Preliminary Results from the Analysis of Wind Component Error, July Data

Abstract

Estimation of mean square prediction error of wind components is required in the optimal interpolation (OI) process in numerical prediction of atmospheric variables. Statistical models with log-linear scale parameters which include covariates are described for the prediction error. Data from February April and July of 1991 are used to fit the model parameters and to study the predictive ability of the models. This preliminary investigation indicates that observational and first guess wind components can be helpful in predicting mean square prediction error for wind components. The predictions using observational winds appear to be better at the 850 mb level. The predictions using first guess winds appear to be better at the 250 mb level.

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 1992
Accession Number
ADA259654

Entities

People

  • Donald P. Gaver Jr.
  • Patricia A. Jacobs

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Artificial Intelligence
  • California
  • Data Analysis
  • Data Sets
  • Industrial Engineering
  • Interpolation
  • Mathematics
  • Military Research
  • North America
  • North Carolina
  • Operations Research
  • Public Health
  • Random Variables
  • Standards
  • Statistical Analysis
  • Statistics
  • Universities

Fields of Study

  • Environmental science

Readers

  • Computational Modeling and Simulation
  • Ocean-Atmosphere Mesoscale Modeling, Data Assimilation, and Flux Boundary Layers
  • Statistical inference.