Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 17, issue 11
Articles | Volume 17, issue 11
https://doi.org/10.5194/amt-17-3583-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-17-3583-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 11
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

Viewed

Total article views: 2,075 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,558 445 72 2,075 83 97
  • HTML: 1,558
  • PDF: 445
  • XML: 72
  • Total: 2,075
  • BibTeX: 83
  • EndNote: 97
Views and downloads (calculated since 03 Jan 2024)
Cumulative views and downloads (calculated since 03 Jan 2024)

Viewed (geographical distribution)

Total article views: 2,075 (including HTML, PDF, and XML) Thereof 2,044 with geography defined and 31 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 06 Dec 2025
Download
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.
Read more
Share