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

大气气溶胶是大气中无处不在的颗粒，由灰尘、烟尘、污染物甚至树木的自然排放物组成。 气溶胶对天气和气候至关重要，因为它们散射阳光，并作为发展云滴的基础。 该奖项将为来自阿巴拉契亚州立大学和格鲁吉亚理工大学的一组研究人员提供资金，以研究随着湿度增加颗粒的生长，以及作为云滴基础的颗粒尺寸范围。 气溶胶对气候的影响在政府间气候变化专门委员会（气专委）的报告中被强调为气候预测的一个关键不确定因素。 该项目具有显著的教育和培训效益，计划有8-12名本科生和硕士生参与该项目。 阿巴拉契亚州立大学是一所以本科为主的大学，将从与研究密集型机构的合作中受益。 该奖项的首要科学目标是培训，评估和应用测量训练模型，用于计算北卡罗来纳州布恩市阿巴拉契亚州立大学气溶胶网络站点的气溶胶液态水含量（ALWC）和云凝结核（CCN）光谱。 ALWC不能直接测量，但它可以从更常见的测量气溶胶光学特性估计。 2024年冬季和夏季的密集实地活动将提供必要的数据来开发、训练和评估用于计算ALWC和CCN光谱的机器学习模型。 然后，这些模型将被回顾性地应用到阿巴拉契亚街的历史测量数据库中，以研究气溶胶吸湿性、ALWC和CCN光谱是如何变化的以及为什么变化。 更具体地说，研究人员将测试以下假设：1。机器学习模型，如随机森林，当使用区域代表性的颗粒数量尺寸分布和气溶胶光学特性进行训练时，能够预测阿巴拉契亚圣站点的ALWC和CCN光谱;2.近年来，美国东南部气溶胶成分的变化导致阿巴拉契亚街测得的吸湿性气溶胶减少。较少的吸湿性颗粒反过来导致较低的ALWC。在过去的十年中，气溶胶成分、吸湿性和精细模式颗粒尺寸的变化正在降低阿巴拉契亚街站点在不同过饱和值下的云凝结核浓度。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.