Dataset.xwrf.postprocess(decode_times=True, calculate_diagnostic_variables=True, drop_diagnostic_variable_components=True)

Postprocess the dataset. This method will perform the following operations:

  • Rename dimensions to match the CF conventions.

  • Rename variables to match the CF conventions.

  • Rename variable attributes to match the CF conventions.

  • Convert units to Pint-friendly units.

  • Decode times.

  • Include projection coordinates.

  • Collapse time dimension.

  • decode_times (bool, optional) – Decode the string-like wrfout times to xarray-friendly Pandas types. Defaults to True.

  • calculate_diagnostic_variables (bool, optional) – Calculates essential diagnostic variables (potential temperature, air pressure, geopotential, and geopotential height) that are otherwise only present in wrfout files as split components or dependent upon special adjustments. Also calculates earth-relative wind fields, as winds by default are grid-relative. Defaults to True. If the underlying fields on which any of these calculated fields depends is missing, that calculated variable is skipped. These will be eagerly evalulated, unless your data has been chunked with Dask, in which case these fields will also be Dask arrays.

  • drop_diagnostic_variable_components (bool, optional) – Determine whether to drop the underlying fields used to calculate the diagnostic variables. Defaults to True. Never drops grid-relative wind fields.


The postprocessed dataset.

Return type: