Advanced Numerical Methods for Numerical Weather Prediction

Abstract

LONG-TERM GOAL. The long term goal of this research is to explore new numerical methods for the next generation global atmospheric model. The reason for considering new methods is due to the paradigm shift in high performance computing from vector computers to distributed memory machines. To take full advantage of the new architectures the global domain must now be partitioned into subdomains/ elements that can then be solved independently on the multiple processors of a distributed memory machine. These new methods, along with the grids used to tile the sphere, must be local in nature to exploit the new architectures. OBJECTIVES. The objective of this project is to develop a prototype for the next generation global model through the application of advanced numerical techniques. As part of this project, we will develop advanced numerical methods that are local in nature. Because we are developing local methods, we also need to develop local grid generation methods, such as icosahedral and cubic gnomonic grids, which yield uniform spatial representation on the sphere. This combination of grids and local methods simplifies the construction of efficient and high order algorithms for the atmospheric equations. In addition, some of this new technology (such as the semi-Lagrangian method) will be used to improve the current spectral forecast model of the Navy Operational Global Atmospheric Prediction System (NOGAPS).

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2000
Accession Number
ADA609966

Entities

People

  • Francis X. Giraldo

Organizations

  • United States Naval Research Laboratory

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Computational Science
  • Computer Architecture
  • Computers
  • Computing System Architectures
  • Demographic Cohorts
  • Equations
  • Errors
  • Finite Element Analysis
  • Galerkin Method
  • Grids
  • High Performance Computing
  • Meteorology
  • Military Research
  • Models
  • Shallow Water
  • Weather Forecasting

Readers

  • Approximation Theory.
  • Ocean-Atmosphere Mesoscale Modeling, Data Assimilation, and Flux Boundary Layers
  • Parallel and Distributed Computing.