Machine Learning Approach to Investigating the Relative Importance of Meteorological and Aerosol-Related Parameters in Determining Cloud Microphysical Properties

Machine Learning Approach to Investigating the Relative Importance of Meteorological and Aerosol-Related Parameters in Determining Cloud Microphysical Properties

Aerosol effects on cloud properties are notoriously difficult to disentangle from variations driven by meteorological factors. Here, a machine learning model is trained on reanalysis data and satellite retrievals to predict cloud microphysical properties, as a way to illustrate the relative importan...

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Bibliographic Details
Main Authors: Frida A.-M. Bender, Tobias Lord, Anna Staffansdotter, Verena Jung, Sabine Undorf
Format: Article
Language:English
Published: Stockholm University Press 2024-01-01
Series:Tellus: Series B, Chemical and Physical Meteorology
Subjects:
aerosol-cloud interaction
cloud brightening
machine learning
gradient boosting regression
reanalysis
remote sensing
Online Access:https://account.b.tellusjournals.se/index.php/su-j-tbcpm/article/view/1868

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