Laplacian Smoothing Splines with Generalized Cross Validation for Objective Analysis of Meteorological Data.

Abstract

The use of Laplacian smoothing splines (LSS) with generalized cross validation (GCV) to choose the smoothing parameter for the objective analysis problem is investigated. Simulated 500 mb pressure height fields are approximated from first-quess data with spatially correlated errors and observed values having independent errors. It is found that GCV does not allow LSS to adapt to variations in individual realizations, and that specification of a single suitable parameter value for all realizations leads to smaller rms error overall. While the tests were performed in the context of data from a meteorology problem, it is expected the results carry over to data from other sources. A comparison shows that significantly better approximations can be obtained using LSS applied in a unified manner to both first-guess and observed values rather than in a correction to first-guess scheme (as in Optimum Interpolation) when the first-guess error has low spatial correlation.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1985
Accession Number
ADA159336

Entities

People

  • R. Franke

Organizations

  • Naval Postgraduate School

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Communities of Interest

  • Materials and Manufacturing Processes
  • Space

DTIC Thesaurus Topics

  • Atmospheric Sciences
  • Computational Science
  • Computations
  • Computer Science
  • Covariance
  • Data Science
  • Fluid Dynamics
  • Grids
  • Interpolation
  • Mathematics
  • Meteorological Data
  • Meteorology
  • Research Facilities
  • Simulations
  • Standards
  • Statistics
  • Weather Forecasting

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  • Approximation Theory.
  • Atmospheric Science/Meteorology
  • Computational Modeling and Simulation