Articles | Volume 17, issue 11
https://doi.org/10.5194/amt-17-3583-2024
https://doi.org/10.5194/amt-17-3583-2024
Research article
 | 
13 Jun 2024
Research article |  | 13 Jun 2024

A random forest algorithm for the prediction of cloud liquid water content from combined CloudSat–CALIPSO observations

Richard M. Schulte, Matthew D. Lebsock, John M. Haynes, and Yongxiang Hu

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Cited articles

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Short summary
This paper describes a method to improve the detection of liquid clouds that are easily missed by the CloudSat satellite radar. To address this, we use machine learning techniques to estimate cloud properties (optical depth and droplet size) based on other satellite measurements. The results are compared with data from the MODIS instrument on the Aqua satellite, showing good correlations.
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