Collaborative Research: CMG: Adaptive High-Order Methods for Nonhydrostatic Numerical Weather Prediction

合作研究：CMG：非静水数值天气预报的自适应高阶方法

• 批准号: 0530820
• 负责人: Louis Wicker
• 金额: --
• 依托单位: University of Oklahoma Norman Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-15 至 2011-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0530820&HistoricalAwards=false
• 关键词: Collaborative; Research; CMG; Adaptive; Order

This project is a new collaboration between geoscientists and mathematicians at two locations: Norman, Oklahoma and Boulder, Colorado. The initiative brings scientists new to the atmospheric science community to aid in the development of parallel, adaptive, unstructured, high-order methods for atmospheric modeling. Established atmospheric modelers will guide the effort, offering their experience with numerical modeling of complex weather systems. The collaboration also brings together the considerable expertise and resources of the National Center for Atmospheric Research (NCAR) located in Boulder, Colorado and the National Severe Storms Laboratory (NSSL) located in Norman, Oklahoma. The NCAR effort will build on its experience of developing high-order adaptive spectral element solvers for the shallow water equations, moving to high-order finite-volume adaptive Discontinuous Galerkin methods for the nonhydrostatic equation set. The NSSL effort will aid in this development by addressing the adaptivity and parallel computing issues and testing the methods on a suite of test problems of increasing complexity with a central long term goal of modeling tornadoes.Weather simulation is a complex problem involving many different scalesand requiring high resolution solutions of the equations governing the Earth's atmosphere. Recently, it has been recognized that localized flow structures, such as storms, may play an important role in obtaining the best weather forecast. Higher resolution weather simulations are required in applications in the atmospheric and air quality communities, at operational centers around the country attempting to forecast significant weather events, and for research endeavors that focus on the dynamics of severe storms such as hurricanes or tornadoes. Recent developments in computational mathematics techniques may be applicable to these models in order to tailor them to treatment of local structures such as storms and fronts. This project aims to create a collaboration between mathematicians who have developed these methods and meteorologists who have modeled and studied such events, that will yield more efficient and accurate models. Across the globe weather modelers are striving to increase resolution for the models, but are limited in their methodology to only reducing the spacing, thereby increasing computer time significantly. The US is already behind in that race. This project would leapfrog that strategy by developing more efficient models that can automatically hone in on localized structures with high precision.

这个项目是俄克拉荷马州诺曼和科罗拉多州博尔德两个地点的地球科学家和数学家之间的新合作。该倡议为大气科学界带来了新的科学家，以帮助开发并行的、自适应的、非结构化的、用于大气建模的高阶方法。成熟的大气模型师将指导这项工作，提供他们在复杂天气系统的数值模拟方面的经验。这项合作还汇集了位于科罗拉多州博尔德的国家大气研究中心(NCAR)和位于俄克拉何马州诺曼的国家严重风暴实验室(NSSL)的大量专业知识和资源。NCAR的工作将建立在其开发浅水方程的高阶自适应谱元素解算器的经验基础上，转向用于非静力方程组的高阶有限体积自适应间断Galerkin方法。NSL的工作将通过解决适应性和并行计算问题，并以龙卷风建模为中心长期目标，在一系列日益复杂的测试问题上测试方法，从而帮助这一发展。天气模拟是一个涉及许多不同尺度的复杂问题，需要控制地球大气的方程的高分辨率解。最近，人们认识到局地气流结构，如风暴，可能在获得最佳天气预报方面发挥重要作用。在大气和空气质量社区的应用中，在全国各地试图预报重大天气事件的业务中心，以及专注于飓风或龙卷风等严重风暴动态的研究工作中，都需要更高分辨率的天气模拟。计算数学技术的最新发展可能适用于这些模型，以便使它们适合于处理风暴和锋面等局部结构。这个项目的目的是在开发了这些方法的数学家和对这些事件进行建模和研究的气象学家之间建立合作，从而产生更有效和更准确的模型。全球各地的天气建模人员都在努力提高模型的分辨率，但他们的方法仅限于减少间距，从而显著增加了计算机时间。在这场竞赛中，美国已经落后了。这个项目将通过开发更有效的模型来超越这一战略，这些模型可以自动地高精度地研磨局部结构。