wrf.ctt¶

wrf.ctt(pres_hpa, tkel, qv, qcld, height, terrain, qice=None, meta=True, units=u'degC')¶

Return the cloud top temperature.

This is the raw computational algorithm and does not extract any variables from WRF output files. Use wrf.getvar() to both extract and compute diagnostic variables.

Parameters:

pres_hpa (xarray.DataArray or numpy.ndarray) –
Full pressure (perturbation + base state pressure) in [hPa], with the rightmost dimensions as bottom_top x south_north x west_east

Note

The units for psfc_hpa are [hPa].

Note

This variable must be supplied as a xarray.DataArray in order to copy the dimension names to the output. Otherwise, default names will be used.
tkel (xarray.DataArray or numpy.ndarray) – Temperature in [K] with same dimensionality as pres_hpa.
qv (xarray.DataArray or numpy.ndarray) – Water vapor mixing ratio in [kg/kg] with the same dimensionality as pres_hpa.
qcld (xarray.DataArray or numpy.ndarray) – Cloud water vapor mixing ratio in [kg/kg] with the same dimensionality as pres_hpa.
height (xarray.DataArray or numpy.ndarray) – Geopotential height in [m] with the same dimensionality as pres_hpa.
terrain (xarray.DataArray or numpy.ndarray) – Terrain height in [m]. This is at least a two-dimensional array with the same dimensionality as pres_hpa, excluding the vertical (bottom_top/top_bottom) dimension.
qice (xarray.DataArray or numpy.ndarray, optional) – Ice mixing ratio in [kg/kg] with the same dimensionality as pres_hpa.
meta (bool) – Set to False to disable metadata and return numpy.ndarray instead of xarray.DataArray. Default is True.
units (str) – The desired units. Refer to the getvar() product table for a list of available units for ‘ctt’. Default is ‘degC’.

Warning

The input arrays must not contain any missing/fill values or numpy.nan values.

Returns:	The cloud top temperature. If xarray is enabled and the meta parameter is True, then the result will be an `xarray.DataArray` object. Otherwise, the result will be a `numpy.ndarray` object with no metadata.
Return type:	`xarray.DataArray` or `numpy.ndarray`