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无处不在的颗粒，由灰尘，烟灰，污染，甚至是树木的自然排放。气溶胶对于天气和气候至关重要，因为它们会散布阳光并充当发展云滴的基础。该奖项将为阿巴拉契亚州立大学和佐治亚理工学院的一组研究人员提供资金，以研究颗粒的湿度增加，以及作为云滴剂基础的颗粒大小的范围。在政府间气候变化（IPCC）报告中，对气候的影响已被强调，这是气候项目的关键不确定性。该项目具有重大的教育和培训优势，并计划了8至12年级的本科生和硕士学位学生参与该项目。阿巴拉契亚州是一所小学大学，将受益于与研究密集型机构的合作。该奖项的总体科学目标是在阿巴拉契亚州立大学的Aerosol网络网站上，培训，评估和应用以测量训练的模型来计算气溶胶液体水含量（ALWC）和云冷凝核（CCN）光谱。无法直接测量ALWC，但可以从更常见的气溶胶光学特性中估算。 2024年冬季和夏季的密集现场活动将提供必要的数据，以开发，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，这些模型将回顾性地应用于阿巴拉契亚街的历史数据库，以检查如何以及为什么气溶胶吸湿性，ALWC和CCN光谱在变化。更具体地说，研究人员将检验以下假设：1。当使用区域代表性的颗粒数大小分布和气溶胶光学特性训练时，像随机森林这样的机器学习模型能够预测阿巴拉契亚街站点的ALWC和CCN光谱； 2。近年来，美国东南部的气溶胶组成变化导致在阿巴拉契亚街上测量的吸湿性气溶胶较少。较少的湿气颗粒反过来导致较低的Alwc.3。在过去的十年中，改变气溶胶成分，吸湿性和细态粒径是在不同超饱和值的阿巴拉契亚街地点的CCN浓度降低。和更广泛的影响审查标准。