Key points are not available for this paper at this time.
Abstract High‐resolution forecasts from numerical models can look quite realistic and provide the forecaster with very useful guidance. However, when verified using traditional metrics they often score quite poorly because of the difficulty of predicting an exact match to the observations at high resolution. ‘Fuzzy’ verification rewards closeness by relaxing the requirement for exact matches between forecasts and observations. The key to the fuzzy approach is the use of a spatial window or neighbourhood surrounding the forecast and/or observed points. The treatment of the data within the window may include averaging (upscaling), thresholding, or generation of a PDF, depending on the particular fuzzy method used and its implicit decision model concerning what makes a good forecast. The size of the neighbourhood can be varied to provide verification results at multiple scales, thus allowing the user to determine at which scales the forecast has useful skill. This article describes a framework for fuzzy verification that incorporates several fuzzy verification methods. It is demonstrated on a high‐resolution precipitation forecast from the United Kingdom (UK) and the results interpreted to show the additional information that can be gleaned from this approach. Copyright © 2008 Royal Meteorological Society
Elizabeth E. Ebert (Sat,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: