Impact of Cloud Vertical Structure Perturbations on the Retrieval of Cloud Optical Thickness and Effective Radius from FY4A/AGRI

Abstract. The vertical structure of clouds plays a critical role in atmospheric radiative transfer processes and is a major source of uncertainty in satellite-based retrievals of cloud optical thickness (COT) and cloud effective radius (CER). This study develops a retrieval model for COT and CER based on a random forest framework coupled with spatial gradient features, using multispectral observations from the FY4A/AGRI (Advanced Geostationary Radiation Imager) and simulations from Advanced Radiative Transfer Modeling System (ARMS) over central and eastern China during June–August 2018. The retrieval results agree well with MODIS, with correlation coefficients of 0.87 and 0.91 for COT and CER, respectively. To assess the impact of vertical cloud structure, ten sensitivity experiments varied water and ice content in different cloud layers. The results indicate that upper-level ice clouds significantly mask reflectance from lower clouds, reducing total reflectance by approximately 50 %, leading to lower retrieved values than those of single-layer clouds. For CER < 20 μm, the mean COT increase due to low- and mid-level water cloud variations in single-layer clouds exceeds that in double-layer clouds by about 24 %, primarily due to the masking effect of upper-level ice clouds in double-layer structures. This masking also contributes to retrieval biases in three-layer cloud systems. Furthermore, increased mid-level liquid water enhances the nonlinear relationship between COT and CER, increasing retrieval uncertainties. This study highlights the importance of considering multi-layer cloud structures in remote sensing algorithms and radiative transfer models.