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This knowledge base article shows you how to calculate daily total precipitation using ERA5 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:

  • CDS API (tested with 0.1.1) - 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 ERA5 data for 1st and 2nd January 2017. This script will download total precipitation, in hourly steps, from CDS (Climate Data Store). Notice to cover total precipitation for 1st January 2017, we need two days of data.
    1. 1st January 2017 time = 01 - 23  will give you total precipitation data to cover 00 - 23 UTC for 1st January 2017
    2. 2nd January 2017 time = 00 will give you total precipitation data to cover 23 - 24 UTC for 1st January 2017
    #!/usr/bin/env python
    """
    Save as get-tp.py, then run "python get-tp.py".
     
    Input file : None
    Output file: tp_20170101-20170102.nc
    """
    import cdsapi
    
    c = cdsapi.Client()
    r = c.retrieve(
        'reanalysis-era5-single-levels', {
                'variable'    : 'total_precipitation',
                'product_type': 'reanalysis',
                'year'        : '2017',
                'month'       : '01',
                'day'         : ['01', '02'],
                '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'
                ],
                'format'      : 'netcdf'
        })
    r.download('tp_20170101-20170102.nc')
  2. Run a second script to calculate daily total precipitation. All it does is to add up 24 values for a given day as describe in step 1.

    #!/usr/bin/env python
    """
    Save as file calculate-daily-tp.py and run "python calculate-daily-tp.py".
     
    Input file : tp_20170101-20170102.nc
    Output file: daily-tp_20170101.nc
    """
    import time, sys
    from datetime import datetime, timedelta
    
    from netCDF4 import Dataset, date2num, num2date
    import numpy as np
    
    day = 20170101
    d = datetime.strptime(str(day), '%Y%m%d')
    f_in = 'tp_%d-%s.nc' % (day, (d + timedelta(days = 1)).strftime('%Y%m%d'))
    f_out = 'daily-tp_%d.nc' % day
    
    time_needed = []
    for i in range(1, 25):
        time_needed.append(d + timedelta(hours = i))
    
    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'))
                sys.exit(200)
            else:
                print('Found %s' % tm.strftime('%Y%m%d %H:%M:%S'))
                indices.append(a[0])
    
        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
            else:
                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)
            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)
    
            # Variables
            var = ds_dest.createVariable(var_tp.name, 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'
                    % (datetime.now().strftime('%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 https://www.unidata.ucar.edu/software/netcdf/docs/BestPractices.html#bp_Packed-Data-Values for detailed information.