Reconstruction of Three-Dimensional Cloud Structures Using FY-4B AGRI Observations and CMA-MESO Forecasts

Abstract

Accurate observation of cloud parameters is critical for weather forecasting, climate model improvement, and extreme-weather warning, yet the reconstruction of three-dimensional cloud structures remains a major challenge. In this study, geostationary infrared observations from FY-4B AGRI are combined with CMA-MESO background fields within a Bayesian framework to reconstruct the model-predicted hydrometeor field and subsequently derive the complete three-dimensional cloud structure, characterized by cloud liquid water content, cloud ice water content, and cloud fraction. The incorporation of prior information enhances the reliability and physical interpretability of the reconstructed cloud structure. For a squall line that affected South China on 30 April 2024, the reconstructed cloud field shows closer agreement with observations in both brightness temperature and radar reflectivity than the pre-reconstruction model simulation, yielding a more accurate representation of cloud-field characteristics. A further assimilation experiment demonstrates that introducing the reconstructed hydrometeors into the CMA-MESO initial field brings the spatial distribution of precipitation into closer agreement with observations and improves the threat score for light-to-moderate precipitation. Finally, combining the AGRI-reconstructed cloud field with GIIRS hyperspectral data to extract sub-grid-scale information highlights the value of multi-sensor synergy and establishes a foundation for hyperspectral all-sky data assimilation.