Muenchen01
Kreuseler,M.; Nocke,T.; Schumann, H.: Integration of Clustering and Visualization Techniques for Visual Data Analysis. Proceedings of the 25th Annual Conference of the Gesellschaft für Klassifikation e.V., University of Munich, March, 2001, published in: O. Opitz, M. Schwaiger (editors): Exploratory Data Analysis in Empirical Research", Springer-Verlag, Heidelberg-Berlin, 2003, S. 119-132
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CompStat04
Nocke, T.; Schumann, H.; Böhm, U.: Methods for the Visualization of Clustered Climate Data. In: Computational Statistics (2004) 19(1), p.75-94
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Samo04
In this paper we describe the multi-run simulation experiment environment SimEnv and its application in quality assurance matters for computer models. SimEnv has been developed to provide key working techniques for experimenting with complex models. This includes a wide range of simulation and model output evaluation methods in combination with corresponding visualization techniques. The SimEnv framework facilitates the easy execution of multi-run model simulation experiments for standardized, pre-formed experiment types which represent different sampling strategies of the model’s input space. Further experiment types may easily be included, making SimEnv an open experimentation system. The coupling of models to the environment is supported by a simple interface, requiring only minimal model source code modifications. Uncertainty and sensitivity analyses are enabled in SimEnv by combining experiments available from the pool of predefined experiment types with interactive post-processing, applying sequences of related operators to both model output and reference data. Use of SimEnv as an experimental framework for models in global change research demonstrates the applicability of the approach to multi-input / multi-output problems with large amounts of spatio-temporal model output and emphasizes the importance of graphical result presentation and evaluation by appropriate visualization techniques.
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Visuelles Data Mining und Visualisierungsdesign für die Klimaforschung
Dissertation Thomas Nocke 2007, University of Rostock
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Samo07Poster
SIMENV - A FLEXIBLE FRAMEWORK FOR SENSITIVITY AND UNCERTAINTY ANALYSES OF LARGE-VOLUME MODEL OUTPUT
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Visual Exploration and Evaluation of Climate-related Simulation Data
Large, heterogeneous volumes of simulation data are calculated and stored in many disciplines, e. g. in climate and climate impact research. To gain insight, current climate analysis applies statistical methods and model sensitivity analyzes in combination with standard visualization techniques. However, there are some obstacles for researchers in applying the full functionality of sophisticated visualization, exploiting the available interaction and visualization functionality in order to go beyond data presentation tasks. In particular, there is a gap between available and actually applied multi-variate visualization techniques. Furthermore, visual data comparison of simulation (and measured) data is still a challenging task. Consequently, this paper introduces a library of visualization techniques, tailored to support exploration and evaluation of climate simulation data. These techniques are integrated into the easy-to-use visualization framework SimEnvVis - designed as a front-end user interface to a simulation environment - which provides a high level of user support generating visual representations.
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