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Journal of Meteorological Research

, Volume 33, Issue 4, pp 705–719 | Cite as

Comparisons of AGRI/FY-4A Cloud Fraction and Cloud Top Pressure with MODIS/Terra Measurements over East Asia

  • Tao Wang
  • Jiali LuoEmail author
  • Jinglin Liang
  • Baojian Wang
  • Wenshou Tian
  • Xiaoyan Chen
Regular Article
  • 5 Downloads

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.

Key words

AGRI/FY-4A MODIS/Terra cloud fraction cloud top pressure 

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Notes

Acknowledgments

We thank Zhe Xu, Xiaohu Zhang, Xi Wang, and Fu Wang from the National Satellite Meteorological Center (NSMC) of China Meteorological Administration for their guidance on the FY-4A cloud products. We also thank the NSMC for providing FY-4A datasets and the MODIS Atmosphere Group for providing MODIS datasets.

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Copyright information

© The Chinese Meteorological Society and Springer-Verlag Berlin Heidelberg 2019

Authors and Affiliations

  • Tao Wang
    • 1
  • Jiali Luo
    • 1
    Email author
  • Jinglin Liang
    • 1
  • Baojian Wang
    • 2
  • Wenshou Tian
    • 1
  • Xiaoyan Chen
    • 2
  1. 1.Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric SciencesLanzhou UniversityLanzhouChina
  2. 2.Gansu Meteorological BureauLanzhouChina

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