MBCn: Multivariate bias correction (N-pdft)

In MBC: Multivariate Bias Correction of Climate Model Outputs

Multivariate bias correction (N-pdft)

Description

Multivariate bias correction that matches the multivariate distribution using QDM and the N-dimensional probability density function transform (N-pdft) following Cannon (2018).

Usage

MBCn(o.c, m.c, m.p, iter=30, ratio.seq=rep(FALSE, ncol(o.c)),
     trace=0.05, trace.calc=0.5*trace, jitter.factor=0,
     n.tau=NULL, ratio.max=2, ratio.max.trace=10*trace,
     ties='first', qmap.precalc=FALSE, rot.seq=NULL,
     silent=FALSE, n.escore=0, return.all=FALSE, subsample=NULL,
     pp.type=7)

Arguments

o.c	matrix of observed samples during the calibration period.
m.c	matrix of model outputs during the calibration period.
m.p	matrix of model outputs during the projected period.
iter	maximum number of algorithm iterations.
ratio.seq	vector of logical values indicating if samples are of a ratio quantity (e.g., precipitation).
trace	numeric values indicating thresholds below which values of a ratio quantity (e.g., ratio=TRUE) should be considered exact zeros.
trace.calc	numeric values of thresholds used internally when handling of exact zeros; defaults to one half of trace.
jitter.factor	optional strength of jittering to be applied when quantities are quantized.
n.tau	number of quantiles used in the quantile mapping; NULL equals the length of the m.p series.
ratio.max	numeric values indicating the maximum proportional changes allowed for ratio quantities below the ratio.max.trace threshold.
ratio.max.trace	numeric values of trace thresholds used to constrain the proportional change in ratio quantities to ratio.max; defaults to ten times trace.
ties	method used to handle ties when calculating ordinal ranks.
qmap.precalc	logical value indicating if m.c and m.p are outputs from QDM.
rot.seq	use a supplied list of random rotation matrices. NULL generates on the fly.
silent	logical value indicating if algorithm progress should be reported.
n.escore	number of cases used to calculate the energy distance when monitoring convergence.
return.all	logical value indicating whether results from all iterations are returned.
subsample	use subsample draws of size n.tau to calculate initial empirical quantiles; if NULL, calculate normally.
pp.type	type of plotting position used in quantile.

Value

a list of with elements consisting of:

mhat.c	matrix of bias corrected m.c values for the calibration period.
mhat.p	matrix of bias corrected m.p values for the projection period.

References

Cannon, A.J., 2018. Multivariate quantile mapping bias correction: An N-dimensional probability density function transform for climate model simulations of multiple variables. Climate Dynamics, 50(1-2):31-49. doi:10.1007/s00382-017-3580-6

Cannon, A.J., S.R. Sobie, and T.Q. Murdock, 2015. Bias correction of simulated precipitation by quantile mapping: How well do methods preserve relative changes in quantiles and extremes? Journal of Climate, 28:6938-6959. doi:10.1175/JCLI-D-14-00754.1

Pitié, F., A.C. Kokaram, and R. Dahyot, 2005. N-dimensional probability density function transfer and its application to color transfer. In Tenth IEEE International Conference on Computer Vision, 2005. ICCV 2005. (Vol. 2, pp. 1434-1439). IEEE.

Pitié, F., A.C. Kokaram, and R. Dahyot, 2007. Automated colour grading using colour distribution transfer. Computer Vision and Image Understanding, 107(1):123-137.

See Also

QDM, MBCp, MBCr, MRS, escore, rot.random

MBC documentation built on May 3, 2023, 1:16 a.m.