Diurnal Evaluation of AOD-Based Surface Visibility Estimation Using FY-4B Geostationary Observations

Satellite-AOD-based visibility estimation has largely focused on daily-scale estimates or limited overpass times, while its diurnal variations are less understood and discussed. This study therefore investigates surface visibility estimations over China during daytime at 00, 03, 06, and 09 UTC (i.e., 08:00, 11:00, 14:00, and 17:00 China Standard Time, UTC+8), using aerosol optical depth (AOD) observations from the geostationary FY-4B satellite and ERA5 meteorological data. A physically informed framework combines a first-order physical visibility estimate based on AOD, an empirically calibrated relative humidity (RH) adjustment, and XGBoost residual learning. The complete framework, evaluated against ground-based NCEI visibility observations, achieved R = 0.84, RMSE = 2.80 km, and MAE = 2.28 km under sample-based five-fold cross-validation. Independent validation from July 2024 to June 2025 yielded R = 0.79, RMSE = 3.10 km, and MAE = 2.56 km. Visibility-estimation accuracy varied across daytime observation times, with relatively lower errors at 00 and 03 UTC and the largest errors at 06 UTC in both the first-order physical estimate and the final model. Although the complete framework yielded RMSE values of 2.63–3.68 km across the four time slots, these diurnal differences remained evident. The physical estimate also exhibited RH-dependent bias, overestimating visibility at lower RH and underestimating it at higher RH, with a transition near 50%. An empirical RH-conditioned adjustment further reduced estimation errors. These findings highlight the importance of accounting for diurnal and RH-dependent errors in simplified AOD-to-visibility conversion and demonstrate the potential of repeated geostationary observations for daytime surface visibility estimation.