/ 02 1 Dynamically Adapting Unstructured Triangular Grids : A New Paradigm for Geophysical Fluid Dynamics Modeling

In the early days of computing, geophysical fluid dynamics (GFD), numerical weather prediction (NWP) in particular, was a dominant factor in the design of computer architecture and algorithms. This early work focussed initially on finite difference algorithms on a rectangular computational grid and later on spectral methods. After the initial work of Charney, von Neumann, and Arakawa however, the focus shifted from the basic algorithms for the numerical solution of the fundamental differential equations to improvements in the model physics. Further work on fundamental numerical algorithms shifted to other disciplines – predominately the then emerging aerospace community. As a result, for 40 years, the GFD community has been using numerical techniques that are virtually unchanged. The two primary numerical methodologies that have been used for modeling the atmosphere and the ocean have been spectral methods for global modeling and structured rectilinear grids for regional modeling. In the last few years, a new paradigm for atmospheric and oceanic simulation has emerged: adaptive, unstructured, triangular grids. This paradigm has the advantage of tremendous flexibility in providing high resolution where required by either static physical properties (terrain elevation, coastlines, land use) or the evolving dynamical situation. The first application of this paradigm was the Operational Multiscale Environment model with Grid Adaptivity (OMEGA), an atmospheric simulation and forecasting tool; a more recent application of this paradigm is the more recently developed Multiscale Ocean Simulation System (MOSS). OMEGA with its embedded Atmospheric Dispersion Model (ADM) is a new atmospheric simulation system for real-time hazard prediction, conceived out of a need to advance the state-of-the-art in numerical weather prediction in order to improve our capability to predict the transport and diffusion of hazardous releases. MOSS is a nascent implementation of a similar grid structure and numerical algorithm to the simulation of ocean circulation. The purpose of this paper is to provide a description of this new paradigm and to present its use in atmospheric and oceanic simulation. Bacon et al. Proceedings of the Indian Academy of Science 08/22/02

[1]  Da‐Lin Zhang,et al.  A two-way interactive nesting procedure with variable terrain resolution , 1986 .

[2]  H. Kuo On Formation and Intensification of Tropical Cyclones Through Latent Heat Release by Cumulus Convection , 1965 .

[3]  David E. Smith,et al.  The global topography of Mars and implications for surface evolution. , 1999, Science.

[4]  Kelvin K. Droegemeier,et al.  Application of Continuous Dynamic Grid Adaption Techniques to Meteorological Modeling. Part I: Basic Formulation and Accuracy , 1992 .

[5]  P. Smolarkiewicz A Fully Multidimensional Positive Definite Advection Transport Algorithm with Small Implicit Diffusion , 1984 .

[6]  R. Daley Atmospheric Data Analysis , 1991 .

[7]  J. G. Charney,et al.  On the Scale of Atmospheric Motions , 1990 .

[8]  F. Girardi,et al.  The European Tracer Experiment Description and Summary of the ETEX Project , 1998 .

[9]  A. Staniforth,et al.  The Operational CMC–MRB Global Environmental Multiscale (GEM) Model. Part I: Design Considerations and Formulation , 1998 .

[10]  Itzhak Lottati,et al.  A second-order Godunov scheme on a spatial adapted triangular grid , 1994 .

[11]  David P. Bacon,et al.  Application Of Adaptive Grid Refinement ToPlume Modeling , 1970 .

[12]  J. Neumann,et al.  Numerical Integration of the Barotropic Vorticity Equation , 1950 .

[13]  R. Anthes A Cumulus Parameterization Scheme Utilizing a One-Dimensional Cloud Model , 1977 .

[14]  Yubao Liu,et al.  Surface Winds at Landfall of Hurricane Andrew (1992)—A Reply , 1999 .

[15]  Robert W. Jones A Nested Grid for a Three-Dimensional Model of a Tropical Cyclone. , 1977 .

[16]  A. Staniforth,et al.  Comments on “Swolarkiewicz's Deformational Flow” , 1987 .

[17]  Lawrence L. Takacs,et al.  Data Assimilation Using Incremental Analysis Updates , 1996 .

[18]  A. Staniforth,et al.  The Operational CMC–MRB Global Environmental Multiscale (GEM) Model. Part II: Results , 1998 .

[19]  F. Girardi,et al.  ETEX. A European tracer experiment; observations, dispersion modelling and emergency response , 1998 .

[20]  Joseph D. Baum,et al.  Numerical simulation of shock-elevated box interaction using an adaptive finite element shock capturing scheme , 1989 .

[21]  R. Pielke,et al.  A comprehensive meteorological modeling system—RAMS , 1992 .

[22]  I. Lottati,et al.  A Finite-Volume Algorithm for Three-Dimensional Magnetohydrodynamics on an Unstructured, Adaptive Grid in Axially Symmetric Geometry , 1998 .

[23]  Thomas J. Dunn,et al.  A Dynamically Adapting Weather and Dispersion Model: The Operational Multiscale Environment Model with Grid Adaptivity (OMEGA) , 2000 .

[24]  H. D. Orville,et al.  Bulk Parameterization of the Snow Field in a Cloud Model , 1983 .

[25]  J. Kain,et al.  A One-Dimensional Entraining/Detraining Plume Model and Its Application in Convective Parameterization , 1990 .

[26]  A. Radhika Sarma,et al.  An Operational Multiscale Hurricane Forecasting System , 2002 .

[27]  Takashi Sasamori,et al.  A Linear Harmonic Analysis of Atmospheric Motion with Radiative Dissipation , 1972 .

[28]  Rainald Loehner,et al.  Numerical simulation of a blast inside a Boeing 747 , 1993 .

[29]  Pius Lee,et al.  Evaluation of the Operational Multiscale Environment Model with Grid Adaptivity against the European Tracer Experiment , 2001 .

[30]  F. Girardi,et al.  The European Tracer Experiment Experimental Results and Database , 1998 .