Multivariate—Intervariable, Spatial, and Temporal—Bias Correction*

By Mathieu Vrac, Petra Friederichs. 2015

206 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/851a2039f76c57118a6df3355beaa6b7deda7e9e

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In Atmospheric Science

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Citations in the corpus

Bias Correction of GCM Precipitation by Quantile Mapping: How Well Do Methods Preserve Changes in Quantiles and Extremes? (2015, unknown): “…that univariate algorithms can be used in a nonlocal context through careful pairing of observed and model grid cells, and further that multi-variate bias corrections often rely on univariate quantile mapping algorithms as a key part of their application (Li et al. 2014; Vrac and Friederichs 2015).”
Direct and component-wise bias correction of multi-variate climate indices: the percentile adjustment function diagnostic tool (2018, methodology): “On the one hand, RCMs present biases in inter-variable correlation fields (Wilcke et al. 2013), thus requiring specific multi-variate bias-adjustment techniques (e.g., Li et al. 2014; Vrac and Friederichs 2015; Cannon 2016) able to reconstruct the intervariable dependences of the observations.”
Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables (2017, methodology): “1 In comparison to the EC-BC method by Vrac and Friederichs (2015), which also corrects the full multivariate distribution within the calibration sample, MBCn preserves much of the underlying content of the climate model within the projection period.”
The effect of univariate bias adjustment on multivariate hazard estimates (2019, methodology): “In recognition of these issues, several multivariate bias-adjustment methods have been suggested in the recent past (Piani and Haerter, 2012; Li et al., 2014; Vrac and Friederichs, 2015; Cannon, 2016; Mehrotra and Sharma, 2016; Vrac, 2018).”
Multivariate stochastic bias corrections with optimal transport (2018, background): “The first type corrects separately each marginal and applies afterwards a correction of the dependence structure (e.g., Vrac and Friederichs, 2015; Vrac, 2018; Nahar et al., 2018; Cannon, 2018).”
Changes in Spatiotemporal Precipitation Patterns in Changing Climate Conditions (2016, background): “Some work has extended bias correction methods to also address biases in spatiotemporal dependence (Vrac and Friederichs 2015), but thesemethods do not allow for future changes in that dependence.”