ERA-Interim production to stop on 31st August 2019

For ERA-Interim (1st January 1979 to 31st August 2019) access through the ECMWF Web API stopped on 01 June 2023

Its successor ERA5 is available from the Climate Data Store (CDS) (What are the changes from ERA-Interim to ERA5?) and users are strongly advised to migrate to ERA5 (How to download ERA5).

For those users who still need access to ERA-Interim after 01 June 2023 (subject to further notice), they can do so via the Climate Data Store (CDS) API.

This knowledge base article shows you how to calculate daily total precipitation using ERA-Interim data. 

Before you continue, make sure you read through knowledge base articles listed below:

You are also supposed to know how to work with Python under Linux, in particular, how to install packages using pip. You are recommended to use the latest release of packages listed here:

  •  Climate Data Store (CDS) API - required for step 1
  • netCDF4 (tested with 1.4.0) - required for step 2
  • numpy (tested with 1.14.5) - required for step 2

Step-by-step guide

  1. Use script below to download daily total precipitation ERA-Interim data for 1st January 2018. This script will download total precipitation from two forecasts at 00 UTC and 12 UTC, both at step 12. This will give you 24 hours coverage.
    1. Time 00 UTC and step 12 will give you 00 - 12 UTC total precipitation data
    2. Time 12 UTC and step 12 will give you 12 - 24 UTC total precipitation data
    #!/usr/bin/env python
       import cdsapi
       c = cdsapi.Client()
       c.retrieve('reanalysis-era-interim', { # Requests follow MARS syntax
                                              # Keywords 'expver' and 'class' can be dropped. They are obsolete
                                              # since their values are imposed by 'reanalysis-era-interim'
           'date'    : '2018-01-01',          # The hyphens can be omitted
           'levtype' : 'sfc',                  # Pressure levels, 'ml' for model levels, 'sfc' for surface
           'param'   : '228',                 # Full information at
                                              # The native representation for temperature is spherical harmonics
           'stream'  : 'oper',                # Denotes atmospheric fields. Wave fields use 'wave'.
           'time'    : '00/12',               
           'type'    : 'fc',
    	   'step'    : '00/to/12'
           'area'    : '80/-50/-25/0',        # North, West, South, East. Default: global
           'grid'    : '1.0/1.0',             # Latitude/longitude. Default: spherical harmonics or reduced Gaussian grid
           'format'  : 'netcdf',              # Output needs to be regular lat-lon, so only works in combination with 'grid'!
       }, '')   # Output file. Adapt as you wish.
  2. Run a second script to calculate daily total precipitation. All it does is to add up the two values for 00 - 12 UTC and 12 - 24 UTC for a given day.

    #!/usr/bin/env python
    Save as file and run "python".
    Input file :
    Output file:
    import time, sys
    from datetime import datetime, timedelta
    from netCDF4 import Dataset, date2num, num2date
    import numpy as np
    day = 20180101
    f_in = '' % day
    f_out = '' % day
    d = datetime.strptime(str(day), '%Y%m%d')
    time_needed = [d + timedelta(hours = 12), d + timedelta(days = 1)]
    with Dataset(f_in) as ds_src:
        var_time = ds_src.variables['time']
        time_avail = num2date(var_time[:], var_time.units,
                calendar = var_time.calendar)
        indices = []
        for tm in time_needed:
            a = np.where(time_avail == tm)[0]
            if len(a) == 0:
                sys.stderr.write('Error: precipitation data is missing/incomplete - %s!\n'
                        % tm.strftime('%Y%m%d %H:%M:%S'))
                print('Found %s' % tm.strftime('%Y%m%d %H:%M:%S'))
        var_tp = ds_src.variables['tp']
        tp_values_set = False
        for idx in indices:
            if not tp_values_set:
                data = var_tp[idx, :, :]
                tp_values_set = True
                data += var_tp[idx, :, :]
        with Dataset(f_out, mode = 'w', format = 'NETCDF3_64BIT_OFFSET') as ds_dest:
            # Dimensions
            for name in ['latitude', 'longitude']:
                dim_src = ds_src.dimensions[name]
                ds_dest.createDimension(name, dim_src.size)
                var_src = ds_src.variables[name]
                var_dest = ds_dest.createVariable(name, var_src.datatype, (name,))
                var_dest[:] = var_src[:]
                var_dest.setncattr('units', var_src.units)
                var_dest.setncattr('long_name', var_src.long_name)
            ds_dest.createDimension('time', None)
            # Variables
            var = ds_dest.createVariable('time', np.int32, ('time',))
            time_units = 'hours since 1900-01-01 00:00:00'
            time_cal = 'gregorian'
            var[:] = date2num([d], units = time_units, calendar = time_cal)
            var.setncattr('units', time_units)
            var.setncattr('long_name', 'time')
            var.setncattr('calendar', time_cal)
            var = ds_dest.createVariable(, np.double, var_tp.dimensions)
            var[0, :, :] = data
            var.setncattr('units', var_tp.units)
            var.setncattr('long_name', var_tp.long_name)
            # Attributes
            ds_dest.setncattr('Conventions', 'CF-1.6')
            ds_dest.setncattr('history', '%s %s'
                    % ('%Y-%m-%d %H:%M:%S'),
                    ' '.join(time.tzname)))
            print('Done! Daily total precipitation saved in %s' % f_out)
For simplicity, data in the output NetCDF file of the second script is unpacked. You may want to pack the data to save some disk spaces. Refer to for detailed information.

This document has been produced in the context of the Copernicus Climate Change Service (C3S).

The activities leading to these results have been contracted by the European Centre for Medium-Range Weather Forecasts, operator of C3S on behalf of the European Union (Delegation agreement signed on 11/11/2014). All information in this document is provided "as is" and no guarantee or warranty is given that the information is fit for any particular purpose.

The users thereof use the information at their sole risk and liability. For the avoidance of all doubt, the European Commission and the European Centre for Medium-Range Weather Forecasts have no liability in respect of this document, which is merely representing the author's view.