# Calculate climatology of 2m temperature (ERA5) # Period: Copernicus: (1993-2016) # # Script version 1.1 # # Script works with: # Python version: 3.10.13 # # Package version # numpy: 1.26.3 # pandas: 2.2.0 # netCDF4: 1.6.4 # # Ver1.0.0: Created by Mariko Koseki, 19.11.2024 # ver1.1: updated by Mariko, 20.10.2025 import netCDF4 import numpy as np from numpy import dtype import pandas as pd import datetime import os import sys import platform print('--- Python version ---') print(platform.python_version()) print('--- Package version ---') print('numpy: ', np.__version__) print('pandas: ', pd.__version__) print('netCDF4: ', netCDF4.__version__) ##--- Set path ------------------------------------------## ## Set path to input files ## ''' Set '' (path to input NetCDF file) ''' era5_monthly = '/nird/projects/NS9873K/norcpm/validation/reanalysis/ECMWF/ERA5/original/monthly_single_level/monthly_averaged_reanalysis/2t/' ## Set path to output files ## ''' Set '' (path to output file) ''' out_dir = '/nird/projects/NS9873K/norcpm/validation/reanalysis/ECMWF/ERA5/clim/2m_temperature/' if not os.path.exists(out_dir): os.makedirs(out_dir) ##--- Set year, month ------------------------------------## ''' Set 'start_year' and 'end_year' Copernicus: 1993-2016 ''' start_year = 1993 # 1993 end_year = 2016 # 2016 #y_str_list = np.arange(start_year,end_year).tolist() #ym_str_list = ["{}{:02}".format(y,m) for y in range(start_year,end_year) for m in range(1,13)] y_str_list = ["{}".format(y) for y in range(start_year,end_year+1)] m_str_list = ["{:02}".format(m) for m in range(1,13)] ## Set length of variables ## nyear = len(y_str_list) nmonth = len(m_str_list) nlat = 721; nlon = 1440 months = np.arange(1, nmonth + 1) ##--- Read netCDF files ----------------------------------## ## Calculate climatology ## ''' ## Example: "ERA5_167_1993.nc" ## #ver1.1 dimensions: valid_time = 12 ; latitude = 721 ; longitude = 1440 ; variables: int64 number ; number:long_name = "ensemble member numerical id" ; number:units = "1" ; number:standard_name = "realization" ; int64 valid_time(valid_time) ; valid_time:long_name = "time" ; valid_time:standard_name = "time" ; valid_time:units = "seconds since 1970-01-01" ; valid_time:calendar = "proleptic_gregorian" ; double latitude(latitude) ; latitude:_FillValue = NaN ; latitude:units = "degrees_north" ; latitude:standard_name = "latitude" ; latitude:long_name = "latitude" ; latitude:stored_direction = "decreasing" ; double longitude(longitude) ; longitude:_FillValue = NaN ; longitude:units = "degrees_east" ; longitude:standard_name = "longitude" ; longitude:long_name = "longitude" ; string expver(valid_time) ; float t2m(valid_time, latitude, longitude) ; t2m:_FillValue = NaNf ; t2m:GRIB_paramId = 167LL ; t2m:GRIB_dataType = "an" ; t2m:GRIB_numberOfPoints = 1038240LL ; t2m:GRIB_typeOfLevel = "surface" ; t2m:GRIB_stepUnits = 1LL ; t2m:GRIB_stepType = "avgua" ; t2m:GRIB_gridType = "regular_ll" ; t2m:GRIB_uvRelativeToGrid = 0LL ; t2m:GRIB_NV = 0LL ; t2m:GRIB_Nx = 1440LL ; t2m:GRIB_Ny = 721LL ; t2m:GRIB_cfName = "unknown" ; t2m:GRIB_cfVarName = "t2m" ; t2m:GRIB_gridDefinitionDescription = "Latitude/Longitude Grid" ; t2m:GRIB_iDirectionIncrementInDegrees = 0.25 ; t2m:GRIB_iScansNegatively = 0LL ; t2m:GRIB_jDirectionIncrementInDegrees = 0.25 ; t2m:GRIB_jPointsAreConsecutive = 0LL ; t2m:GRIB_jScansPositively = 0LL ; t2m:GRIB_latitudeOfFirstGridPointInDegrees = 90. ; t2m:GRIB_latitudeOfLastGridPointInDegrees = -90. ; t2m:GRIB_longitudeOfFirstGridPointInDegrees = 0. ; t2m:GRIB_longitudeOfLastGridPointInDegrees = 359.75 ; t2m:GRIB_missingValue = 3.40282346638529e+38 ; t2m:GRIB_name = "2 metre temperature" ; t2m:GRIB_shortName = "2t" ; t2m:GRIB_totalNumber = 0LL ; t2m:GRIB_units = "K" ; t2m:long_name = "2 metre temperature" ; t2m:units = "K" ; t2m:standard_name = "unknown" ; t2m:GRIB_surface = 0. ; t2m:coordinates = "number valid_time latitude longitude expver" ; ''' t2m_clim_median_3d = np.zeros((nmonth, nlat, nlon)) #(12, 