ecmwfr

Programmatic interface to the two European Centre for Medium-Range Weather Forecasts API services. The package provides easy access to all available Data Stores from within R, matching and expanding upon the ECMWF python tools. Support is provided for the Climate Data Store, the Atmosphere Data Store and the Early Warning Data Store (from the Copernicus Emergency Management Services).

How to cite this package

You can cite this package like this "we obtained data from the European Centre for Medium-Range Weather Forecasts API using the ecmwf R package (Hufkens, Stauffer, and Campitelli 2019)". Here is the full bibliographic reference to include in your reference list (don't forget to update the 'last accessed' date):

Hufkens, K., R. Stauffer, & E. Campitelli. (2019). ecmwfr: Programmatic interface to the two European Centre for Medium-Range Weather Forecasts API services. Zenodo. https://doi.org/10.5281/zenodo.2647531.

Installation

stable release

To install the current stable release use a CRAN repository:

install.packages("ecmwfr")
library("ecmwfr")

development release

To install the development releases of the package run the following commands:

if(!require(remotes)){install.packages("remotes")}
remotes::install_github("bluegreen-labs/ecmwfr")
library("ecmwfr")

Vignettes are not rendered by default, if you want to include additional documentation please use:

if(!require(remotes)){install.packages("remotes")}
remotes::install_github("bluegreen-labs/ecmwfr", build_vignettes = TRUE)
library("ecmwfr")

breaking changes (>= 2.0.0)

querying data

With the introduction of version 2.0.0 and the migration to the new API some changes to the package were implemented which will cause breaking changes. In particular the wf_request() function(s) now use a default ecmwfr user field due to the consolidation of the API, with a single sign-on across all services and the use of a Personal Access Token (PAT) rather than user and password credentials.

In order to migrate to version >=2.0.0 you will have to provide a new PAT using wf_set_key() and remove the user argument from any wf_request() call, i.e.:

# The original v1.x.x call
wf_request(
  request,
  user = "your_id"
)

# The new v2.x.x call
wf_request(
  request
)

The requests themselves should translate mostly without intervention and remain nested lists of parameters.

netCDF data format

In comparison with the original services the new API regresses in terms of netCDF support. Those relying on common netCDF support such as ecosystem modellers will find this troubling. Note that both CDS and ADS have different policies and use different methods. Note that this regression in usability is not caused by this package. Please forward any issues you have with formatting of the data to the ECMWF using the public forum. We suggest to fall back to grib files, and convert internally if netCDF driver files are needed. Sadly, consistency for now is not, and will not be, guaranteed it seems.

Use: ECMWF Data Store services

Create a ECMWF account by self registering. Once your user account has been verified you can get your personal access token or key by visiting one of the Data Stores user profiles, for example the CDS user profile.

The Personal Access Token is a long string of letters and numbers:

Personal Access Token: abcd1234-foo-bar-98765431-XXXXXXXXXX

This Personal Access Token gives you access to all Data Store services, including the climate atmosphere and emergency management services. This information is required to be able to retrieve data via the ecmwfr package. Use the ecmwfr wf_set_key function to store your login information in the system keyring (see below).

Setup

You have to save your login information before proceeding. The package does not allow you to use your key inline in scripts to limit security issues when sharing scripts on github or otherwise. The following lines should NEVER be included in any script and run only once at setup.

# set a key to the keychain
wf_set_key(key = "abcd1234-foo-bar-98765431-XXXXXXXXXX")

# you can retrieve the key using
wf_get_key()

# the output should be the key you provided
# "abcd1234-foo-bar-98765431-XXXXXXXXXX"

# Alternatively you can input your login info with an interactive request
# if you do not put in the key directly
wf_set_key()

# you will get a command line request to provide the required details

Before you can download any data you have to make sure to accept the terms and conditions here: Before downloading and processing data from CDS please make sure you accept the terms and conditions in the profile pages of your Data Store of choice.

Data Requests

To download data use the wf_request function, and a request string syntax. The simplest way to get the requests is to go to the Data Store website which offers an interactive interface to create these requests e.g., for the CDS ERA-5 reanalysis data:

After formatting the request online copy the API request python code to your script. The request should include the dataset, request and target field (if available).

