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
