Improving Short-Range Irradiance Forecast With Assimilating FY-4B All-Sky Radiances Into a Numerical Solar Prediction Model

Under the context of carbon neutrality, solar energy will become one of the primary energy sources in the future. Accurate forecasting of solar energy facilitates the rational allocation and management of solar resources. Cloud is a critical factor affecting solar energy forecasting, and accurate solar energy prediction requires appropriate cloud initialization in numerical weather prediction models. Geostationary satellite infrared (IR) radiances provide abundant cloud and hydrometeor information and play a critical role in solar energy forecasting. Therefore, appropriate assimilation of Fengyun-4B advanced geostationary radiation imager (FY-4B AGRI) IR radiances holds significant potential to improve solar energy forecasts. To more accurately account for cloud impacts on solar irradiance in cloud initialization, this study develops an all-sky assimilation scheme for FY-4B AGRI IR radiances specifically tailored for a numerical solar prediction model (WRF-Solar): first, based on cloud statistical characteristics calculated from discrepancies between WRF-Solar background and FY-4B AGRI observations, an all-sky radiance observation error model applicable to the effective assimilation of AGRI IR radiances is constructed. Subsequently, a cloud-dependent quality control method is developed utilizing this observation error model, which enhances the consistency between cloudy radiances in the background and observations, thereby facilitating accurate extraction of cloud information during the assimilation process. Finally, the cloud-sensitive brightness temperatures (BTs) from FY-4B AGRI IR window channel are incorporated as cloud predictor into the adaptive variational bias correction model, thereby more effectively improving the mitigation of systematic biases caused by the uncertainty of cloud. Based on the constructed cloud initialization model, a continuous 9-day experiment integrating FY-4B AGRI all-sky IR radiance assimilation and solar irradiance forecasting was conducted. The improvement effects on irradiance forecasts were quantitatively evaluated, with the results demonstrating that: the all-sky radiance assimilation effectively incorporates cloud and hydrometeor information from FY-4B AGRI observations in cloud-covered regions, improves BTs simulations in the analysis, and generates substantial hydrometeor analysis increments. The all-sky assimilation of FY-4B AGRI IR radiances significantly reduces global horizontal irradiance forecast errors under cloudy conditions, with particularly pronounced error reductions observed during midday periods. This study provides valuable References for improving solar energy forecasting and enhancing efficiency of clean energy utilization through the comprehensive utilization of new-generation geostationary meteorological satellite data.