Applying Fuzzy Clustering to a Multimodel Ensemble for U.S. East Coast Winter Storms: Scenario Identification and Forecast Verification

Abstract

AbstractThis article introduces a method for objectively separating and validating forecast scenarios within a large multimodel ensemble for the medium-range (3?7 day) forecasts of extratropical cyclones impacting the U.S. East Coast. The method applies fuzzy clustering to the principal components (PCs) of empirical orthogonal function (EOF) analysis on mean sea level pressure (MSLP) from a 90-member combination of the global ensembles from the National Centers for Environmental Prediction, the Canadian Meteorological Center, and the European Centre for Medium-Range Weather Forecasts. Two representative cases are presented to illustrate the applications of this method. Application to the 26?28 January 2015 event demonstrates that the forecast scenarios determined by the fuzzy clustering method are well separated and consistent in different state variables (i.e., MSLP, 500-hPa geopotential height, and total precipitation). The fuzzy clustering method and an existing ensemble sensitivity method are applied to the 26?28 December 2010 event to investigate forecast uncertainty, which demonstrates that these two methods are complementary to each other and can be used in the operations together to track the evolution of forecast uncertainty. For past cases one can define a cluster close to the analysis based on the projection of the analysis onto the PC base of clustering. This analysis group is validated using conventional validation metrics for both cases examined, and this analysis group has fewer errors than the other groups as well as the multimodel ensemble mean and individual model means.