There is an application on the Climate Data Store (CDS) which allows users to compute and download selected daily statistics of variables from a number of hourly ERA5 datasets: Daily statistics calculated from ERA5 data
It provides users with a simple tool to obtain ERA5 data aggregated at daily frequency without having to download the original sub-daily resolution data. The ERA5 data is subset to the selected rectangular spatial region of interest and sampled at the selected frequency. Time coordinates can optionally be shifted to a selected time zone. The data is then aggregated to a daily frequency using the selected statistic and returned to the user in a single netCDF file.
It is possible also to use the CDS API to request daily data from the CDS ERA5 daily application as described on this user forum post:
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
- 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.
- 1st January 2017 time = 01 - 23 will give you total precipitation data to cover 00 - 23 UTC for 1st January 2017
- 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')
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)