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

这个研究项目寻求更好的方法来模拟大气气溶胶，或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)它们包括有害的微粒污染，2)它们包括凝结核，这对云和降水的形成至关重要，3)它们影响地球的温度，直接通过将阳光反射回太空，间接通过影响云的反射率。气溶胶在天气和气候模式中很难模拟，因为它们数量多、体积小、形成、相互作用和消散的方式复杂。由于计算能力的限制，气溶胶通常以非常简单的方式表示，并且许多大气模型仅将气溶胶浓度作为粒子有效半径的函数来跟踪。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，其表现也大不相同，因此可能需要20个单独的参数来正确描述每个颗粒。在这个项目中开发的粗粒度技术为开发近似气溶胶模拟技术提供了一个框架，其中通过跟踪有限数量随机生成的“超粒子”的演变来模拟气溶胶浓度。每个超粒子代表一个粒子在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更好地模拟气候变化，因为气候模拟中的许多不确定性来自气溶胶及其与云相互作用的不确定性。更好的气溶胶模拟也意味着更好的模拟空气微粒污染的能力，这将导致空气质量评估和预测的改进。此外，该项目将通过开发一门本科课程来支持教育，在这门课程中，微积分概念将通过应用于大气热力学来教授。

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.