Three Dimensional Covariance Functions: Real Data.

Abstract

Height innovation data for a two-month period from NOGAPS was analyzed to obtain height prediction and observation error covariances. Different methods of weighting the data in least squares approximations were investigated using the second order autoregressive correlation function, both with and without an additive constant (varying with pressure level). Based on the properties of the derived covariance matrices and its parameters, the SOAR without an additive constant was used for the horizontal approximations. The vertical correlations were fit using a combination of SOAR plus and additive constant and a transformation of the logP coordinate to another coordinate to achieve a best fit. The resulting three dimensional approximation is partially separable, being the product of the horizontal covariance function (which depends on height) and the vertical correlation function. Figures demonstration various aspects of the process and the results are given.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1997
Accession Number
ADA332079

Entities

People

  • Richard Franke

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Additives (Chemicals)
  • Covariance
  • Data Sets
  • Eigenvalues
  • Equations
  • Latitude
  • Longitude
  • Marine Meteorology
  • Measurement
  • Military Research
  • Numbers
  • Observation
  • Square Roots
  • Standards
  • Thickness
  • Three Dimensional
  • Weather Forecasting

Readers

  • Mathematics or Statistics
  • Ocean-Atmosphere Mesoscale Modeling, Data Assimilation, and Flux Boundary Layers
  • Statistical inference.