Investigating reduced-dimensional variability in aircraft-observed aerosol–cloud parameters

Investigating reduced-dimensional variability in aircraft-observed aerosol–cloud parameters

Aerosol-cloud interactions contribute significant uncertainty to modern climate model predictions. Analysis of complex observed aerosol-cloud parameter relationships is a crucial piece of reducing this uncertainty. Here, we apply two machine learning methods to explore variability in in-situ observa...

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Main Authors: Kayley M. Butler, Sam J. Silva, Armin Sorooshian, Richard H. Moore, Glenn S. Diskin, John B. Nowak, Luke Ziemba, Ewan Crosbie, Michael A. Shook, Joshua DiGangi, Edward Winstead, Claire Robinson, Yonghoon Choi
Format: Article
Language:English
Published: Cambridge University Press 2025-01-01
Series:Environmental Data Science
Subjects:
aerosol
atmospheric composition
clouds
dimensionality reduction
machine learning
Online Access:https://www.cambridge.org/core/product/identifier/S2634460225000172/type/journal_article
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https://www.cambridge.org/core/product/identifier/S2634460225000172/type/journal_article

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