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

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Complicated and regionally representative diurnal cycle characteristics of clouds may introduce some errors in the cloud mask (CLM) algorithm of the Geostationary (GEO) meteorological satellite imaging system, which are very… Click to show full abstract

Complicated and regionally representative diurnal cycle characteristics of clouds may introduce some errors in the cloud mask (CLM) algorithm of the 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 the 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 25 ground-based stations in China. The results indicate that the CCs calculated by the FY-4A/AGRI CLM 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 CCs 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. The CC results retrieved at the vegetation surface by FY-4A/AGRI, however, show the best and stable performance. Because of that, the two independent cloud tests induce most of the overestimations, and 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 $3.8\mu \text{m}$ band. Many extremely overestimated CC samples (about 56.3%) are eliminated. After that, the FY-4A/AGRI CLM product is more reasonable compared with the corresponding infrared and visible imageries.

Keywords: cycle; clm algorithm; satellite; diurnal cycle; cloud covers

Journal Title: IEEE Transactions on Geoscience and Remote Sensing
Year Published: 2023

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