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

合作研究：RUI——应用测量、模型和机器学习来改进气溶胶吸水和云凝核的参数化

• 批准号: 2307150
• 负责人: James Sherman
• 金额: $ 47.37万
• 依托单位: Appalachian State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2307150&HistoricalAwards=false
• 关键词: Collaborative; Research; RUI; Applying; Measurements

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 project is co-funded by the Directorate for Geosciences to support AI/ML advancement in the geosciences.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.

大气气溶胶是大气中普遍存在的颗粒，由灰尘、烟灰、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，因为它们会散射阳光，并成为形成云滴的基础。将为阿巴拉契亚州立大学和佐治亚理工学院的研究小组提供资金，以研究颗粒随湿度增加的增长，以及作为云滴对气候影响的基础的颗粒尺寸范围。政府间气候变化专门委员会 (IPCC) 报告称，该项目具有显着的教育和培训效益，计划让阿巴拉契亚州主要参与 8 至 12 名本科生和硕士生。该奖项的首要科学目标是训练、评估和应用用于计算气溶胶液态水含量 (ALWC) 和云凝结核的测量训练模型。北卡罗来纳州布恩阿巴拉契亚州立大学气溶胶网络站点的 (CCN) 光谱无法直接测量，但可以根据 2024 年冬季和夏季更常见的气溶胶光学特性进行估计。将提供必要的数据来开发、训练和评估机器学习模型，这些模型将用于计算 ALWC 和 CCN 光谱，然后将这些模型回顾性地应用于阿巴拉契亚圣路易斯的测量历史数据库。为了研究气溶胶吸湿性、ALWC 和 CCN 光谱如何以及为何发生变化，更具体地说，研究人员将测试以下假设：1. 使用区域代表性颗粒数大小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地点的 ALWC 和 CCN 光谱；2. 美国东南部气溶胶成分的变化导致在近年来，阿巴拉契亚圣地吸湿性颗粒的减少反过来导致了 ALWC 的降低。 过去十年中气溶胶成分、吸湿性和精细模式颗粒尺寸的变化正在降低阿巴拉契亚圣地在不同过饱和度下的 CCN 浓度。该项目由地球科学理事会共同资助，以支持地球科学领域的 AI/ML 进步。该奖项反映了 NSF 的法定使命，并通过评估认为值得支持利用基金会的智力优势和更广泛的影响审查标准。