Identifying the Skill of Higher Resolution Precipitation Forecasts with Neighborhood Verification Techniques

As numerical weather prediction models began to increase considerably in resolution, it became clear that traditional grid-point-by-grid-point verification methods did not provide material information about forecast performance. High-resolution numerical weather prediction (NWP) models produce more detailed precipitation structures but the real benefit is the more realistic statistics obtained from the higher resolution rather than the information for the specific grid point. Neighborhood verification rewards closeness by relaxing the requirement for exact matches between forecasts and observations. The advantage of the neighborhood approachis the use of a spatialwindowsurrounding the forecast and/or observedpoints. The size of the neighborhood can be varied to provide verification results at multiple scales, enabling the determination of which scales the forecast has the most useful skill. A strong convective event is used as a test case for forecasting precipitation over the complexterrain of the Alps. Theavailable precipitation data are treated withinwindows using a variety of methods for averaging (upscaling), thresholding, and PDF generation, each of which provides distinctly useful information on model performance.