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Adaptive grids in weather and climate modeling.

机译:天气和气候建模中的自适应网格。

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摘要

Adaptive Mesh Refinement (AMR) provides an attractive framework for atmospheric flows since it allows improved horizontal resolution in a limited region without requiring a fine grid resolution throughout the entire model domain. In this thesis, the adaptive grid technique has been applied to a revised version of NCAR/NASA's next generation dynamical core for climate and weather research. This hydrostatic so-called Lin-Rood dynamics package with a conservative finite volume discretization in flux form provides highly efficient algorithms for high performance computing.; The adaptive model design utilizes a spherical adaptive-grid library which is based on a cache-efficient block-structured data layout. This AMR communication library for parallel processors has been newly developed in the Computer Science Department at the University of Michigan. All blocks are self-similar and split into four in the event of refinement requests. The resolution of neighboring blocks can only differ by a factor of two which leads to cascading refinement regions.; The adaptive dynamical core is run in two configurations: the full 3D hydrostatic dynamical core on the sphere and the corresponding 2D shallow water model that has been extracted out of the 3D version. This shallow water setup serves as an ideal testbed for the horizontal discretization and the 2D adaptive-mesh strategy. It further allows the efficient testing of interpolation routines at fine-coarse grid interfaces.; The static and dynamic adaptations are tested using the standard shallow water test suite and a newly-developed idealized 3D baroclinic wave test case. Static adaptations are used to vary the resolution in pre-defined regions of interest. This includes static refinements near mountain ranges or static coarsenings in the longitudinal direction for the implementation of a so-called reduced grid in polar regions. Dynamic adaptations are based on flow characteristics and guided by refinement criteria that detect user-defined features of interest during a simulation. In particular, flow-based refinement criteria, such as vorticity or gradient indicators, are suggested. Refinements and coarsenings occur according to pre-defined threshold values.; This research project is characterized by an interdisciplinary approach involving atmospheric science, computer science and applied mathematics.
机译:自适应网格细化(AMR)为大气流动提供了一个有吸引力的框架,因为它可以在有限的区域内提高水平分辨率,而无需在整个模型域内都具有良好的网格分辨率。在本文中,自适应网格技术已应用于NCAR / NASA下一代气候和天气研究动力核心的修订版。这种静压的所谓的Lin-Rood动力学程序包具有以磁通形式保守的有限体积离散化,为高性能计算提供了高效的算法。自适应模型设计利用了球形自适应网格库,该库基于高速缓存有效的块结构化数据布局。这个用于并行处理器的AMR通信库是由密歇根大学的计算机科学系新开发的。所有块都是自相似的,并且在细化请求的情况下分为四个部分。相邻块的分辨率只能相差两个因子,从而导致精炼区域级联。自适应动力核心以两种配置运行:球形上的完整3D静液压动力核心和已从3D版本中提取的相应2D浅水模型。这种浅水设置是水平离散化和2D自适应网格策略的理想测试平台。它进一步允许在粗略的网格接口处有效地测试插值例程。使用标准浅水测试套件和新开发的理想化3D斜压波测试案例对静态和动态适应进行测试。静态调整用于更改预定义感兴趣区域中的分辨率。这包括在山脉附近进行静态精修或在纵向进行静态粗化,以在极地地区实施所谓的缩小网格。动态调整基于流量特性,并由细化标准指导,该细化标准可在模拟过程中检测用户定义的感兴趣特征。特别是,提出了基于流量的细化标准,例如涡度或梯度指标。根据预定义的阈值进行细化和粗化。该研究项目的特点是涉及大气科学,计算机科学和应用数学的跨学科方法。

著录项

  • 作者

    Jablonowski, Christiane.;

  • 作者单位

    University of Michigan.;

  • 授予单位 University of Michigan.;
  • 学科 Physics Atmospheric Science.
  • 学位 Ph.D.
  • 年度 2004
  • 页码 266 p.
  • 总页数 266
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 大气科学(气象学);
  • 关键词

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