Remote Sensing (Nov 2022)

Estimating Layered Cloud Cover from Geostationary Satellite Radiometric Measurements: A Novel Method and Its Application

  • Zhonghui Tan,
  • Shuo Ma,
  • Xin Wang,
  • Yudi Liu,
  • Weihua Ai,
  • Wei Yan

DOI
https://doi.org/10.3390/rs14225693
Journal volume & issue
Vol. 14, no. 22
p. 5693

Abstract

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Layered cloud cover (LCC), that is, cloud cover at different levels, is crucial for estimating cloud radiative effects and modeling climate change. However, accurate LCC characterization using passive satellite measurements is challenging because of the difficulties in resolving cloud vertical structures. In this study, we developed a novel method to estimate LCC from geostationary satellite radiometric measurements. The proposed method resolves cloud vertical structures by retrieving cloud-top and cloud-base heights for both single- and multi-layer clouds; thus, better estimating LCC. Our results agreed well with active satellite measurements, showing identification accuracies of 86%, 90%, and 91% for high, medium, and low clouds, respectively. Additionally, our LCC estimates derived from satellite measurements were used to evaluate those from atmospheric reanalysis. The annual averaged total, high, medium, and low cloud covers given by our methods were 0.681, 0.393, 0.356, and 0.455, respectively, while those from ERA-5 were 0.623, 0.415, 0.274, and 0.392, respectively. These results indicate that the total cloud cover determined by ERA-5 was lower than that derived from satellite measurements, potentially as a result of medium and low-level clouds.

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