ERA5: How to calculate daily total precipitation

Last modified on Dec 28, 2023 13:01

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:

• How to use the CDS API
• ERA5 terminology: analysis and forecast; time and steps; instantaneous and accumulated and mean rates and min/max parameters
• ERA5: data documentation

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)
   

Related articles

• Page:
  ERA5: How to calculate wind speed and wind direction from u and v components of the wind?
• Page:
  Parameters valid at the specified time
• Page:
  Convective and large-scale precipitation
• Page:
  Model grid box and time step
• Page:
  EQC documentation