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 datadem_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 ratelapse_correction (
bool
, optional) – If True, temperature is correctured based on lapse rate, by default True.freq (
str
,Timedelta
, optional) – output frequency of timedimensionreproj_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)