Clustering methodologies applied to short-term ensemble forecasting

Ensemble analysis is increasingly becoming a part of the operational weather forecasting. In this paper we are interested in the analysis of short-term ensembles. We present the results of applying two basic techniques from multivariate data analysis namely cluster analysis and principal component analysis to short-term ensemble forecasting. In particular we analyze the clustering tendencies in ten field variables which are the output of the Eta model for ten different initial conditions. The aim of this work is to analyze the trajectories from these initial conditions and see how they cluster during evolution. In order to achieve our goal, we apply the rotated principal component analysis (RPCA) and several hierarchical and non-hierarchical clustering methods. Also we study the sensitivity of these methods with respect to changing various parameters.