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

Adler, R. F. and Negri, A. J.: A Satellite Infrared Technique to Estimate Tropical Convective and Stratiform Rainfall, J. Appl. Meteorol. Clim., 27, 30–51, https://doi.org/10.1175/1520-0450(1988)027<0030:ASITTE>2.0.CO;2, 1988. 
Aha, D. W. and Bankert, R. L.: A Comparative Evaluation of Sequential Feature Selection Algorithms, in: Learning from Data: Artificial Intelligence and Statistics V, edited by: Fisher, D. and Lenz, H.-J., Springer, New York, NY, 199–206, https://doi.org/10.1007/978-1-4612-2404-4_19, 1996. 
Austin, R. T., Heymsfield, A. J., and Stephens, G. L.: Retrieval of ice cloud microphysical parameters using the CloudSat millimeter-wave radar and temperature, J. Geophys. Res.-Atmos., 114, D00A23, https://doi.org/10.1029/2008JD010049, 2009.​​​​​​​ 
Baba, K., Shibata, R., and Sibuya, M.: Partial Correlation and Conditional Correlation as Measures of Conditional Independence, Aust. N. Z. J. Stat., 46, 657–664, https://doi.org/10.1111/j.1467-842X.2004.00360.x, 2004. 
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001. 
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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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