Abstract
The spatial resolution of Numerical Weather Prediction (NWP) models has increased significantly in recent years. While high-resolution models are able to produce more detailed precipitation structures, their true benefit lies in more realistic statistics rather than the information provided for a specific grid point. Unfortunately, NWP model verification using traditional grid-point-by-grid-point methods has not managed to keep pace due to the limited amount of point observations available in comparable resolution. Spatial verification methods represent a possible solution since they reward closeness or resemblance by relaxing the requirement for exact matches between the forecast and observations.An intense convective event in the Mediterranean region is used as a test case to analyze the forecasting performance of the Consortium for Small-scale Modeling model (COSMO) at two different resolutions. Satellite estimates of precipitation are used as ground truth. The precipitation forecasts are assessed using various spatial methods and averaging techniques, each of which provides distinctly useful information on model performance.The results from this particular test case indicate that a neighbourhood verification framework as well as the use of the Structure, Amplitude and Location (SAL) index, which belongs to the object-based methods, can identify the scales and precipitation thresholds at which the fine resolution configuration (COSMO-GR3) provides more accurate forecasts than the coarser resolution configuration (COSMO-GR7) and is therefore worth the additional computational burden. Additional insight is gained by comparing the results of various spatial methods with traditional verification metrics based on point observations. Finally, it is demonstrated that the interpolation method used in the adaptation of data at various scales can, in some cases, influence verification results as much as model resolution.
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