Laplacian Smoothing Splines with Generalized Cross Validation for Objective Analysis of Meteorological Data.
Technical rept. Oct 84-Mar 85,
NAVAL POSTGRADUATE SCHOOL MONTEREY CA
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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.
- Numerical Mathematics