Interactive Visual Analysis of Regional Time Series Correlation in Multi-field Climate Ensembles

Abstract

Spatio-temporal multi-field data resulting from ensemble simulations are commonly used in climate research to investigate possible climatic developments and their certainty. One analysis goal is the investigation of possible correlations among different spatial regions in the different fields to find regions of related behavior. We propose an interactive visual analysis approach that focuses on the analysis of correlations in spatio-temporal ensemble data. Our approach allows for finding correlations between spatial regions in different fields. Detection of clusters of strongly correlated spatial regions is supported by lower-dimensional embeddings. Then, groups can be selected and investigated in detail, e.g., to study the temporal evolution of the selected group, their Fourier spectra or the distribution of the correlations over the different ensemble members. We apply our approach to selected 2D scalar fields of a large ensemble climate simulation and demonstrate the utility of our tool with several use cases.