Fengyun-4A (FY-4A), the second generation of China’s geostationary meteorological satellite, provides high spatiotemporal resolution cloud products over East Asia. In this study, cloud fraction (CFR) and cloud top pressure (CTP)… Click to show full abstract
Fengyun-4A (FY-4A), the second generation of China’s geostationary meteorological satellite, provides high spatiotemporal resolution cloud products over East Asia. In this study, cloud fraction (CFR) and cloud top pressure (CTP) products in August 2017 derived from the Advanced Geosynchronous Radiation Imager (AGRI) aboard FY-4A (AGRI/FY-4A) are retrospectively compared with those from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Terra (MODIS/Terra) over East Asia. To avoid possible errors in the comparison caused by the lower temporal coverage of MODIS/Terra products compared to that of AGRI/FY-4A over the same region and to account for time lags between observations of the two instruments, we construct datasets of AGRI/FY-4A CFR and CTP to match those of MODIS/Terra in each scan over East Asia in August 2017. Results show that the CFR and CTP datasets of the two instruments generally agree well, with the linear correlation coefficients (R) between CFR (CTP) data of 0.83 (0.80) regardless of time lags. Though longer time lags contribute to the worse consistency between CFR (CTP) data derived from observations of the two instruments in most cases, large CFR/CTP discrepancies do not always match with long time lags. Large CFR discrepancies appear in the vicinity of the Tibetan Plateau (TP; 28°–45°N, 75°–105°E). These differences in the cloud detection by the two instruments largely occur when MODIS/Terra detects clear-sky while AGRI/FY-4A detects higher values of CFR, and this accounts for 61% of the CFR discrepancy greater than 50% near the TP. In the case of CTP, the largest discrepancies appear in the eastern Iranian Plateau (IP; 25°–45°N, 60°–80°E), where there are some samples with long time lags (20–35 min) and fewer daily data samples are available for computing monthly means compared to other regions since there are many clear-sky data samples there during the study period.
               
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