Combining FY‐4B Infrared Window Channel's Cloud Analysis With Water Vapor Radiance Assimilation for the Prediction of Solar Irradiance

Abstract In the context of the energy crisis and dual‐carbon strategy, solar energy emerges as a highly viable resource. Appropriate cloud initialization is crucial for accurate solar irradiance forecasting. The water vapor channels and infrared window channels of Fengyun‐4B Advanced Geostationary Radiation Imager (FY‐4B AGRI) provide cloud information with different characteristics, respectively. Effectively utilizing these data to improve the accuracy of cloud initial fields and solar irradiance forecasting holds significant research value. In this study, cloud information from FY‐4B AGRI infrared window channels was successfully assimilated into a numerical solar prediction model using a cloud analysis scheme, with hydrometeor species in the cloud analysis were extended to five types (cloud water, ice, rain, snow, and graupel) to better extract cloud information. Subsequently, the hydrometeor‐augmentation cloud analysis was applied to provide more reasonable background for all‐sky radiance assimilation of water vapor channels, enabling synergistic assimilation of cloud information from infrared window and water vapor channels. Evaluation of solar irradiance forecasting showed that assimilating infrared window channels cloud information clearly reduced forecast errors of irradiance. The hydrometeor‐augmentation cloud analysis, by providing richer cloud information for solar irradiance forecasting, achieved notable improvements. Furthermore, the synergistic assimilation of infrared window and water vapor channels cloud information further enhanced the cloud initial fields, leading to a reduction in solar irradiance forecast errors of up to 50% relative to the no‐assimilation experiment. This study offers a reference for achieving the better utilization of multi‐channel cloud information from FY‐4 satellites, thereby improving solar energy forecasting.