Page History

#!/usr/bin/python3

import cdsapi
import netCDF4
from netCDF4 import num2date
import numpy as np
import os
import pandas as pd

# Retrieve data and store as netCDF4 file
c = cdsapi.Client()
file_location = './t2m.nc'
c.retrieve(
    'reanalysis-era5-single-levels',
    {
        'product_type':'reanalysis',
        'variable':'2m_temperature',  # 't2m'
        'year':'2019',
        'month':'06',
        'day':[
            '24','25'
        ],
        'time':[
            '00:00','06:00','12:00',
            '18:00'
        ],
        'format':'netcdf'
    },
    file_location)

# Open netCDF4 file
f = netCDF4.Dataset(file_location)

# Extract variable
t2m = f.variables['t2m']

# Get dimensions assuming 3D: time, latitude, longitude
time_dim, lat_dim, lon_dim = t2m.get_dims()
time_var = f.variables[time_dim.name]
times = num2date(time_var[:], time_var.units)
latitudes = f.variables[lat_dim.name][:]
longitudes = f.variables[lon_dim.name][:]

output_dir = './'

# =============================== METHOD 1 ============================
# Extract each time as a 2D pandas DataFrame and write it to CSV
# =====================================================================
os.makedirs(output_dir, exist_ok=True)
for i, t in enumerate(times):
    filename = os.path.join(output_dir, f'{t.isoformat()}.csv')
    print(f'Writing time {t} to {filename}')
    df = pd.DataFrame(t2m[i, :, :], index=latitudes, columns=longitudes)
    df.to_csv(filename)
print('Done')

# =============================== METHOD 2 ============================
# Write data as a table with 4 columns: time, latitude, longitude, value
# =====================================================================
filename = os.path.join(output_dir, 'table.csv')
print(f'Writing data in tabular form to {filename} (this may take some time)...')
times_grid, latitudes_grid, longitudes_grid = [
    x.flatten() for x in np.meshgrid(times, latitudes, longitudes, indexing='ij')]
df = pd.DataFrame({
    'time': [t.isoformat() for t in times_grid],
    'latitude': latitudes_grid,
    'longitude': longitudes_grid,
    't2m': t2m[:].flatten()})
df.to_csv(filename, index=False)
print('Done')

Please note that the netCDF file could contain an additional dimension when there is a mix of ERA5 and ERA5T data. For more details please have a look at the following link: ERA5 CDS requests which return a mixture of ERA5 and ERA5T data

_{This document has been produced in the context of the Copernicus Atmosphere Monitoring Service (CAMS) and Copernicus Climate Change Service (C3S).}

_{The activities leading to these results have been contracted by the European Centre for Medium-Range Weather Forecasts, operator of CAMS and C3S on behalf of the European Union (Delegation Agreement signed on 11/11/2014 and Contribution Agreement signed on 22/07/2021). All information in this document is provided "as is" and no guarantee or warranty is given that the information is fit for any particular purpose.}

_{The users thereof use the information at their sole risk and liability. For the avoidance of all doubt , the European Commission and the European Centre for Medium - Range Weather Forecasts have no liability in respect of this document, which is merely representing the author's view.}