Assessment of Geostationary Satellite Cloud Effective Radius over East China with In-Situ Aircraft Measurements

Cloud effective radius (CER) is a fundamental microphysical property of clouds, critical for understanding cloud formation and radiative effects. Satellite spectral imagers, widely utilized in passive remote sensing, facilitate the monitoring of cloud characteristics, including CER, over extended time periods and spatial scales. Various observational methods have been employed to evaluate satellite cloud property products; however, in-situ measurement evaluations of CER remain limited, particularly for products over China. This study utilized aircraft observations provided by the China Meteorological Administration Weather Modification Center to evaluate the CER retrieved by Fengyun-4A AGRI and Himawari-8 AHI. Three cases were selected from the aircraft dataset for evaluation, characterized by flights through non-precipitating stratiform clouds and stable and high-quality measurements. Rigorous data selection and collocation procedures were employed to ensure a comprehensive comparison. Satellite retrievals from heterogeneous cloud fields were excluded, and representative in-cloud aircraft measurements were identified through multi-parameter filtering. The flight trajectory was adjusted to account for horizontal cloud movement corresponding to time differences between observations from different platforms. Additionally, in-situ measurements from different vertical layers were adjusted to a comparable position near the cloud top. Results indicate that CER retrieved from satellites is generally overestimated compared to in-situ measurements. For AGRI, the average difference (AD) is 2.90 µm, with a root mean square difference (RMSD) of 3.53 µm. For AHI, the AD is 2.92 µm, and the RMSD is 3.59 µm. To enhance future validation and evaluation of remote sensing results, factors such as instrument calibration, flight patterns, and cloud conditions will be carefully considered. Increasing the number of cases will further reduce errors associated with individual instances, enabling more precise assessments.