Estimating hourly full-coverage PM over China based on TOA reflectance data from the Fengyun-4A satellite.

It is challenging to retrieve hourly ground-level PM on a national scale in China due to the sparse site measurements and the limited coverage of Low Earth Orbit (LEO) satellite observations. The new geostationary meteorological satellite of China, Fengyun-4A (FY-4A), provides a unique opportunity to fill this gap. In this study, the Random Forest (RF) algorithm was applied to retrieve hourly PM of China directly from FY-4A Top-of-Atmosphere (TOA) reflectance data. A one-year PM retrieval shows a strong agreement to ground-based measurements, with the averaged R approaching 0.92, while the RMSE was only 10.0 μg/m³. An analysis of the regional differences of the performance and the dependency on satellite Viewing Zenith Angle (VZA) show that sparse measurements, high VZA, and solar zenith angle (SZA) are the primary sources of the uncertainty. The use of the FY-4A improved 17% spatial coverage compared to the Himawari-8-based PM retrievals, enabling full-coverage, hourly PM monitoring over China, and potentially could improve PM predictions from air quality models after data assimilation.