Download climate normals from Environment and Climate Change Canada

Downloads climate normals from Environment and Climate Change Canada (ECCC) for one or more stations (defined by climate_ids). For details and units, see the glossary_normals, variables_normals_old, and variables_normals_new included data sets and/or the glossary_normals vignette: vignette("glossary_normals", package = "weathercan").

Usage

normals_dl(climate_ids, normals_years = "current", format = TRUE)

Arguments

climate_ids: Character. A vector containing the Climate ID(s) of the station(s) you wish to download data from. See the stations() data frame or the stations_search() function to find Climate IDs.
normals_years: Character. The year range for which you want climate normals. Default current (i.e. 1991-2020). One of current, 1991-2020, 1981-2010, or 1971-2000. current returns only stations from the most recent complete normals year range (i.e. 1991-2020).
format: Logical. If TRUE (default) formats measurements to numeric and date accordingly. Unlike weather_dl(), normals_dl() will always format column headings as normals data from ECCC cannot be directly made into a data frame without doing so.

Value

For new climate normals, a tibble of normals. For older climate normals, a tibble with nested normals and first/last frost data.

Details

The format and method of downloading climate normals from ECCC varies by year span.

Regardless of year, each normals measurement column has a corresponding _code column which reflects the data quality of that measurement (see the 1991-2020, 1981-2010, or 1971-2000 for more details) ECCC calculation documents.

Newer normals (1991-2020)

Newer normals from ECCC are provided in one bulk downloaded which weathercan will fetch and store in a local cache directory (cache_dir()). Then normals_dl() will read, filter, format, and return the climate normals in a data frame easier to work with in R than the original data.

These normals are also provided in a single table, so both 'normals' and 'frost' data are combined in one.

Newer climate normals are downloaded from the url stored in option weathercan.urls.normals_1991_2020. To change this location use: options(weathercan.urls.normals_1991_2020 = "your_new_url").

Older normals (1981-2010 and earlier)

Older normals from ECCC are provided by individual file downloads which weathercan will fetch, format and return as requested (no local on-disk cache storage).

These older normals also include two separate data types: averages by month for a variety of measurements as well as data relating to the frost-free period. Because these two data sources are quite different, we return them as nested data so the user can extract them as they wish. See examples for how to use the unnest() function from the tidyr package to extract the two different datasets.

The data also returns a column called meets_wmo this reflects whether or not the climate normals for this station met the WMO standards for temperature and precipitation (i.e. both have code >= A).

Older climate normals are downloaded from the url stored in option weathercan.urls.normals. To change this location use: options(weathercan.urls.normals = "your_new_url").

@inheritSection weather_dl Verbosity

Examples

if (FALSE) { # check_eccc()

# Find the climate_id
stations_search("Brandon A", normals_years = "current")

# Download climate normals 1991-2020 ("current" normals)
n <- normals_dl(climate_ids = "5010480")
n

# Download multiple climate Ids - But only one location!
# - 1990-2010 normals use composite stations
stations_search("Winnipeg", normals_years = "current")
n <- normals_dl(climate_ids = c("502S001", "5023227", "5023222"))
unique(dplyr::select(n, "location_name", "composite_stations"))

# Download multiple climate Ids
n <- normals_dl(climate_ids = c("5010480", "5023222"))
unique(dplyr::select(n, "location_name", "composite_stations"))

# Download climate normals 1981-2010
# - Note: Very different data format from current normals!
n <- normals_dl(climate_ids = "5010480", normals_year = "1981-2010")

# Pull out last frost data *with* station information
library(tidyr)
f <- unnest(n, frost)
f

# Pull out normals *with* station information
nm <- unnest(n, normals)
nm

# Download climate normals 1971-2000
n <- normals_dl(climate_ids = "5010480", normals_years = "1971-2000")
n

# Note that some do not have last frost dates
n$frost

# Download multiple stations for 1981-2010,
n <- normals_dl(
  climate_ids = c("301C3D4", "301FFNJ", "301N49A"),
  normals_years = "1981-2010"
)
unnest(n, frost)

# Note, putting both normals and frost data into the same data set can be
# done, but makes for a very unweildly dataset (there is lots of repetition).
nm <- unnest(n, normals) |>
  unnest(frost)
}