ERA-Interim: How to calculate daily total precipitation

This knowledge base article shows you how to calculate daily total precipitation using ERA-Interim data. If you just want monthly means, then you can simply download it from http://apps.ecmwf.int/datasets/data/interim-mdfa/levtype=sfc/.

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

• How to download ERA-Interim data from the ECMWF data archive
• ERA-Interim: 'time' and 'steps', and instantaneous, accumulated and min/max parameters

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:

• ECMWF WebAPI (tested with 1.5.0) - 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
   """
   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",
   })

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 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)
   
           # 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(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)
   
   
   

Related articles

• Page:
  Transformation or regridding of ECMWF Reanalyses data
• Page:
  Model grid box and time step
• Page:
  ERA-Interim: documentation
• Page:
  ERA-Interim: How to calculate daily total precipitation
• Page:
  ERA-Interim known issues