pysteps.postprocessing.probmatching.shift_scale

pysteps.postprocessing.probmatching.shift_scale(R, f, rain_fraction_trg, second_moment_trg, **kwargs)

Find shift and scale that is needed to return the required second_moment and rain area. The optimization is performed with the Nelder-Mead algorithm available in scipy. It assumes a forward transformation ln_rain = ln(rain)-ln(min_rain) if rain > min_rain, else 0.

Parameters:

R : array_like

The initial array to be shift and scaled.

f : function

The inverse transformation that is applied after the shift and scale.

rain_fraction_trg : float

The required rain fraction to be matched by shifting.

second_moment_trg : float

The required second moment to be matched by scaling. The second_moment is defined as second_moment = var + mean^2.

Returns:

shift : float

The shift value that produces the required rain fraction.

scale : float

The scale value that produces the required second_moment.

R : array_like

The shifted, scaled and back-transformed array.

Other Parameters:  

scale : float

Optional initial value of the scale parameter for the Nelder-Mead optimisation. Typically, this would be the scale parameter estimated the previous time step. Default : 1.

max_iterations : int

Maximum allowed number of iterations and function evaluations. More details: https://docs.scipy.org/doc/scipy/reference/optimize.minimize-neldermead.html Deafult: 100.

tol : float

Tolerance for termination. More details: https://docs.scipy.org/doc/scipy/reference/optimize.minimize-neldermead.html Default: 0.05*second_moment_trg, i.e. terminate the search if the error is less than 5% since the second moment is a bit unstable.