Open AccessApplying a Divisive Clustering Algorithm to a Large Ensemble for Medium-Range Forecasting at the Weather Prediction Center
Author(s) -
Keith F. Brill,
Anthony R. Fracasso,
Christopher M. Bailey
Publication year - 2015
Publication title -
weather and forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.393
H-Index - 106
eISSN - 1520-0434
pISSN - 0882-8156
DOI - 10.1175/waf-d-14-00137.1
Subject(s) - cluster analysis , ensemble forecasting , computer science , range (aeronautics) , consistency (knowledge bases) , ensemble average , cluster (spacecraft) , process (computing) , ensemble learning , meteorology , data mining , artificial intelligence , geography , climatology , geology , materials science , composite material , programming language , operating system
This article explores the potential advantages of using a clustering approach to distill information contained within a large ensemble of forecasts in the medium-range time frame. A divisive clustering algorithm based on the one-dimensional discrete Fourier transformation is described and applied to the 70-member combination of the 20-member National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System (GEFS) and the 50-member European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble. Cumulative statistical verification indicates that clusters selected objectively based on having the largest number of members do not perform better than the ECMWF ensemble mean. However, including a cluster in a blended forecast to maintain continuity or to nudge toward a preferred solution may be a reasonable strategy in some cases. In such cases, a cluster may be used to sharpen a forecast weakly depicted by the ensemble mean but favored in consideration of continuity, consistency, collaborative thinking, and/or the trend in the guidance. Clusters are often useful for depicting forecast solutions not apparent via the ensemble mean but supported by a subset of ensemble members. A specific case is presented to demonstrate the possible utility of a clustering approach in the forecasting process.