721, 1440) t2m_clim_mean_3d = np.zeros((nmonth, nlat, nlon)) #ver1.1 for mon_num in range(0, nmonth): mm = m_str_list[mon_num] print('month: ', mm) ##--- Calculate median for nyear -----------------------------------------------------### t2m_clim_median = np.zeros((nyear, nlat, nlon)) #(nyear, 721, 1440) t2m_clim_mean = np.zeros((nyear, nlat, nlon)) #ver1.1 for year_num in range(0, nyear): yyyy = y_str_list[year_num] print('year: ', yyyy) nc_2m_temp = era5_monthly + 'ERA5_167_' + yyyy + '.nc' #ver1.1 print('file name: ', nc_2m_temp) print('') nc = netCDF4.Dataset(nc_2m_temp, 'r') ''' ## Check length of variables ## nlon = len(nc.dimensions['longitude']) # Longitude: nlat = len(nc.dimensions['latitude']) # Latitude: print(nlon, nlat) ''' ## Read variables ## lons = nc.variables['longitude'][:] # Longitude lats = nc.variables['latitude'][:] # Latitude t2m = nc.variables['t2m'][:][:][:] # t2m(date, latitude, longitude) ## Convert variables into Numpy ndarray ## t2m_np = np.array(t2m) print(t2m_np.shape) t2m_np2 = t2m_np[mon_num,:,:] print(t2m_np2.shape) ## Add results into a zeros numpy ndarray ## t2m_clim_median[year_num, :,:] = t2m_np2 t2m_clim_mean[year_num, :,:] = t2m_np2 #ver1.1 ## Close netCDF file ## nc.close() median_2d = np.median(t2m_clim_median, axis=0) #(721, 1440) mean_2d = np.mean(t2m_clim_mean, axis=0) #(721, 1440) #ver1.1 t2m_clim_median_3d[mon_num,:,:] = median_2d t2m_clim_mean_3d[mon_num,:,:] = mean_2d #ver1.1 print('file shape of 3D', t2m_clim_median_3d.shape) #(12, 721, 1440) print('') print('Calculation completed') print('') print('------------------------') ##--- Save results as a new NetCDF file ------------------------------## print('Save climatology') ### Median ### ## File name ## outfile = out_dir + 'era5_2m_temperature_clim_median_' + str(start_year) + '_' + str(end_year) + '.nc' nc2 = netCDF4.Dataset(outfile ,'w', format="NETCDF4") ## Define dimensions ## nc2.createDimension('latitude', nlat) nc2.createDimension('longitude', nlon) nc2.createDimension('month', nmonth) ## Define variables ## ### latitude ### lat = nc2.createVariable('latitude', dtype('float32').char, ('latitude',)) lat.units = 'degrees_north' lat.long_name = 'latitude' lat[:] = lats ### longitude ### lon = nc2.createVariable('longitude', dtype('float32').char, ('longitude',)) lon.units = 'degrees_east' lon.long_name = 'longitude' lon[:] = lons ### month ### month = nc2.createVariable('month', dtype('int32').char, ('month',)) month.units = 'month' month.long_name = 'month' month[:] = months ### climatology ### clim_2m_temp = nc2.createVariable('clim_2m_temp_era5', dtype('float32').char, ('month', 'latitude', 'longitude',)) clim_2m_temp.units = 'K' clim_2m_temp.long_name = 'climatology of 2meter temperature ERA5' clim_2m_temp[:,:,:] = t2m_clim_median_3d ### close NetCDF file ### nc2.close() ### Mean ### ## File name ## outfile = out_dir + 'era5_2m_temperature_clim_mean_' + str(start_year) + '_' + str(end_year) + '.nc' nc3 = netCDF4.Dataset(outfile ,'w', format="NETCDF4") ## Define dimensions ## nc3.createDimension('latitude', nlat) nc3.createDimension('longitude', nlon) nc3.createDimension('month', nmonth) ## Define variables ## ### latitude ### lat = nc3.createVariable('latitude', dtype('float32').char, ('latitude',)) lat.units = 'degrees_north' lat.long_name = 'latitude' lat[:] = lats ### longitude ### lon = nc3.createVariable('longitude', dtype('float32').char, ('longitude',)) lon.units = 'degrees_east' lon.long_name = 'longitude' lon[:] = lons ### month ### month = nc3.createVariable('month', dtype('int32').char, ('month',)) month.units = 'month' month.long_name = 'month' month[:] = months ### climatology ### clim_2m_temp = nc3.createVariable('clim_2m_temp_era5', dtype('float32').char, ('month', 'latitude', 'longitude',)) clim_2m_temp.units = 'K' clim_2m_temp.long_name = 'climatology of 2meter temperature ERA5' clim_2m_temp[:,:,:] = t2m_clim_mean_3d ### close NetCDF file ### nc3.close() print('') print('Completed!!')