University of Michigan
Department of Atmospheric, Oceanic & Space Sciences
Ann Arbor, MI, USA
Ph.D. thesis was published in February 2004
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
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.