707 Aug 1943 18.9 7.2 13.1 27.2 1.7
#> 9 1707 Sep 1943 10.9 2.0 6.5 18.3 -6.1
#> 10 1707 Oct 1943 6.1 -1.9 2.1 17.2 -15.6
#> TotalRain TotalSnow TotalPrecip LastSnowGrnd
#> <dbl> <dbl> <dbl> <int>
#> 1 NA NA NA NA
#> 2 0.0 9.4 9.4 NA
#> 3 0.0 2.8 2.8 NA
#> 4 0.0 18.0 18.0 NA
#> 5 9.9 2.8 12.7 NA
#> 6 4.8 0.0 4.8 NA
#> 7 36.6 0.0 36.6 NA
#> 8 17.8 0.0 17.8 NA
#> 9 5.8 2.8 8.6 NA
#> 10 19.1 5.1 24.1 NA
#> # ... with 680 more rows, and 2 more variables: MaxGustDir <int>,
#> # MaxGustSpeed <chr>You should be able to work with these objects mostly as if they were data frames.
Allthough not yet exposed through any functions in the package, you can access a snapshot of the station metadata via the canadaHCD:::station_data data frame.
canadaHCD:::station_data
#> # A tibble: 12,565 × 19
#> Name Province ClimateID StationID WMOID
#> <fctr> <fctr> <fctr> <fctr> <fctr>
#> 1 ACTIVE PASS British Columbia 1010066 14 NA
#> 2 ALBERT HEAD British Columbia 1010235 15 NA
#> 3 BAMBERTON OCEAN CEMENT British Columbia 1010595 16 NA
#> 4 BEAR CREEK British Columbia 1010720 17 NA
#> 5 BEAVER LAKE British Columbia 1010774 18 NA
#> 6 BECHER BAY British Columbia 1010780 19 NA
#> 7 BRENTWOOD BAY 2 British Columbia 1010960 20 NA
#> 8 BRENTWOOD CLARKE ROAD British Columbia 1010961 21 NA
#> 9 BRENTWOOD W SAANICH RD British Columbia 1010965 22 NA
#> 10 BEAR CREEK British Columbia 1010PJR 9634 NA
#> TCID LatitudeDD LongitudeDD Latitude
#> <fctr> <dbl> <dbl> <int>
#> 1 NA 48.87 -123.28 485200000
#> 2 NA 48.40 -123.48 482400000
#> 3 NA 48.58 -123.52 483500000
#> 4 NA 48.50 -124.00 483000000
#> 5 NA 48.50 -123.35 483000000
#> 6 NA 48.33 -123.63 482000000
#> 7 NA 48.60 -123.47 483600000
#> 8 NA 48.57 -123.45 483400000
#> 9 NA 48.57 -123.43 483400000
#> 10 NA 48.50 -123.90 483000000
#> # ... with 12,555 more rows, and 10 more variables: Longitude <int>,
#> # Elevation <dbl>, FirstYear <int>, LastYear <int>, HourlyFirstYr <int>,
#> # HourlyLastYr <int>, DailyFirstYr <int>, DailyLastYr <int>,
#> # MonthlyFirstYr <int>, MonthlyLastYr <int>If we wanted to know which resolutions of data were available for the YELLOWKNIFE HYDRO station, we can extract certain columns from the station data object
id <- grep("YELLOWKNIFE HYDRO", canadaHCD:::station_data$Name)
vars <- c("HourlyFirstYr", "HourlyLastYr", "DailyFirstYr", "DailyLastYr", "MonthlyFirstYr",
"MonthlyLastYr")
canadaHCD:::station_data[id, vars]
#> # A tibble: 1 × 6
#> HourlyFirstYr HourlyLastYr DailyFirstYr DailyLastYr MonthlyFirstYr
#> <int> <int> <int> <int> <int>
#> 1 NA NA 1943 2000 1943
#> MonthlyLastYr
#> <int>
#> 1 2000The output shows that this station has no hourly data, but daily and monthly data sets exist.