Adapting Quantile Mapping to Bias Correct Solar Radiation Data

TL;DR

This paper presents an adapted quantile mapping method for bias correction of solar radiation data, ensuring physical plausibility and analyzing residual biases across climate regions using real-world data.

Contribution

It introduces a physically plausible bias correction technique for solar radiation data using quantile mapping combined with clearsky GHI measurements, and analyzes residual biases regionally.

Findings

Effective bias correction of GHI data demonstrated.

Residual biases vary by climate region.

Method validated against real solar radiation data.

Abstract

Bias correction is a common pre-processing step applied to climate model data before it is 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 is physically plausible through incorporating measurements of clearsky 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 considered biases by climate region. This analysis is applied to four sets of climate model output from NA-CORDEX and compared against data from the National Solar Radiation Database produced by the National Renewable Energy Lab.