wrf.cape_3d

wrf.cape_3d(pres_hpa, tkel, qv, height, terrain, psfc_hpa, ter_follow, missing=<MagicMock name='mock().item()' id='140499233145616'>, meta=True)

Return the three-dimensional CAPE and CIN.

This function calculates the maximum convective available potential energy (CAPE) and maximum convective inhibition (CIN). This function uses the RIP [Read/Interpolate/plot] code to calculate potential energy (CAPE) and convective inhibition (CIN) [J kg-1] for every grid point in the entire 3D domain (treating each grid point as a parcel).

The leftmost dimension of the returned array represents two different quantities:

  • return_val[0,…] will contain CAPE [J kg-1]

  • return_val[1,…] will contain CIN [J kg-1]

This function also supports computing CAPE along a single vertical column. In this mode, the pres_hpa, tkel, qv and height arguments must be one-dimensional vertical columns, and the terrain and psfc_hpa arguments must be scalar values (float, numpy.float32 or numpy.float64).

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 at least three dimensions when operating on a grid of values. The rightmost dimensions can be top_bottom x south_north x west_east or bottom_top x south_north x west_east. When operating on only a single column of values, the vertical column can be bottom_top or top_bottom. In this case, terrain and psfc_hpa must be scalars.

    Note

    The units for pres_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.

  • height (xarray.DataArray or numpy.ndarray) – Geopotential height in [m] with the same dimensionality as pres_hpa.

  • terrain (xarray.DataArray, numpy.ndarray, or a scalar) – Terrain height in [m]. When operating on a grid of values, this argument is at least a two-dimensional array with the same dimensionality as pres_hpa, excluding the vertical (bottom_top/top_bottom) dimension. When operating on a single vertical column, this argument must be a scalar (float, numpy.float32, or numpy.float64).

  • psfc_hpa (xarray.DataArray, numpy.ndarray, or a scalar) –

    Surface pressure in [hPa]. When operating on a grid of values, this argument is at least a two-dimensional array with the same dimensionality as pres_hpa, excluding the vertical (bottom_top/top_bottom) dimension. When operating on a single vertical column, this argument must be a scalar (float, numpy.float32, or numpy.float64).

    Note

    The units for psfc_hpa are [hPa].

  • ter_follow (bool) – A boolean that should be set to True if the data uses terrain following coordinates (WRF data). Set to False for pressure level data.

  • missing (float, optional) – The fill value to use for the output. Default is wrf.default_fill(numpy.float64).

  • meta (bool) – Set to False to disable metadata and return numpy.ndarray instead of xarray.DataArray. Default is True.

Warning

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

Returns:

The CAPE and CIN as an array whose leftmost dimension is 2 (0=CAPE, 1=CIN). 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

See also

wrf.getvar(), wrf.cape_2d()