The Impact of Stochastic Perturbations in Physics Variables for Predicting Surface Solar Irradiance

• Published in: Atmosphere Vol. 13; no. 11; p. 1932
• Main Authors: Kim, Ju-Hye, Jiménez, Pedro A., Sengupta, Manajit, Dudhia, Jimy, Yang, Jaemo, Alessandrini, Stefano
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2022; MDPI
• Subjects: Aerosols; Backscatter; Backscattering; Clouds; ensemble forecast; Ensemble forecasting; Experiments; Irradiance; Kinetic energy; National Solar Radiation Database (NSRDB); Perturbation; Perturbation (Astronomy); Perturbations; Physics; Predictions; Probability theory; Radiation; SOLAR ENERGY; Solar irradiance; Solar radiation; stochastic perturbation; Stochasticity; Variables; Weather forecasting; weather research and forecasting (WRF) solar; United States
• ISSN: 2073-4433; 2073-4433
• DOI: 10.3390/atmos13111932

We present a probabilistic framework tailored for solar energy applications referred to as the Weather Research and Forecasting-Solar ensemble prediction system (WRF-Solar EPS). WRF-Solar EPS has been developed by introducing stochastic perturbations into the most relevant physical variables for solar irradiance predictions. In this study, we comprehensively discuss the impact of the stochastic perturbations of WRF-Solar EPS on solar irradiance forecasting compared to a deterministic WRF-Solar prediction (WRF-Solar DET), a stochastic ensemble using the stochastic kinetic energy backscatter scheme (SKEBS), and a WRF-Solar multi-physics ensemble (WRF-Solar PHYS). The performances of the four forecasts are evaluated using irradiance retrievals from the National Solar Radiation Database (NSRDB) over the contiguous United States. We focus on the predictability of the day-ahead solar irradiance forecasts during the year of 2018. The results show that the ensemble forecasts improve the quality of the forecasts, compared to the deterministic prediction system, by accounting for the uncertainty derived by the ensemble members. However, the three ensemble systems are under-dispersive, producing unreliable and overconfident forecasts due to a lack of calibration. In particular, WRF-Solar EPS produces less optically thick clouds than the other forecasts, which explains the larger positive bias in WRF-Solar EPS (31.7 W/m2) than in the other models (22.7–23.6 W/m2). This study confirms that the WRF-Solar EPS reduced the forecast error by 7.5% in terms of the mean absolute error (MAE) compared to WRF-Solar DET, and provides in-depth comparisons of forecast abilities with the conventional scientific probabilistic approaches (i.e., SKEBS and a multi-physics ensemble). Guidelines for improving the performance of WRF-Solar EPS in the future are provided.