hydromt.model.processes.meteo.temp#

hydromt.model.processes.meteo.temp(temp, dem_model: DataArray, dem_forcing: DataArray | None = None, lapse_correction: bool = True, freq: str | Timedelta | None = None, reproj_method: str = 'nearest_index', lapse_rate: float = -0.0065, resample_kwargs: Dict[str, str] | None = None) → DataArray[source]#

Return lazy reprojection of temperature to model grid.

Use lapse_rate for downscaling, and resampling of time dimension to frequency.

Parameters:

  • temp (xarray.DataArray) – DataArray of temperature forcing [°C]

  • dem_model (xarray.DataArray) – DataArray of the target resolution and projection, contains elevation data

  • dem_forcing (xarray.DataArray, optional) – DataArray of elevation at forcing resolution. If provided this is used with dem_model to correct the temperature downscaling using a lapse rate

  • lapse_correction (bool, optional) – If True, temperature is correctured based on lapse rate, by default True.

  • freq (str, Timedelta, optional) – output frequency of timedimension

  • reproj_method (str, optional) – Method for spatital reprojection of precip, by default ‘nearest_index’

  • lapse_rate (float, optional) – lapse rate of temperature [C m-1] (default: -0.0065)

  • resample_kwargs (dict, optional) – Additional key-word arguments (e.g. label, closed) for time resampling method

Returns:

t_out – processed temperature forcing

Return type:

xarray.DataArray (lazy)