To illustrate the application of the fuzzy clustering tool in verification and separation of scenarios, the 2015 blizzard is first explored using the multi-model ensemble including 90-members from ECMWF, Canadian Meteorological Center and NCEP ensemble datasets. Fuzzy clustering analysis based on the Principal Components of the two leading Empirical Orthogonal Function patterns of the 1- to 6-day ensemble forecasts are computed to group ensemble members into N (in our case 5) clusters. For after the fact verification, the analysis can be included as an additional ensemble member in the computation. We then examine 60 cool season HIW cases (20082015) using TIGGE ensemble data to statistically assess the performance of different modeling systems in capturing the scenario that includes the analysis. In actual operational application of the fuzzy clustering tool, the ensemble mean can be included as an additional member to objectively identify members that are closest to the mean. In summary, the clustering tool can efficiently separate different scenarios in a multi-model ensemble in targeted regional domains, provide forecasters an effective and objective method to compare forecast uncertainties among different operational models, and can be used as a tool to assess model performance.