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Remote Sensing
Volume 17
Issue 14
10.3390/rs17142469
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Article

On the Hybrid Algorithm for Retrieving Day and Night Cloud Base Height from Geostationary Satellite Observations

by
Tingting Ye
1,
Zhonghui Tan
1,*,
Weihua Ai
1,
Shuo Ma
1,
Xianbin Zhao
1,
Shensen Hu
1,
Chao Liu
2,3 and
Jianping Guo
4
1
College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China
2
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China
3
Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, School of Atmospheric Physics, Nanjing University of Information Science & Technology, Nanjing 210044, China
4
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(14), 2469; https://doi.org/10.3390/rs17142469
Submission received: 9 May 2025 / Revised: 2 July 2025 / Accepted: 14 July 2025 / Published: 16 July 2025
(This article belongs to the Special Issue Advances in Sensor Design and Algorithm Development for Cloud Remote Sensing)
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Abstract

Most existing cloud base height (CBH) retrieval algorithms are only applicable for daytime satellite observations due to their dependence on visible observations. This study presents a novel algorithm to retrieve day and night CBH using infrared observations of the geostationary Advanced Himawari Imager (AHI). The algorithm is featured by integrating deep learning techniques with a physical model. The algorithm first utilizes a convolutional neural network-based model to extract cloud top height (CTH) and cloud water path (CWP) from the AHI infrared observations. Then, a physical model is introduced to relate cloud geometric thickness (CGT) to CWP by constructing a look-up table of effective cloud water content (ECWC). Thus, the CBH can be obtained by subtracting CGT from CTH. The results demonstrate good agreement between our AHI CBH retrievals and the spaceborne active remote sensing measurements, with a mean bias of −0.14 ± 1.26 km for CloudSat-CALIPSO observations at daytime and −0.35 ± 1.84 km for EarthCARE measurements at nighttime. Additional validation against ground-based millimeter wave cloud radar (MMCR) measurements further confirms the effectiveness and reliability of the proposed algorithm across varying atmospheric conditions and temporal scales.
Keywords: cloud base height; passive radiometer; active radar and lidar; remote sensing; deep learning cloud base height; passive radiometer; active radar and lidar; remote sensing; deep learning
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MDPI and ACS Style

Ye, T.; Tan, Z.; Ai, W.; Ma, S.; Zhao, X.; Hu, S.; Liu, C.; Guo, J. On the Hybrid Algorithm for Retrieving Day and Night Cloud Base Height from Geostationary Satellite Observations. Remote Sens. 2025, 17, 2469. https://doi.org/10.3390/rs17142469

AMA Style

Ye T, Tan Z, Ai W, Ma S, Zhao X, Hu S, Liu C, Guo J. On the Hybrid Algorithm for Retrieving Day and Night Cloud Base Height from Geostationary Satellite Observations. Remote Sensing. 2025; 17(14):2469. https://doi.org/10.3390/rs17142469

Chicago/Turabian Style

Ye, Tingting, Zhonghui Tan, Weihua Ai, Shuo Ma, Xianbin Zhao, Shensen Hu, Chao Liu, and Jianping Guo. 2025. "On the Hybrid Algorithm for Retrieving Day and Night Cloud Base Height from Geostationary Satellite Observations" Remote Sensing 17, no. 14: 2469. https://doi.org/10.3390/rs17142469

APA Style

Ye, T., Tan, Z., Ai, W., Ma, S., Zhao, X., Hu, S., Liu, C., & Guo, J. (2025). On the Hybrid Algorithm for Retrieving Day and Night Cloud Base Height from Geostationary Satellite Observations. Remote Sensing, 17(14), 2469. https://doi.org/10.3390/rs17142469

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