pyrtlib.apiwebservices.ERA5Reanalysis.read_data#

classmethod ERA5Reanalysis.read_data(file: str, lonlat: tuple) → DataFrame#

Read data from the ERA5 Reanalysis dataset.

Parameters:

file (str) – The netcdf file
lonlat (tuple) – longitude and latitude

Returns:

Dataframe containing the variables retrieved.

Return type:

pandas.DataFrame

Note

Variables name and units information are reported within the attribute units of the returned dataframe (see example below).

Example

>>> from pyrtlib.apiwebservices import ERA5Reanalysis
>>> lonlat = (15.8158, 38.2663)
>>> date = datetime(2020, 2, 22, 12)
>>> nc_file = ERA5Reanalysis.request_data(tempfile.gettempdir(), date, lonlat)
>>> df_era5 = ERA5Reanalysis.read_data(nc_file, lonlat)
>>> df_era5.attrs['units']
{'p': 'hPa',
'z': 'km',
't': 'K',
'rh': '%',
'clwc': 'kg kg-1',
'ciwc': 'kg kg-1',
'crwc': 'kg kg-1',
'cswc': 'kg kg-1',
'o3': 'kg kg-1',
'q': 'kg kg-1'}

Note

To convert specific cloud water content (CLWC) or specific cloud ice water content (CIWC) from kg kg-1 to g m-3 using this function pyrtlib.utils.kgkg_to_gm3()

Examples using `pyrtlib.apiwebservices.ERA5Reanalysis.read_data`#

Performing Upwelling Brightness Temperature calculation using ERA5 Reanalysis Observations.

Performing Upwelling Brightness Temperature calculation using ERA5 Reanalysis Observations in cloudy condition.

pyrtlib.apiwebservices.ERA5Reanalysis.read_data#

Examples using pyrtlib.apiwebservices.ERA5Reanalysis.read_data#

Examples using `pyrtlib.apiwebservices.ERA5Reanalysis.read_data`#