Impact of cloud vertical structure perturbations on the retrieval of cloud optical thickness and effective radius from FY4A/AGRI

The vertical structure of clouds plays a critical role in atmospheric radiative transfer and is a major source of uncertainty in satellite-based retrievals of cloud optical thickness (COT) and cloud effective radius (CER). Most operational algorithms assume a single homogeneous layer, but the biases introduced by this simplification under realistic multilayer conditions remain poorly quantified. This study systematically investigates how perturbations in cloud vertical structure affect COT and CER retrievals using Fengyun-4A Advanced Geostationary Radiation Imager (FY4A/AGRI) and simulations from Advanced Radiative Transfer Modeling System (ARMS) over central and eastern China during June–August 2018. We designed ten sensitivity experiments by varying water and ice content across single-, double-, and triple-layer cloud configurations to quantify the impact of structural differences on channel reflectance and the COT-CER relationship. The results indicate that upper-level ice clouds significantly mask reflectance from lower water clouds, reducing total reflectance by approximately 50 % and leading to systematic retrieval biases: single-layer algorithms underestimate COT at small CER (< 10 µm) but overestimate it by approximately 20 units under larger CER conditions. In single-layer clouds, variations in low- and mid-level water content produce mean COT increases that are ∼ 24 % larger than in double-layer structure, with similar biases occurring in three-layer clouds. Furthermore, enhanced mid-level liquid water enhances the nonlinear relationship between COT and CER, increasing retrieval uncertainties. These findings identify cloud vertical heterogeneity as a major source of retrieval bias and emphasize the necessity of integrating multilayer cloud information into satellite retrieval algorithms.