update 15.01.2022 All tests successful This latest version of the toolbox has now been tested for both the Rutgers and Kate Hedstroms versions of the code. For both versions, the correct forcing files are created and are read correctly by the ROMS source code. In both tests cases the results show an improvement in vertical mixing of heat in the water column due to the higher time frequency compared to ERA INTERIM.

The toolbox has also been tested for an Arctic model that covers 40-90N and -180 to 180E. Initially, ROMS did not accept the ERA5 data as the maximum longitude of the ERA5 data was slightly less than the model grid.

          Gridded:  LonMin = -180.0000 LonMax =  179.7500
                    LatMin =   40.0000 LatMax =   90.0000
          Model:    LonMin = -179.9848 LonMax =  179.9680
                    LatMin =   45.0081 LatMax =   89.9205

To resolve this issue you can pply a brillian CDO command called sethalo which basically replicates the last index value (-180E) at the maximum longitude (180E) to wrap the globe. To repeat this command on all files generated by this toolbox run(also new file added to repository here:

for file in era5*.nc
do
 name=${file##*/}
 base=${name%.nc}
 echo "$file" new file "${base}_halo.nc"
 cdo sethalo,0,1 "$file" "${base}_halo.nc"
done

After runnning the CDO commands (add_halo.sh) you may need to update the attributes of the .nc files by running add_attributes_to_netcdf4.sh found here

A new option to reduce the memory footprint of the result files has been implemented. The option extract_data_every_6_hours (defined as True/False in ECMWF_query.py) allow you to specify 6 hourly, instead of hourly, output to file frequency. For forcing files that cover large regions such as the entire Northern Hemisphere, this drastically reduces the total file size. The option can be used to specify other output frequency through the list of timesteps required (ECMWF:tools.py):

if self.config_ecmwf.extract_data_every_6_hours is True:
			times = ['00:00', '06:00', '12:00', '18:00']

update 12.11.2021 - resolved Believe that the issue with ROMS reading ERA5 generated files using ERA5-ROMs has been resolved by inverting the latitude (and data array) of ERA5. Apparently ROMS is not able to read the ERA5 in the ECMWF standard way of representing latitude. Please create an issue if this is still a problem!

update 10.08.2020 After conversations with ECMWF support I changed the code to only use ERA5 parameter names and not parameter IDs as these did not result in stable downloads (some parameter IDs seems to be missing for some months while the parameter names are consistent).

This toolbox enables you to download ERA5 atmospheric forcing data for your model domain for a specified period. The toolbox uses the [Climate Data Store] Python API to connect and download specific variables required by ROMS to perform simulations with atmospheric forcing. These variables are included in the list below:

   'Specific_humidity',
   '10m_u_component_of_wind',
   '10m_v_component_of_wind',
   '2m_temperature',
   '2m_dewpoint_temperature',
   'Mean_sea_level_pressure',
   'Total_cloud_cover',
   'Total_precipitation',
   'Mean_surface_net_short-wave_radiation_flux',
   'Mean_surface_net_long-wave_radiation_flux',
   'Mean_surface_downward_long-wave_radiation_flux',
   'Mean_surface_latent_heat_flux',
   'Mean_surface_sensible_heat_flux',
   'Evaporation',
   'Mean surface downward short-wave radiation flux'

Details of the ERA5 variables can be found here To see the details for how ROMS requires naming convention etc.

To start signup and get necessary credentials at the [Climate Data Store]. Store the credentials in a file called .cdsapircin you root $HOME directory. It should look something like this:

url: https://cds.climate.copernicus.eu/api/v2 key: 28122:f85a4564-8895-498d-ad8a-gf274ba38d2r ls -lrt

Edit the file ECMWF_query.pyto define the start and end period you want to download data. If you want you can edit the months, days, and time steps of the day data will be downloaded in the file ECMWF_tools.py but by default the program downloads data for all available reanalysis time steps of the day for all days for all months of the year. Each result file contains data for one variable for one year.

Note: If you are using Kate Hedstroms ROMS version with sea-ice you need to download 'mean_surface_downward_short_wave_radiation_flux' while the regular Rutgers ROMS version uses 'mean_surface_net_short_wave_radiation_flux'. This can be defined in the option self.ROMS_version = "Kate"in ECMWF_query.py.

The request API can be tested here: ECMWF ERA5 API requests and the Python cdsapi is described cdsapi. Data availability from Copernicus available here https://cp-availability.ceda.ac.uk/.

The region for where you extract the data is defined by the variable self.area = "80/0/50/25"found in ECMWF_query.py. The area is constrained by North/West/South/East.

The time units in the resulting ROMS files are converted from the ERA5 units (1900-01-01) to the standard ROMS reference time 1948-01-01. The toolbox uses the netCDF4 date2numand num2date functions for this conversion.

The main query for the call for data is found in ECMWF_tools.py

		def submit_request(self, parameter, year, out_filename):

		options = {
			'product_type': 'reanalysis',
			"year": year,
			"month": ["01", "02", "03", "04", "05", "06", "07", "08", "09", "10", "11", "12"],
			'day': [
				'01', '02', '03',
				'04', '05', '06',
				'07', '08', '09',
				'10', '11', '12',
				'13', '14', '15',
				'16', '17', '18',
				'19', '20', '21',
				'22', '23', '24',
				'25', '26', '27',
				'28', '29', '30',
				'31',
			],
			'time': [
				'00:00', '01:00', '02:00',
				'03:00', '04:00', '05:00',
				'06:00', '07:00', '08:00',
				'09:00', '10:00', '11:00',
				'12:00', '13:00', '14:00',
				'15:00', '16:00', '17:00',
				'18:00', '19:00', '20:00',
				'21:00', '22:00', '23:00',
			],
			"variable": [parameter],
			'format': "netcdf",
			"area": self.config_ecmwf.area,
			"verbose": self.config_ecmwf.debug,
		}
		# Add more specific options for variables on pressure surfaces
		if parameter == "specific_humidity":
			self.config_ecmwf.reanalysis = "reanalysis-era5-pressure-levels"
			options["levtype"] = 'pl'
			options["pressure_level"] = '1000'
		else:
			self.config_ecmwf.reanalysis = 'reanalysis-era5-single-levels'

		try:
			# Do the request
			self.server.retrieve(self.config_ecmwf.reanalysis, options, out_filename)
		except Exception as e:
			print(e)
			print("[!] -------------------------- PROBLEMS WITH {0}".format(out_filename))

		metadata = self.config_ecmwf.get_parameter_metadata(parameter)
		converter = ECMWF_convert_to_ROMS.ECMWF_convert_to_ROMS()
		converter.convert_to_ROMS_units_standards(out_filename, metadata, parameter, self.config_ecmwf)

To run the toolbox after editing the settings simply run python ECMWF_tools.py

If your model domain covers the entire Arctic, or northern hemisphere and reaches from -180 to 180 you have to convert the ERA5 data to be connected. This is done by introducing a fake point at 180 which is identical to point 0. This makes the data across the meridian line consistent and ROMS wont choke on it. This step requires CDO installed.

Run make_files_fully_global-sh:

for filename in results/*.nc; do
		echo "Converting: " "$filename" " to" "halo/$(basename "$filename" .nc)_halo.nc"
    cdo sethalo,0,1 "$filename" "halo/$(basename "$filename" .nc)_halo.nc"
done

####Unittest A few simple unittests are included in test_ERA5.py.