geocat.comp.meteorology.heat_index

geocat.comp.meteorology.heat_index(temperature, relative_humidity, alternate_coeffs=False)

Compute the ‘heat index’ as calculated by the National Weather Service.

The heat index calculation in this function is described at: https://www.wpc.ncep.noaa.gov/html/heatindex_equation.shtml

The ‘Heat Index’ is a measure of how hot weather “feels” to the body. The combination of temperature and humidity produce an “apparent temperature” or the temperature the body “feels”. The returned values are for shady locations only. Exposure to full sunshine can increase heat index values by up to 15°F. Also, strong winds, particularly with very hot, dry air, can be extremely hazardous as the wind adds heat to the body

The computation of the heat index is a refinement of a result obtained by multiple regression analysis carried out by Lans P. Rothfusz and described in a 1990 National Weather Service (NWS) Technical Attachment (SR 90-23). All values less that 40F/4.4C/277.65K are set to the ambient temperature.

In practice, the Steadman formula is computed first and the result averaged with the temperature. If this heat index value is 80 degrees F or higher, the full regression equation along with any adjustment as described above is applied. If the ambient temperature is less the 40F (4.4C/277.65K), the heat index is set to the ambient temperature.

Parameters:

• temperature (numpy.ndarray, xarray.DataArray, list, float) – temperature(s) in Fahrenheit

• relative_humidity (numpy.ndarray, xarray.DataArray, list, float) – relative humidity as a percentage. Must be the same shape as temperature

• alternate_coeffs (bool, optional) – flag to use alternate set of coefficients appropriate for temperatures from 70F to 115F and humidities between 0% and 80%

Returns:

heatindex (numpy.ndarray, xarray.DataArray) – Calculated heat index. Same shape as temperature

Examples

>>> import numpy as np
>>> import geocat.comp
>>> t = np.array([104, 100, 92])
>>> rh = np.array([55, 65, 60])
>>> hi = heat_index(t,rh)
>>> hi
array([137.36135724, 135.8679973 , 104.68441864])

See also

Related GeoCAT Functions: _heat_index() _xheat_index()

Related NCL Functions: heat_index_nws