  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.
