EnvirVis: Workshop on Visualisation in Environmental Sciences
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Browsing EnvirVis: Workshop on Visualisation in Environmental Sciences by Author "Böttinger, Michael"
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Item Interactive Visual Analysis of Regional Time Series Correlation in Multi-field Climate Ensembles(The Eurographics Association, 2023) Evers, Marina; Böttinger, Michael; Linsen, Lars; Dutta, Soumya; Feige, Kathrin; Rink, Karsten; Zeckzer, DirkSpatio-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.Item Topology-based Feature Detection in Climate Data(The Eurographics Association, 2019) Kappe, Christopher P.; Böttinger, Michael; Leitte, Heike; Bujack, Roxana and Feige, Kathrin and Rink, Karsten and Zeckzer, DirkThe weather and climate research community needs to analyze increasingly large datasets, mostly obtained by observations or produced by simulations. Ensemble simulation techniques, which are used to capture uncertainty, add a further dimension to the multivariate time-dependent 3D data, even tightening the challenge of finding relevant information in the data for answering the respective research questions. In this paper we propose a topology-based method to support the visual analysis of climate data by detecting regions with particularly strong local minima or maxima and highlighting them with colored contours. Combined with preceding clustering of the data fields, typical spatial patterns characterizing the climate variability are detected and visualized. We demonstrate the utility of our method with a study of global temperature anomalies of a 150-years ensemble simulation consisting of 100 members.