Laplacian smoothing splines with generalized cross validation for objective analysis of meteorological data
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Authors
Franke, Richard H.
Subjects
Objective analysis
Optimum interpolation
Generalized cross validation
Laplacian smoothing splines
Optimum interpolation
Generalized cross validation
Laplacian smoothing splines
Advisors
Date of Issue
1985-08
Date
October 1984 - March 1985
Publisher
Monterey, California. Naval Postgraduate School
Language
en_US
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-guess 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 smoothing 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 that in a
correction to first-guess scheme (as in Optimum Interpolation) when the firstguess
error has low spatial correlation.
Type
Technical Report
Description
Series/Report No
Department
Identifiers
NPS Report Number
NPS-53-85-0008
Sponsors
funded by the Naval Environmental Prediction Research Facility,
Monterey, CA under Program Element 611 53N, Project (none), "Interpolation of
Scattered Meteorological Data"
Funder
Format
Citation
Distribution Statement
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.