This package provides a set of simple handling functions for importing, parsing, and summarising ERA5 netcdf4 files downloaded from the copernicus climate data store (CDS). It has been tested using single levels and pressure levels data, with files downloaded both manually and through API methods such as ecmwfr.

A simple workflow is outlined below.

1. Create an account on the Copernicus CDS

• Navigate to https://cds.climate.copernicus.eu/cdsapp#!/home and create an account.

2. Download ERA5 netcdf4 data using manual or API methods

• ERA5 data can be fetched manually from the CDS using the browser interface: https://cds.climate.copernicus.eu/cdsapp#!/search?type=dataset&text=ERA5.

3. Name files in a local store

• Give the files a suitable name that conforms to your chosen file naming convention. If you are working with individual field types (for example, 2m temperature), but are storing those files alongside other file of different field types, files of a common field type should be named to reflect this.

4. Install and load the ERA5handlers package

• This package and its dependencies can be fetched with the devtools package, using devtools::install_github("MRPHarris/ERA5handlers")

5. Import desired files into R

• The collate_era5() function imports and parses ERA5 netcdf4 files. The package ships with a small netcdf4 file containing 2m temperature data from 2000, used in line with the Copernicus license. The data includes the grid points lat = c(-80.00 -80.25 -80.50) and lon = c(-81.50 -81.25 -81.00 -80.75 -80.50), covering a small portion of the southern Ellsworth Mountains in Antarctica.

In this case, we use collate_era5() to read in the single file, and narrow the coordinates to a single grid point.

# Load package
library(ERA5handlers)
# Specify local data store
dat_store_era5 <- "D:/DATA/General data/ERA5/"
# Get filenames in store
era5_fnames <- list.files(dat_store_era5, full.names = TRUE)
# Get target era5 file
temp2m_2000_PH <- collate_era5(era5_fnames, string = 'temp2m_2000', coords = c(-80.25, -81.25))
head(temp2m_2000_PH)

#> # A tibble: 6 x 12
#>   value   lon   lat timestamp           name  coord       year month   day  hour
#>   <dbl> <dbl> <dbl> <dttm>              <fct> <fct>      <dbl> <dbl> <dbl> <dbl>
#> 1  259. -81.2 -80.2 2000-01-01 00:00:00 t2m   -81.25 -8~  2000     1     1     0
#> 2  258. -81.2 -80.2 2000-01-01 01:00:00 t2m   -81.25 -8~  2000     1     1     1
#> 3  258. -81.2 -80.2 2000-01-01 02:00:00 t2m   -81.25 -8~  2000     1     1     2
#> 4  257. -81.2 -80.2 2000-01-01 03:00:00 t2m   -81.25 -8~  2000     1     1     3
#> 5  257. -81.2 -80.2 2000-01-01 04:00:00 t2m   -81.25 -8~  2000     1     1     4
#> 6  257. -81.2 -80.2 2000-01-01 05:00:00 t2m   -81.25 -8~  2000     1     1     5
#> # ... with 2 more variables: month_seq <dbl>, day_seq <dbl>

Imported data can now be analysed freely. A simple monthly value aggregator is provided in monthmeans_era5(), which will determine the monthly mean or standard deviation for the value in a given given field within an ERA5 file. Months can be extracted from a single year, or across multiple years.

# Derive month means. Single year.
PH_temp_monthmeans <- monthmeans_era5(temp2m_2000_PH, total_months = TRUE) %>%
  mutate(month_varmean = month_varmean - 273.15) # K to C conversion
# Plot the monthly temperature means at this grid point for 1999, with standard devations
sd_polygon_avtemp <- data.frame(x = c(rep(PH_temp_monthmeans$month), 
                                      rev(rep(PH_temp_monthmeans$month))), 
                                y = c(PH_temp_monthmeans$month_varmean + PH_temp_monthmeans$month_varsd, 
                                      rev(PH_temp_monthmeans$month_varmean - PH_temp_monthmeans$month_varsd)))
month_labs <- c("J","F","M","A","M","J","J","A","S","O","N","D")
ggplot() +
  # SD polygon
  geom_polygon(data = sd_polygon_avtemp, aes(x = x, y = y), fill = "grey60", colour = "NA", alpha = 0.2) +
  # Avtemp
  geom_line(data = PH_temp_monthmeans, aes(x = month, y = month_varmean)) +
  geom_point(data = PH_temp_monthmeans, aes(x = month, y = month_varmean)) +
  # SD: upper
  geom_line(data = PH_temp_monthmeans, aes(x = month, y = month_varmean + month_varsd), linetype = 'dashed') +
  geom_point(data = PH_temp_monthmeans, aes(x = month, y = month_varmean + month_varsd), shape = 4) +
  # SD: lower
  geom_line(data = PH_temp_monthmeans, aes(x = month, y = month_varmean - month_varsd), linetype = 'dashed') +
  geom_point(data = PH_temp_monthmeans, aes(x = month, y = month_varmean - month_varsd), shape = 4) +
  # themes etc.
  scale_x_continuous(breaks = seq(1,12,1), labels = month_labs, expand = c(0,0)) +
  scale_y_continuous(expand = c(0,0), limits = c(-40,-5), breaks = seq(-40,-5,5)) +
  labs(x = "Month", y = "Temp in C") +
  ggtitle('ERA5 mean monthly 2m temperature during 2000 for 80.25S 81.25W') +
  theme_cowplot(12)