A Comparison of Two Initialization Methods in Data Assimilation

Abstract

Two different initialization methods were developed and tested in global data assimilation experiments covering a five-day period. One method was based on the nonlinear normal mode initialization, and the other was based on the balance equation. Both techniques were developed using the calculus of variations methodology. In both methods, the initial divergence was computed from the forecast first-guess fields, except it was partially modified in the nonlinear normal mode method to improve the balance. The assimilation system used to test the initialization methods was developed for the global forecast model at the Fleet Numerical Oceanography Center. This model was adapted from the general circulation model developed at the University of California at Los Angeles. A comparison of the gravity wave noise from the two methods is given for versions of the model with and without heating. Other comparisons are given for divergence, precipitation rates, wave structure and cyclogenesis. The two methods are similar in their performance in data assimilation. The balance equation method is more flexible in weight specification and, consequently the forecasts verify with observations closer than the normal mode method.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1982
Accession Number
ADA119599

Entities

People

  • Edward H. Barker

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

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

DTIC Thesaurus Topics

  • Atmospheric Motion
  • Atmospheric Sciences
  • Calculus
  • Calculus Of Variations
  • Computational Fluid Dynamics
  • Computational Science
  • Data Analysis
  • Equations
  • Gravity Waves
  • Grids
  • Meteorology
  • Military Research
  • North America
  • Observation
  • Research Facilities
  • Schools
  • Stratified Fluids

Fields of Study

  • Environmental science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Atmospheric Science/Meteorology