Published December 22, 2023 | Version v1

Random Forest Cloud Model for Predicting Liquid Cloud Microphysical Properties from A-Train Data

Authors/Creators

  • 1. ROR icon Colorado State University

Description

Code for creating and analyzing the performance of a random forest model to predict cloud optical depth and cloud top effective radius from A-train satellite observations. Because of storage limitations, this directory does not include full satellite dataset, but the CloudSat data is available from the CloudSat Data Processing center (https://www.cloudsat.cira.colostate.edu/) and the CALIPSO data from NASA's Atmospheric Science Data Center (https://asdc.larc.nasa.gov/project/CALIPSO). 

Files

Random_Forest_Model.zip

Files (5.1 GB)

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md5:1c018af3ad51ac48a6c21e47a7218655
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