Characterization of convective systems dynamics and microphysics using time-delayed tandem microwave radiometers
Résumé
Deep convective cloud systems are central to the global water and energy cycle, and yet their representation in climate models remains challenging. This study explores the potential of machine learning to classify and characterize cloud structures inside cloud systems using radiometric measurements from the C²OMODO (Convective Core Observation through MicrOwave Derivative in the trOpics) mission. The gradient boosting algorithm is used to classify clouds into four types: anvil, stratiform, convective, and deep convective, and achieves high performances. Furthermore, retrievals of both dynamical and microphysical quantities are shown to perform well.
The classification method has also been shown to significantly improve the performance of geophysical variable retrieval. This study highlights the potential of the forthcoming C²OMODO mission in advancing our understanding of convective systems.
| Origine | Fichiers produits par l'(les) auteur(s) |
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| Licence |
Domaine public
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