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

一个由数学家、计算算法专家和大气动力学家组成的合作团队，在数值天气预报和气候建模中遇到的涡旋主导流的自适应网格细化的主题领域，优先考虑了一系列具有挑战性和大部分未解决的问题。正在采用的一个共同的潜在主题是寻求改进自适应多尺度方法，以更好地解决主导这类地球物理流动的强涡旋的精细特征。这种基于特征的半结构化目标不同于对涡核进行高分辨率网格划分的简单嵌套。将探讨的回答这些问题的建议方法包括比较和混合已建立的数值算法，如有限体积和涡团方法，以及径向基函数方案，树编码和较少探索的这些杂交。在气候模拟和天气预报等领域，需要解决涡旋表示中的精细结构，以提高预测技能和长期行为。发展自适应的、多尺度解析的数值格式来处理涡旋主导的流动也有望在地球物理以外的领域得到更广泛的应用。

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.