Downloading Climate Normals
Climate Normals and Averages describe the average climate conditions
specific to a particular location. These can be downloaded from
Environment and Climate Change Canada using the
normals_dl() function.
First we’ll load the weathercan package for downloading
the data and the tidyr package for unnesting the data (see
below).
To download climate normals, we’ll first find the stations we’re
interested in using the stations_search() function. We’ll
use the normals_years = "current" argument to filter to
only stations with available climate normals for the
1981-2010 year range.
stations_search("Winnipeg", normals_years = "current")## # A tibble: 1 × 13
## prov station_name station_id climate_id WMO_id TC_id lat lon elev tz normals
## <chr> <chr> <dbl> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <chr> <lgl>
## 1 MB WINNIPEG RICHA… 3698 5023222 71852 YWG 49.9 -97.2 239. Etc/… TRUELet’s look at the climate normals from this station in Winnipeg, MB.
Note that unlike the weather_dl() function, the
normals_dl() function requires climate_id not
station_id. By default the normals are downloaded for the
years “1981-2010” (currently 1981-2010 and 1971-2000 are the only year
ranges available)
n <- normals_dl(climate_ids = "5023222")
n## # A tibble: 1 × 7
## prov station_name climate_id normals_years meets_wmo normals frost
## <chr> <chr> <chr> <chr> <lgl> <list> <list>
## 1 MB WINNIPEG RICHARDSON INT'L A 5023222 1981-2010 TRUE <tibble> <tibble>Because there are two different types of climate normals (weather measurements and first/last frost dates), the data are nested as two different datasets. We can see that the Airport (Richardson Int’l) has 197 average weather measurements/codes as well as first/last frost dates.
We can also see that this station has data quality sufficient to meet the WMO standards for temperature and precipitation (i.e. both these measurements have code >= A). See the ECCC calculations document for more details.
To extract either data set we can use the unnest()
function from the tidyr package.
Note that this extracts the measurements for all three stations (in
the case of the normals data frame), but not all
measurements are available for each station
normals## # A tibble: 13 × 203
## prov station_name climate_id normals_years meets_wmo period temp_daily_average
## <chr> <chr> <chr> <chr> <lgl> <fct> <dbl>
## 1 MB WINNIPEG RICHARDSON… 5023222 1981-2010 TRUE Jan -16.4
## 2 MB WINNIPEG RICHARDSON… 5023222 1981-2010 TRUE Feb -13.2
## 3 MB WINNIPEG RICHARDSON… 5023222 1981-2010 TRUE Mar -5.8
## 4 MB WINNIPEG RICHARDSON… 5023222 1981-2010 TRUE Apr 4.4
## 5 MB WINNIPEG RICHARDSON… 5023222 1981-2010 TRUE May 11.6
## 6 MB WINNIPEG RICHARDSON… 5023222 1981-2010 TRUE Jun 17
## 7 MB WINNIPEG RICHARDSON… 5023222 1981-2010 TRUE Jul 19.7
## 8 MB WINNIPEG RICHARDSON… 5023222 1981-2010 TRUE Aug 18.8
## 9 MB WINNIPEG RICHARDSON… 5023222 1981-2010 TRUE Sep 12.7
## 10 MB WINNIPEG RICHARDSON… 5023222 1981-2010 TRUE Oct 5
## 11 MB WINNIPEG RICHARDSON… 5023222 1981-2010 TRUE Nov -4.9
## 12 MB WINNIPEG RICHARDSON… 5023222 1981-2010 TRUE Dec -13.2
## 13 MB WINNIPEG RICHARDSON… 5023222 1981-2010 TRUE Year 3To visualize missing data we can use the gg_miss_var()
function from the naniar package.
select(normals, -contains("_code")) %>% # Remove '_code' columns
gg_miss_var(facet = station_name)
suppressWarnings({select(normals, -contains("_code")) %>% # Remove '_code' columns
gg_miss_var(facet = station_name)})
plot of chunk unnamed-chunk-7
Let’s take a look at the frost data.
## Rows: 7
## Columns: 13
## $ prov <chr> "MB", "MB", "MB", "MB", "MB", "MB", "MB"
## $ station_name <chr> "WINNIPEG RICHARDSON INT'L A", "WINNIPEG R…
## $ climate_id <chr> "5023222", "5023222", "5023222", "5023222"…
## $ normals_years <chr> "1981-2010", "1981-2010", "1981-2010", "19…
## $ meets_wmo <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE
## $ frost_code <chr> "A", "A", "A", "A", "A", "A", "A"
## $ date_first_fall_frost <dbl> 265, 265, 265, 265, 265, 265, 265
## $ date_last_spring_frost <dbl> 143, 143, 143, 143, 143, 143, 143
## $ length_frost_free <dbl> 121, 121, 121, 121, 121, 121, 121
## $ prob <chr> "10%", "25%", "33%", "50%", "66%", "75%", …
## $ prob_last_spring_temp_below_0_on_date <dbl> 158, 152, 148, 144, 140, 137, 129
## $ prob_first_fall_temp_below_0_on_date <dbl> 255, 259, 261, 265, 268, 270, 276
## $ prob_length_frost_free <dbl> 96, 109, 114, 119, 126, 129, 141Finding stations with specific measurements
The include data frame, normals_measurements contains a
list of stations with their corresponding measurements. Be aware that
this data might be out of date!
normals_measurements## # A tibble: 307,891 × 5
## prov station_name climate_id normals measurement
## <chr> <chr> <chr> <chr> <chr>
## 1 AB HORBURG 301C3D4 1981-2010 temp_daily_average
## 2 AB HORBURG 301C3D4 1981-2010 temp_daily_average_code
## 3 AB HORBURG 301C3D4 1981-2010 temp_sd
## 4 AB HORBURG 301C3D4 1981-2010 temp_sd_code
## 5 AB HORBURG 301C3D4 1981-2010 temp_daily_max
## 6 AB HORBURG 301C3D4 1981-2010 temp_daily_max_code
## 7 AB HORBURG 301C3D4 1981-2010 temp_daily_min
## 8 AB HORBURG 301C3D4 1981-2010 temp_daily_min_code
## 9 AB HORBURG 301C3D4 1981-2010 temp_extreme_max
## 10 AB HORBURG 301C3D4 1981-2010 temp_extreme_max_code
## # ℹ 307,881 more rowsFor example, if you wanted all climate_ids for stations
that have data on soil temperature for 1981-2010 normals:
normals_measurements %>%
filter(stringr::str_detect(measurement, "soil"),
normals == "1981-2010") %>%
pull(climate_id) %>%
unique()## [1] "3070560" "1100119" "112G8L1" "5021054" "5021848" "8102234" "8403600" "8501900"
## [9] "8502800" "8202800" "8205990" "2403500" "6073960" "6104025" "6105976" "7040440"
## [17] "7042388" "4012400" "4019035" "4028060" "4043900" "4075518"Understanding Climate Normals
The measurements contained in the climate normals are very specific. To better understand how they are calculated please explore the following resources:
- ECCC Climate Normals Calculations (1991-2020 | (1981-2010 | 1971-2000)
- ECCC Climate Normals Technical Documentation