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

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

doi:https://doi.org/10.5194/amt-17-3583-2024

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

Data sets

Data Products CloudSat Data Processing Center https://www.cloudsat.cira.colostate.edu/data-products

CALIPSO Lidar Level 2 1 km Cloud Layer, V4-51 D. Winker https://doi.org/10.5067/CALIOP/CALIPSO/CAL_LID_L2_01kmCLay-Standard-V4-51

Model code and software

Random Forest Cloud Model for Predicting Liquid Cloud Microphysical Properties from A-Train Data Richard Schulte https://doi.org/10.5281/zenodo.10425919

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.