CMG: Adaptive Mesh Refinement for Vortices in Climate and Weather-Forecasting: Comparing and Blending Finite Volume Methods with Vortex/Radial Basis Function Algorithms

CMG：气候和天气预报中涡旋的自适应网格细化：有限体积方法与涡旋/径向基函数算法的比较和混合

• 批准号: 0723440
• 负责人: John Boyd
• 金额: $ 42.39万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-10-01 至 2010-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0723440&HistoricalAwards=false
• 关键词: CMG; Adaptive; Mesh; Refinement; Vortices

A collaborating team of mathematicians, computational algorithm experts and atmospheric dynamacists have prioritized a series of challenging and largely unresolved questions in the topic area of adaptive mesh refinement of vortex dominated flows encountered in numerical weather prediction and climate modeling. A common underlying theme being adopted seeks the improvement of adaptive, multi-scale methods to better resolve the finer features of intense vortices which dominate these kinds of geophysical flows. This semi-structured feature-based goal is different from simple nesting of higher resolution gridding of vortex cores. Suggested approaches which will be explored to answer these questions include comparing and blending well-established numerical algorithms, such as finite volume and vortex blob methods, along with radial basis function schemes, tree codes and less well explored hybridizations of these. Resolving fine structure in the representation of vortices is needed to strengthen predictive skill and long-time behavior in fields such as climate modeling and weather forecasting. Development of adaptive, multi-scale-resolving numerical schemes to treat vortex dominated flows are also expected to have wider application to areas outside of geophysics.

一支由数学家，计算算法专家和大气动力学家组成的合作团队优先考虑了一系列具有挑战性的，在数字天气预测和气候建模中遇到的涡流占主导地位的主题领域的挑战性和未解决的问题。采用的一个共同的基本主题寻求改进自适应，多尺度方法，以更好地解决主导这些地球物理流动的强烈涡流的精细特征。这个半结构化特征的目标与涡流核心更高分辨率网格的简单嵌套不同。将探索以回答这些问题的建议方法包括比较和融合完好的数值算法，例如有限体积和涡旋斑点方法，以及径向基函数方案，树代码，不太探索的这些方法。需要在涡流的表示中解决精细的结构，以增强气候建模和天气预报等领域的预测技能和长期行为。预计，以涡流为主流的自适应，多尺度分辨的数值方案也有望在地球物理学以外的地区更广泛地应用。