Statistical post processes for the improvement of the results of numerical wave prediction models
Abstract
A new mathematical technique for adaptation of the results of numerical wave prediction
models to local conditions is proposed.The main aim is to reduce the systematic part of the
prediction error in the direct model outputs by taking advantage of the availability of local
measurements in the area of interest. The methodology is based on a combination of two
different statistical tools: Kolmogorov-Zurbenko (KZ) and Kalman filters.The first smoothes the
observation time series as well as that of model direct outputs in order to be comparable via
a Kalman filter.This is not the case in general, since forecasted values are smoothed spatially
and temporarily by the model itself while observations are point records where no smoothing
procedure is applied.The direct application of a Kalman filter to such qualitatively different
series may lead to serious instabilities of the method and discontinuities in the results. The
proper utilisation of KZ-filters turn the two series into a compatible mode and, therefore,
makes possible the exploitation of Kalman filters for the identification and subtraction of
systematic errors.The proposed method was tested in an open sea area for significant wave
height forecasts using the wave model WAM and six buoys as observational stations.
Description
A combination of Kolmogorov-Zurbenko and Kalman filters. Journal of Operational Oceanography, volume 4, No.1, 2011
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.Collections
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