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/ Projects and Programs / Collaborative Research: RUI: Applying Measurements, Models, and Machine Learning to Improve Parameterization of Aerosol Water Uptake and Cloud Condensation Nuclei in the Background Southeastern U.S

Collaborative Research: RUI: Applying Measurements, Models, and Machine Learning to Improve Parameterization of Aerosol Water Uptake and Cloud Condensation Nuclei in the Background Southeastern U.S

This project focuses on investigating the critical role of atmospheric aerosols in shaping Earth’s energy budget, particularly their direct impact through sunlight scattering and absorption, as well as their indirect influence as cloud condensation nuclei (CCN). Addressing these factors is crucial, as aerosol-induced effects constitute major uncertainties in climate forcing.  The overarching scientific goal is to develop, evaluate, and apply measurement-trained models for the precise calculation of Aerosol Liquid Water Content (ALWC) and CCN spectra specifically at the background Southeast U.S. APP site. This initiative aims to address the current gaps in understanding aerosol impacts on climate and to establish a foundation for extending these models to other regions. By enhancing our comprehension of aerosol-cloud interactions in this region, our research has the potential to contribute significantly to climate science. The long-term objective is to expand these measurement-trained models to encompass broader geographical contexts, ultimately advancing our ability to predict and mitigate the impacts of aerosols on Earth’s climate.

Facility | Multi-institutional | Research Working Group | Student Research
May 1, 2023
– April 30, 2026

Project Details

Funded by National Science Foundation (NSF)

(External Funding)

Administrative Unit

Research Institute for Environment, Energy, and Economics (RIEEE)

Research Theme

Project Team Members

James Sherman

Professor

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