Calibration of cloud and aerosol related parameters for solar irradiance forecasts in WRF-solar. (15th July 2022)

• Record Type: Journal Article
• Authors: Liu, Ye; Qian, Yun; Feng, Sha; Berg, Larry K.; Juliano, Timothy W.; Jiménez, Pedro A.; Grimit, Eric; Liu, Ying
• Is Part Of: Solar energy. Volume 241(2022)
• Page Start: 1
• Page End: 12
• Publication Date: 2022-07-15
• Subjects: Solar energy -- Periodicals; Solar engines -- Periodicals; 621.47
• DOI: 10.1016/j.solener.2022.05.064
• Languages: English
• ISSNs: 0038-092X

Abstract

Highlights: Surrogate-based optimization improves irradiance forecasting skills up to 33% Calibrated cloud parameters alone improve skill for cloudy, less-polluted condition. Calibrated aerosol parameters further improve skill during wildfire events. Abstract: Model parameters are one of the sources of uncertainties in numerical weather prediction. Recently, the Weather Research and Forecasting model with Solar extensions (WRF-Solar) has been upgraded by enhancing the treatment of sub-grid scale cloud and aerosols with augmentations of a sub-grid scale cloud scheme (CLD3) and an upgraded aerosol-aware Thompson-Eidhammer scheme (TE14). However, the value of model parameters associated with these parameterizations are assigned based on limited measurements or theoretical calculations. Calibrating the sensitive parameters has the potential to improve solar irradiance predictions. In this work, we adopted a multi-objective surrogate-based optimization (SBO) framework to calibrate nine parameters used in CLD3 and TE14 that lead to the largest sensitivity in simulated irradiance. The normalized mean-absolute-error ( N M A E ) of global horizontal irradiance (GHI) and direct normal irradiance (DNI) are minimized by calibrating WRF-Solar over two regions including the Southern Great Plains (SGP) and Central California. We selected two cloudy cases, one over less-polluted SGP and another over Central California with high aerosol loading associated with wildfire events. The results show that generalized linear model (GLM)-based surrogate models approximate physical models well, particularly when the third order and three-way interaction terms are considered. The SBO framework efficiently searches the parameter space for optimal solutions with less computational costs than directly calibrating the physical model. We first calibrate CLD3 parameters over the less-polluted SGP region. Optimized CLD3 parameters alone result in N M A E reduction by 14% for the site-mean and up to 33% for individual cases over the SGP region. With further calibration of TE14 parameters over the Central California during active fire periods, the optimized parameters lead to over 20% reductions of N M A E .