Original paper
Spatial verification of high-resolution ensemble precipitation forecasts using local wavelet spectra
Kapp, Florian; Friederichs, Petra; Brune, Sebastian; Weniger, Michael
Meteorologische Zeitschrift Vol. 27 No. 6 (2018), p. 467 - 480
47 references
published: Dec 11, 2018
published online: Sep 28, 2018
manuscript accepted: Aug 23, 2018
final revised version received: May 30, 2018
manuscript revision requested: Feb 22, 2018
manuscript received: Dec 21, 2017
Open Access (paper may be downloaded free of charge)
Abstract
The purpose of this study is to perform verification of the structural characteristics of high-resolution spatial forecasts without relying on an object identification algorithm. To this end, a wavelet approach developed for image texture analysis is applied to an ensemble of high-resolution quantitative precipitation forecasts. The forecasts are verified against estimates from a high-resolution regional reanalysis with a similar model version. The wavelet approach estimates an averaged wavelet spectrum for each spatial field of the ensemble forecasts and the reanalysis, thereby removing all information on the localization of precipitation and investigating solely the overall structure of forecasts and reanalysis. In order to assess skill using a multivariate score, an additional reduction of dimensionality is needed. This is performed using singular vectors from a linear discriminant analysis as it favors data compression in the direction where the ensemble is most discriminating. We discuss implications of this strategy, show that the averaged wavelet spectra give valuable information on forecast performance. The skill difference between a so-called perfect forecast using for verification a member of the ensemble, and the non-perfect forecast using the reanalysis points to significant deficiencies of the forecast ensemble. Overall, the discriminating power solely based on global spectral information is remarkable, and the COSMO-DE-EPS is a quite good forecast ensemble with respect to the reanalysis.
Keywords
spatial verification • wavelet transform • local wavelet spectrum • linear discriminant analysis • ensemble forecasts