Machine-learning-based approach for solar radiation model uncertainty identification, attribution and bias correction

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Original abstract

Abstract. Accurate surface solar irradiance (SSI) is essential for climate monitoring and solar energy applications, yet operational radiation data products such as the Copernicus Atmospheric Monitoring Service (CAMS) solar radiation service (CRS) still exhibit systematic and situation dependent errors. These are arising from uncertainties in clouds, aerosols and surface properties as well as from area-time mismatch between ground observations and pixel or model gridbox averaged properties. In this study, we develop a data-driven XGBoost-based uncertainty model that predicts the instantaneous CRS irradiance uncertainty for global horizontal (GHI), diffuse horizontal (DHI) and beam normal irradiance (BNI) using only the operational CRS inputs. We apply SHapley Additive exPlanations (SHAP) to quantify the contribution of individual physical predictors and to diagnose the dominant relations of observed deviations to CRS input parameters. Across all components, cloud optical depth is identified as the primary driver of CRS irradiance uncertainty. Aerosol optical depths of different aerosol components and surface reflectance (albedo and BRDF parameters) have additional component-dependent influences, particularly for DHI and BNI. The SHAP analysis also reveals a solar zenith angle dependence with contributions increasing at high solar zenith angles (SZA). A single-case analysis under overcast conditions demonstrates how SHAP can attribute individual large errors to specific cloud, aerosol and surface processes. Finally, we apply the trained situation-dependent model as a post-processing bias correction to the CRS irradiances. The bias correction reduces the median bias from 5.0 to −0.6 Wm-2 for GHI and from 11.1 to 1.0 Wm-2 for DHI. The bias correction improves the root-mean-squared errors and correlation coefficients for all components GHI, DHI, and BNI. The results demonstrate that physically interpretable machine-learning methods can both identify the dominant irradiance deviations based on operationally available CRS input parameters and provide an effective path for a post-processed bias correction.

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