Cluster ensembles for comparison of ocean model output with reference data
We introduce a novel approach to analyze differences and similarities between two spatiotemporal datasets. A machine learning technique called cluster ensembles enables scientists to consider many aspects of the temporal behavior of geophysical processes. Interactive visual exploration supports the interpretation and comparison of detected behavior.
Publication: Köthur, P., Sips, M., Dobslaw, H., Dransch, D. (2014): Visual Analytics for Comparison of Ocean Model Output with Reference Data: Detecting and Analyzing Geophysical Processes Using Clustering Ensembles. - IEEE Transactions on Visualization and Computer Graphics, 20, 12, p. 1893-1902.
In cooperation with GFZ Section 1.3: Earth System Modeling