High-resolution numerical weather prediction (NWP) models produce more detailed precipitation structures but the real benefit is probably the more realistic statistics gained with the higher resolution and not the information on the specific grid point. By evaluating three model pairs, each consisting of a high-resolution NWP system resolving convection explicitly and its low-resolution-driving model with parameterized convection, on different spatial scales and for different thresholds, this paper addresses the question of whether high-resolution models really perform better than their driving lower-resolution counterparts. The model pairs are evaluated by means of two fuzzy verification methods-upscaling (UP) and fractions skill score (FSS)-for the 6 months of the D-PHASE Operations Period and in a highly complex terrain. Observations are provided by the Swiss radar composite and the evaluation is restricted to the area covered by the Swiss radar stations. The high-resolution models outperform or equal the performance of their respective lowerresolution driving models. The differences between the models are significant and robust against small changes in the verification settings. An evaluation based on individual months shows that high-resolution models give better results, particularly with regard to convective, more localized precipitation events.

The unique combination of observing and modelling efforts during the Convective and Orographically-induced Precipitation Study (COPS) and D-PHASE (Demonstration of the

Seventeen days with post-frontal shower precipitation are analysed by means of radar data obtained from the German Weather Service's C-band radar network. The life cycle of clusters -defined here as contiguous rain areas including one or more radar-reflectivity peaks (i.e. convection cells) -is investigated. To allow for the continuous tracking of clusters, sometimes over a time period of more than an hour, a new, specially adapted tracking algorithm has been developed. The life cycle of convective clusters comprises five different stages: genesis; growth (including merging); stagnation; decay (including splitting); and dissolving. The transition likelihoods from a cluster with n maxima to one with m maxima are determined (the case m > n corresponding to growth and m < n to decay). It is found that, predominantly, clusters grow or decay by one cell. Results relating to the temporal evolution of post-frontal showers are presented, and a conceptual growth model is proposed. Although single cells are the most frequent cluster type, the spatial structure of the post-frontal precipitation field is dominated by multi-celled clusters. Their life cycle is essentially affected by cell merging and splitting. Although the transitions of all (about one million) identified clusters have been analysed and quantified, more research is necessary in order to understand the underlying principles of cluster growth.

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