a) (b) Fig. 1: (a) Analysis of the variation in cluster membership over 81 different clusterings of the case "Tropical Cyclone Karl", an ensemble of 51 potential vorticity fields. Circular elements represent ensemble members, colors distinguish clusters (member 45 is enlarged: color of inner circles denotes reference cluster, surrounding pie-charts show how often the member was grouped into another cluster). Dashed outlines highlight cluster representative members. Member 26 is picked, for all members with similar cluster membership variation "variability matrix plots" (squared elements encoding cluster membership of all 81 clusterings) pop up. (b) A "contour probability plot" (CPP, different greens show probabilities for contour line occurrence) shows the variability of an iso-contour within a selected cluster. Overlaid stipple pattern shows the spatial variation of the plot with respect to the 81 clusterings.Abstract-In meteorology, cluster analysis is frequently used to determine representative trends in ensemble weather predictions in a selected spatio-temporal region, e.g., to reduce a set of ensemble members to simplify and improve their analysis. Identified clusters (i.e., groups of similar members), however, can be very sensitive to small changes of the selected region, so that clustering results can be misleading and bias subsequent analyses. In this article, we -a team of visualization scientists and meteorologists-deliver visual analytics solutions to analyze the sensitivity of clustering results with respect to changes of a selected region. We propose an interactive visual interface that enables simultaneous visualization of a) the variation in composition of identified clusters (i.e., their robustness), b) the variability in cluster membership for individual ensemble members, and c) the uncertainty in the spatial locations of identified trends. We demonstrate that our solution shows meteorologists how representative a clustering result is, and with respect to which changes in the selected region it becomes unstable. Furthermore, our solution helps to identify those ensemble members which stably belong to a given cluster and can thus be considered similar. In a real-world application case we show how our approach is used to analyze the clustering behavior of different regions in a forecast of "Tropical Cyclone Karl", guiding the user towards the cluster robustness information required for subsequent ensemble analysis.