Assessment on the Diurnal Cycle of Cloud Covers of Fengyun-4A Geostationary Satellite Based on the Manual Observation Data in China

• Published in: IEEE transactions on geoscience and remote sensing Vol. 61; p. 1
• Main Authors: Liang, Yongen, Min, Min, Yu, Yu, Wang, Xi, Xia, Pan
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Cloud cover; Cloud mask; Clouds; Diurnal; Diurnal variations; Fengyun-4 geostationary meteorological satellite; Ground stations; Manual observation; Meteorological satellites; Polar orbiting satellites; Satellite imagery; Synchronous satellites; China
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2023.3256365

Complicated and regionally representative diurnal cycle characteristics of clouds may introduce some errors into the cloud mask (CLM) algorithm of Geostationary (GEO) meteorological satellite imaging system, which are very difficult to be assessed by using analogous products of fixed-passing polar-orbiting satellites. In this investigation, the diurnal cycle of the performance of CLM algorithm of the Advanced Geosynchronous Radiation Imager onboard the China Fengyun-4A satellite (FY-4A/AGRI) is validated by using manually observed cloud covers (CC) at twenty-five ground-based stations in China. The results indicate that the CCs calculated by the FY-4A/AGRI cloud mask algorithm are overestimated at 11:00 BJT (Beijing Time) and 14:00 BJT (around noon) and underestimated at 08:00 BJT and 20:00 BJT (in the morning and evening) at most stations. In summer, compared with other seasons, the cloud covers obtained from the FY-4A/AGRI over northern China and the Tibetan Plateau are much better consistent with the manual observations, but the situation is the opposite in southern China. However, the CC results retrieved at vegetation surface by FY-4A/AGRI show the best and stable performance. Due to that the two independent cloud tests induce most of overestimations, some sensitivity experiments for the CLM algorithm are conducted. The results show that the best improvement effect is achieved after only closing one cloud test using the 3.8 μm band. Many extremely overestimated cloud cover samples (about 56.3%) are eliminated. After that, the FY-4A/AGRI cloud mask product is more reasonable compared with the corresponding infrared and visible imageries.