gpqm: Generalized Quantile Mapping method for bias correction

In SantanderMetGroup/downscaleR: A climate4R package for statistical downscaling <http://meteo.unican.es/climate4r>

Generalized Quantile Mapping method for bias correction

Description

Implementation of Generalized Quantile Mapping method for bias correction

Usage

gpqm(o, p, s, precip, pr.threshold, theta)

Arguments

o	A vector (e.g. station data) containing the observed climate data for the training period
p	A vector containing the simulated climate by the model for the training period.
s	A vector containing the simulated climate for the variable used in p, but considering the test period.
precip	Logical for precipitation data. If TRUE Adjusts precipitation frequency in 'x' (prediction) to the observed frequency in 'y'. This is a preprocess to bias correct precipitation data following Themeßl et al. (2012). To adjust the frequency, parameter pr.threshold is used (see below).
pr.threshold	The minimum value that is considered as a non-zero precipitation. Ignored when precip = FALSE. See details in function biasCorrection.
theta	numeric indicating upper threshold (and lower for the left tail of the distributions, if needed) above which precipitation (temperature) values are fitted to a Generalized Pareto Distribution (GPD). Values below this threshold are fitted to a gamma (normal) distribution. By default, 'theta' is the 95th percentile (5th percentile for the left tail).

Author(s)

S. Herrera and M. Iturbide

SantanderMetGroup/downscaleR documentation built on July 4, 2023, 4:28 a.m.