Instead of json formatting as shown in the online form the ecmwfr package uses a R lists for all the arguments. This makes changing variables less prone to error, although overall we suggest not to manually create requests and use the RStudio Addin to translate the python json request to R as shown below.

Just select the whole query, including the dataset and target fields, and click on the Addins > ECMWF Python to list. The original python query is listed below so you can try this routine yourself.

# The full python query, which you can translate to an R
# list using the Addin

dataset = "reanalysis-era5-pressure-levels"
request = {
  'product_type': ['reanalysis'],
  'variable': ['temperature'],
  'year': ['2000'],
  'month': ['04'],
  'day': ['04'],
  'time': ['00:00'],
  'pressure_level': ['850'],
  'data_format': 'netcdf',
  'download_format': 'unarchived',
  'area': [70, -20, 60, 30]
}

This will give you a request as an annotated list. If no target file is specified in the original request a target field will be added to the list with a default name TMPFILE. Replace this filename with something that matches your preference and the specified data format. In this case the default name was changed to era5-demo.nc, a netcdf file. This formatted request can now be used by the wf_request function to query and download the data. By default the process is verbose, and will give you plenty of feedback on progress.

# This is an example of a request as converted from 
request <- list(
  dataset_short_name = "reanalysis-era5-pressure-levels",
  product_type = "reanalysis",
  variable = "temperature",
  year = "2000",
  month = "04",
  day = "04",
  time = "00:00",
  pressure_level = "850",
  data_format = "netcdf",
  download_format = "unarchived",
  area = c(70, -20, 60, 30),
  target = "era5-demo.nc"
)

# If you have stored your user login information
# in the keyring by calling cds_set_key you can
# call:
file <- wf_request(
 request  = request,  # the request
 transfer = TRUE,     # download the file
 path     = "."       # store data in current working directory
 )

The Data Store services are quite fast, however, if you request a lot of variables, multiple levels, and data over several years these requests might take quite a while! You can check the scope of your query and if it is out of bounds in the right hand Request Validation panel when formatting your original data request in the web interface.

Note: If you need to download larger amounts of data it is suggested to split the downloads, e.g., download the data in chunks (e.g., month-by-month, or year-by-year). A progress indicator will keep you informed on the status of your request. Keep in mind that all data downloaded will be buffered in memory limiting the downloads to ~6GB on low end systems.

ERA5 demo

The above request uses ERA-5 reanalysis data on pressure levels.

dataset: downloading ERA-5 reanalysis on pressure level(s)
product_type: (deterministic) reanalysis data
variable/pressure_level: requesting temperature on 850 hectopascal
year/month/day: April 4, 2000 (one day in this example)
time: valid at 00:00 UTC (date/time always in UTC),
area: custom subset covering northern Europe
format: output format NetCDF
target: local output file era5-demo.nc

The data set as specified above can be downloaded calling the wf_request function, and upon request returns the location of the NetCDF file on your system. The file variable can then be used directly for further processing (note: should the download be interupted the job ID in your download queue is returned and the job can be resumed using wf_transfer()).

Once the retrieval has finished you should now be the owner of a NetCDF containing the requested information located in the current working directory, called era5-demo.nc.

We can quickly visualize this data using the terra geospatial library using:

# Open NetCDF file and plot the data
# (trap read error on mac - if gdal netcdf support is missing)
r <- terra::rast(file)
terra::plot(r, main = "ERA-5 Reanalysis Demo (2m Temperature 850 hPa)")
maps::map("world", add = TRUE)

File and system based keychains

On linux you can opt to use a file based keyring, instead of a GUI based keyring manager. This is helpful for headless setups such as servers. For this option to work linux users must set an environmental option.

options(keyring_backend="file")

You will be asked to provide a password to encrypt the keyring with. Upon the start of each session you will be asked to provide this password, unlocking all ecmwfr credentials for this session. Should you ever forget the password just delete the file at: ~/.config/r-keyring/ecmwfr.keyring and re-enter all your credentials.

Date specification

For those familiar with the old ECMWF mars syntax: CDS/ADS/CEMS does not accept date = "2000-01-01/to/2000-12-31" specifications. It is possible to specify one specific date via date = "2000-01-01" or multiple days via date = ["2000-01-01","2000-01-02","2000-10-20"] or date = "YYYY-MM-DD/YYYY-MM-DD".

Citation