Collaborative Research: RUI--Applying Measurements, Models, and Machine Learning to Improve Parameterization of Aerosol Water Uptake and Cloud Condensation Nuclei

Atmospheric aerosols are ubiquitous particles in the atmosphere that are made up of dust, soot, pollution, or even natural emissions from trees. Aerosols are crucially important for weather and climate because they scatter sunlight and act as the base for developing cloud droplets. This award will provide funding for a team of researchers from Appalachian State University and Georgia Tech to study the growth of particles with increasing humidity, and the range of particle sizes that serve as the base for cloud droplets. Aerosol impacts on climate have been highlighted in the Intergovernmental Panel on Climate Change (IPCC) reports as a key uncertainty for climate projections. The project has significant educational and training benefits, with plans for 8-12 undergraduate and Master’s level students to be involved in the project. Appalachian State is a primarily undergraduate university and will benefit from collaboration with a research-intensive institution. The overarching scientific objective of this award is to train, evaluate, and apply measurement-trained models for calculating aerosol liquid water content (ALWC) and cloud condensation nuclei (CCN) spectra at an aerosol network site at Appalachian State University, in Boone, North Carolina. ALWC cannot be directly measured, but it can be estimated from more commonly-measured aerosol optical properties. Intensive field campaigns during the winter and summer of 2024 would provide the necessary data to develop, train, and evaluate machine learning models that would be used to calculate ALWC and CCN spectra. Those models would then be retrospectively applied to the historical database of measurements at Appalachian St. to examine how and why aerosol hygroscopicity, ALWC and CCN spectra are changing. More specifically, the researchers will test the following hypotheses: 1. Machine learning models such as Random Forest, when trained using regionally-representative particle number size distributions and aerosol optical properties, are capable of predicting ALWC and CCN spectra at the Appalachian St. site; 2. Changing aerosol composition in the Southeastern US is leading to less hygroscopic aerosols measured at Appalachian St. over recent years. Less hygroscopic particles in turn are leading to lower ALWC. 3. Changing aerosol composition, hygroscopicity, and fine-mode particle size over the last decade are reducing the CCN concentrations at the Appalachian St. site at different supersaturation values. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.