An Overview of the Past, Present and Future of Gravity-Wave Drag Parametrization for Numerical Climate and Weather Prediction Models

Abstract

An overview of the parametrization of gravity wave drag in numerical weather prediction and climate simulation models is presented. The focus is primarily on understanding the current status of gravity wave drag parametrization as a step towards the new parametrizations that will be needed for the next generation of atmospheric models. Both the early history and latest developments in the field are discussed. Parametrizations developed specifically for orographic and convective sources of gravity waves are described separately, as are newer parametrizations that collectively treat a spectrum of gravity wave motions. The differences in issues in and approaches for the parametrization of the lower and upper atmospheres are highlighted. Various emerging issues are also discussed, such as explicitly resolved gravity waves and gravity wave drag in models, and a range of unparametrized gravity wave processes that may need attention for the next generation of gravity wave drag parametrizations in models.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2003
Accession Number
ADA525694

Entities

People

  • Hye-yeong Chun
  • Stephen D. Eckermann
  • Young-joon Kim

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Cyber
  • Energy and Power Technologies
  • Ground and Sea Platforms

DTIC Thesaurus Topics

  • Atmospheric Sciences
  • Boundary Layer
  • Buoyancy
  • Climate Change
  • Convection
  • Equations
  • Fluid Dynamics
  • Gravity Waves
  • Meteorology
  • Physical Theories
  • Physics Laboratories
  • Standing Waves
  • Stratified Fluids
  • Three Dimensional
  • Turbulence
  • Wave Power
  • Weather Forecasting

Fields of Study

  • Environmental science

Readers

  • Atmospheric Science / Meteorology, specifically Wind Wave Turbulence.
  • Computational Modeling and Simulation
  • Graph Algorithms and Convex Optimization.