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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| Format: | Recurso digital |
| Sprache: | Englisch |
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2026
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| _version_ | 1866901914095976448 |
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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 |