An Extrapolation Method for Estimating Overlapping Cloud Base Height From Passive Radiometers

While a variety of methods have been developed for estimating single-layer cloud base height (CBH), few studies have been introduced for retrieving overlapping CBH. To enhance the characterization of the vertical structure of overlapping clouds, which account for approximately a quarter of global cl...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2024, Vol.62, p.1-11
Main Authors: Tan, Zhonghui, Liu, Chao, Li, Yi, Ma, Shuo, Ye, Tingting, Li, Bo, Teng, Shiwen, Ai, Weihua
Format: Article
Language:English
Subjects:
Algorithms
Atmospheric measurements
Climate models
Cloud base height (CBH)
Cloud computing
Clouds
Extrapolation
Height
Ice clouds
Instruments
Lidar
Microwave imagery
MODIS
overlapping clouds
passive radiometer
Radar
Radar imaging
Radiative forcing
Radiometers
Radiometry
Satellite broadcasting
Spatial resolution
Spectroradiometers
Structural analysis
Table lookup
Vertical profiles
Weather modification
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Summary:While a variety of methods have been developed for estimating single-layer cloud base height (CBH), few studies have been introduced for retrieving overlapping CBH. To enhance the characterization of the vertical structure of overlapping clouds, which account for approximately a quarter of global clouds, this study presents an extrapolation algorithm for estimating overlapping CBH from passive radiometers. The algorithm relies on the continuity of cloud boundaries within a given region and develops four tests to identify appropriate single-layer cloud pixels for accurately inferring overlapping CBHs. The algorithm was applied to data from the aqua moderate-resolution imaging spectroradiometer (MODIS), and the results were validated against active cloud profiling radar (CPR)-cloud-aerosol Lidar with orthogonal polarization (CALIOP) measurements. The results indicate that the CBH retrievals derived from a single-layer cloud assumption are significantly biased in overlapping cloud cases. In contrast, the extrapolation algorithm provides more accurate retrievals of both upper layer ice CBH and lower layer water CBH. Specifically, the mean CBH bias for upper layer ice clouds is reduced from −2.4 to −0.9 km, while for lower layer water clouds, it is reduced from 3.8 to 1.6 km. By accurately extracting the vertical structure of overlapping clouds, this approach shows potential for improving cloud radiative forcing estimates, weather modification, and climate modeling.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2024.3491165