Multivariate bias correction that matches marginal distributions
using QDM and the Spearman rank correlation
dependence structure following Cannon (2016).
MBCr(o.c, m.c, m.p, iter=20, cor.thresh=1e-4,
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, silent=FALSE, subsample=NULL,
pp.type=7)a list of with elements consisting of:
matrix of bias corrected m.c values for the calibration period.
matrix of bias corrected m.p values for the projection period.
matrix of observed samples during the calibration period.
matrix of model outputs during the calibration period.
matrix of model outputs during the projected period.
maximum number of algorithm iterations.
if greater than zero, a threshold indicating the change in magnitude of Spearman rank correlations required for convergence.
vector of logical values indicating if samples are of a ratio quantity (e.g., precipitation).
numeric values indicating thresholds below which values of a ratio quantity (e.g., ratio=TRUE) should be considered exact zeros.
numeric values of thresholds used internally when handling of exact zeros; defaults to one half of trace.
optional strength of jittering to be applied when quantities are quantized.
number of quantiles used in the quantile mapping; NULL equals the length of the m.p series.
numeric values indicating the maximum proportional changes allowed for ratio quantities below the ratio.max.trace threshold.
numeric values of trace thresholds used to constrain the proportional change in ratio quantities to ratio.max; defaults to ten times trace.
method used to handle ties when calculating ordinal ranks.
logical value indicating if m.c and m.p are outputs from QDM.
logical value indicating if algorithm progress should be reported.
use subsample draws of size n.tau to calculate empirical
quantiles; if NULL, calculate normally.
type of plotting position used in quantile.
Cannon, A.J., 2016. Multivariate bias correction of climate model output: Matching marginal distributions and inter-variable dependence structure. Journal of Climate, 29:7045-7064. doi:10.1175/JCLI-D-15-0679.1
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
QDM, MBCp, MRS, MBCn escore