Bias correction of monthly precipitation and temperature fields from Intergovernmental Panel on Climate Change AR4 models using equidistant quantile matching

By Haibin Li, Justin Sheffield, Eric F. Wood. 2010

885 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/1ba1a8844f7ae70762c2b5d75a2fa832cca95639

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In Atmospheric Science

In Global and Planetary Change

Citations in the corpus

Quantile-based bias correction and uncertainty quantification of extreme event attribution statements (2016, methodology): “The quantile mapping approach of Li et al. (2010) has been previously used to empirically estimate annual and decadal maximum daily precipitation in an attribution study of an early season blizzard in western South Dakota (Edwards et al., 2014).”
An asynchronous regional regression model for statistical downscaling of daily climate variables (2013, neutral): “This is somewhat similar to a bootstrapping approach (Li et al., 2010).”
Bias Correction of GCM Precipitation by Quantile Mapping: How Well Do Methods Preserve Changes in Quantiles and Extremes? (2015, positive): “In appendix A, we showed that the quantile delta/perturbation, QDM, and equidistant/equiratio CDF matching (Li et al. 2010; Wang and Chen 2014) methods are all fundamentally similar.”
A nonstationary bias‐correction technique to remove bias in GCM simulations (2016, methodology): “…bias correction because of the reliability of the observations and the greater likelihood that these observations would resemble future projections [Li et al., 2010] We first compared the performance of the CNCDFm method in the validation period when using parametric and nonparametric estimation…”
Does applying quantile mapping to subsamples improve the bias correction of daily precipitation? (2018, background): “An additional source for sampling uncertainty is the selection of the calibration data (Li et al., 2010; Lafon et al., 2013), which has to be considered as well.”
Equiratio cumulative distribution function matching as an improvement to the equidistant approach in bias correction of precipitation (2014, methodology): “By performing a synthetic experiment, Li et al. (2010) concluded that the EDCDFm method is superior to the traditional method.”
On the bias correction of general circulation model output for Indian summer monsoon (2013, methodology): “Li et al. (2010) also used the same technique for the Intergovernmental Panel on Climate Change AR4.”
Joint bias correction of temperature and precipitation in climate model simulations (2014, methodology): “Commonly used bias correction methods [e.g., Ines and Hansen, 2006; Christensen et al., 2008; Piani et al., 2010a, 2010b; Li et al., 2010; Haerter et al., 2011; Johnson and Sharma, 2012; Grillakis et al., 2013; Hempel et al., 2013] correct variables independently, with variable interactions…”