MBCn: 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)

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.

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.

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.

Description

Usage

Value

Arguments

References

See Also