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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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