A Lagrangian Time-Series Machine Learning Framework for Predicting Concentrations and Exploring Drivers of Atmospheric Aerosols: Model Development and Application to Cloud Condensation Nuclei in Marine Boundary Layer

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Hauptverfasser: Zhou, Shengqian, Qi, Dong, Liu, Hanyang, Vorobeychik, Yevgeniy, Wang, Jian
Format: Recurso digital
Sprache:Englisch
Veröffentlicht: Zenodo 2026
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author Zhou, Shengqian
Qi, Dong
Liu, Hanyang
Vorobeychik, Yevgeniy
Wang, Jian
author_facet Zhou, Shengqian
Qi, Dong
Liu, Hanyang
Vorobeychik, Yevgeniy
Wang, Jian
contents <p>This preprint presents a Lagrangian time-series machine learning framework for predicting atmospheric aerosol concentrations and investigating their environmental drivers. We demonstrate the advantages of this approach through an application to marine boundary layer cloud condensation nuclei.</p>
format Recurso digital
id zenodo_https___doi_org_10_5281_zenodo_18453129
institution Zenodo
language eng
publishDate 2026
publisher Zenodo
record_format zenodo
spellingShingle A Lagrangian Time-Series Machine Learning Framework for Predicting Concentrations and Exploring Drivers of Atmospheric Aerosols: Model Development and Application to Cloud Condensation Nuclei in Marine Boundary Layer
Zhou, Shengqian
Qi, Dong
Liu, Hanyang
Vorobeychik, Yevgeniy
Wang, Jian
Atmospheric Sciences
Aerosol-Cloud Interactions
Machine Learning
Cloud Condensation Nuclei
<p>This preprint presents a Lagrangian time-series machine learning framework for predicting atmospheric aerosol concentrations and investigating their environmental drivers. We demonstrate the advantages of this approach through an application to marine boundary layer cloud condensation nuclei.</p>
title A Lagrangian Time-Series Machine Learning Framework for Predicting Concentrations and Exploring Drivers of Atmospheric Aerosols: Model Development and Application to Cloud Condensation Nuclei in Marine Boundary Layer
topic Atmospheric Sciences
Aerosol-Cloud Interactions
Machine Learning
Cloud Condensation Nuclei
url https://doi.org/10.5281/zenodo.18453129