Assessing the Benefits of Convection-Permitting Models by Neighborhood Verification: Examples from MAP D-PHASE

Abstract 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 lower-resolution 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.