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: 0530845
• 负责人: William Skamarock
• 金额: --
• 依托单位: University Corporation For Atmospheric Res
• 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 2010-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0530845&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.

这个项目是两个地方的地球科学家和数学家之间的一个新的合作：诺曼，俄克拉荷马州和博尔德，科罗拉多。该计划为大气科学界带来了新的科学家，以帮助开发用于大气建模的并行，自适应，非结构化，高阶方法。成熟的大气建模人员将指导这项工作，提供他们在复杂天气系统数值建模方面的经验。这项合作还汇集了位于科罗拉多博尔德的国家大气研究中心（NCAR）和位于俄克拉荷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的努力将建立在其开发高阶自适应谱元求解浅水方程的经验，移动到高阶有限体积自适应间断Galerkin方法的非流体静力学方程组。NSSL的努力将有助于解决自适应性和并行计算问题，并在一套测试问题上测试方法，这些测试问题的复杂性不断增加，其中心长期目标是模拟tornadoes.Weather模拟是一个复杂的问题，涉及许多不同的scalesand需要高分辨率的解决方案的方程管理地球的大气层。最近，人们已经认识到，局部流动结构，如风暴，可能在获得最好的天气预报中发挥重要作用。在大气和空气质量社区的应用中，需要更高分辨率的天气模拟，在全国各地的运营中心试图预测重大天气事件，并专注于飓风或龙卷风等严重风暴的动态研究工作。计算数学技术的最新发展可能适用于这些模型，以使它们能够处理风暴和锋面等局部结构。该项目旨在开发这些方法的数学家和模拟和研究此类事件的气象学家之间建立合作，这将产生更有效和准确的模型。在全球范围内，地球仪的气象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，从而大大增加了计算机的时间。美国在这场竞赛中已经落后了。该项目将通过开发更有效的模型来跨越这一战略，这些模型可以自动精确地定位局部结构。