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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.

Time 00 UTC and step 12 will give you 00 - 12 UTC total precipitation data
Time 12 UTC and step 12 will give you 12 - 24 UTC total precipitation data

Code Block

language	py

#!/usr/bin/env python
"""
Save as get-tp.py, then run "python get-tp.py".

Input file : None
Output file: tp_20180101.nc
"""
from ecmwfapi import ECMWFDataServer

server = ECMWFDataServer()
server.retrieve({
    "class"  : "ei",
    "dataset": "interim",
    "date"   : "2018-01-01",
    "expver" : "1",
    "grid"   : "0.75/0.75",
    "levtype": "sfc",
    "param"  : "228.128",
    "step"   : "12",
    "stream" : "oper",
    "time"   : "00:00:00/12:00:00",
    "type"   : "fc",
    "format" : "netcdf",
    "target" : "tp_20180101.nc",
})

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.

Code Block

language	py

#!/usr/bin/env python
"""
Save as file calculate-daily-tp.py and run "python calculate-daily-tp.py".

Input file : tp_20180101.nc
Output file: daily-tp_20180101.nc
"""
import time, sys
from datetime import datetime, timedelta

from netCDF4 import Dataset, date2num, num2date
import numpy as np

day = 20180101
f_in = 'tp_%d.nc' % day
f_out = 'daily-tp_%d.nc' % 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'))
            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# Variable(s)
        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(var_tp.name, np.double, var_tp.dimensions)
        var[0, :, :] = data

        #var.units = units
        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)

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