CMG: Coarse-graining and Multiscale Analysis of Stochastic Particle-resolved Aerosol Models

CMG：随机粒子分辨气溶胶模型的粗粒度和多尺度分析

• 批准号: 0934491
• 负责人: Lee DeVille
• 金额: $ 77.35万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0934491&HistoricalAwards=false
• 关键词: CMG; Coarse; graining; Multiscale; Analysis

This research project seeks better ways to simulate atmospheric aerosols, or tiny particles suspended in air. Aerosols are important for at least three reasons: 1) they include harmful particulate pollution, 2) they include condensation nuclei which are essential for the formation of clouds and precipitation, and 3) they affect Earth's temperature, both directly by reflecting sunlight back to space and indirectly by affecting the reflectivity of clouds. Aerosols are difficult to simulate in weather and climate models because of their large numbers, small size, and the complex ways in which they form, interact with each other, and dissipate. Due to limitations in computing power, aerosols are often represented in very simple ways, and many atmospheric models track aerosol concentration only as a function of the effective radius of the particles. But in reality aerosols of a given size behave quite differently depending on their chemical makeup (e.g. sulfate, nitrate, ammonium, sea salt, black carbon, mineral dust, etc.), so that it would take perhaps 20 separate parameters to properly describe each particle. The coarse-graining technique to be developed in this project provides a framework for developing approximate aerosol simulation techniques, in which aerosol concentrations are simulated by tracking the evolution of a finite number of randomly generated "superparticles". Each superparticle represents a distribution of particles in the 20-dimensional parameter space, and the aerosol simulation is accomplished by tracking the evolution of the superparticles.The project will have several benefits beyond its contribution to aerosol simulation. For the broader scientific community, improvements in aerosol simulation will lead to better simulations of climate change, since much of the uncertainty in climate simulations comes from uncertainty in aerosols and their interactions with clouds. Better aerosol simulation also means an improved ability to simulate particulate air pollution, which will lead to improvements in air quality assessment and forecasting. In addition, the project will support education through the development of an undergraduate course in which calculus concepts are taught through their application to atmospheric thermodynamics.

这项研究项目寻求更好的方法来模拟大气气溶胶，即悬浮在空气中的微小颗粒。气溶胶之所以重要，至少有三个原因：1)它们包括有害的颗粒污染；2)它们包括对云和降水的形成至关重要的凝聚核；3)它们通过将阳光反射回太空直接影响地球温度，也通过影响云的反射率间接影响地球温度。由于气溶胶数量多、尺寸小，以及它们形成、相互作用和消散的复杂方式，在天气和气候模式中很难模拟。由于计算能力的限制，气溶胶通常以非常简单的方式表示，许多大气模型只跟踪气溶胶浓度作为颗粒有效半径的函数。但在现实中，给定大小的气溶胶根据其化学组成(如硫酸盐、硝酸盐、铵、海盐、黑碳、矿物粉尘等)的不同表现出很大的不同，因此可能需要20个不同的参数来正确描述每个颗粒。该项目将开发的粗粒化技术为开发近似的气溶胶模拟技术提供了一个框架，在该技术中，通过跟踪有限数量的随机产生的“超级粒子”的演变来模拟气溶胶浓度。每个超粒子代表了粒子在20维参数空间中的分布，气溶胶模拟是通过跟踪超粒子的演化来完成的。除了对气溶胶模拟的贡献外，该项目还将有几个好处。对于更广泛的科学界来说，气溶胶模拟的改进将导致对气候变化的更好模拟，因为气候模拟中的大部分不确定性来自气溶胶及其与云的相互作用的不确定性。更好的气溶胶模拟也意味着模拟颗粒物空气污染的能力的提高，这将导致空气质量评估和预报的改进。此外，该项目将通过开发一门本科课程来支持教育，在该课程中，通过将微积分概念应用于大气热力学来教授它们。