Adapting quantile mapping to bias correct solar radiation data

Abstract

Bias correction is a common preprocessing step applied to climate model data before they are used for further analysis. This article introduces an efficient adaptation of a well-established bias correction method, quantile mapping, for global horizontal irradiance (GHI) that ensures corrected data are physically plausible through incorporating measurements of clear-sky GHI. The proposed quantile mapping method is fit on reanalysis data to first bias correct for regional climate models (RCMs) and is tested on RCMs forced by general circulation models (GCMs) to understand existing biases directly from GCMs. Additionally, we adapt a functional analysis of variance methodology that analyzes sources of remaining biases after implementing the proposed quantile mapping method and consider biases by climate region. The proposed method is able to correct for biases due to seasonality on a monthly time scale as well as produce physically plausible values in the corrected data when compared to observed GHI. Analysis shows that biases from GCMs are generally not geographically specific and that RCMs may contribute strong biases to GHI that need to be corrected for. This analysis is applied to four sets of climate model output from NA-CORDEX and compared against data from the National Solar Radiation Database (NSRDB) produced by the National Renewable Energy Laboratory.