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.