DocumentCode
22337
Title
Visual Analytics for Comparison of Ocean Model Output with Reference Data: Detecting and Analyzing Geophysical Processes Using Clustering Ensembles
Author
Kothur, Patrick ; Sips, Mike ; Dobslaw, Henryk ; Dransch, Doris
Author_Institution
GFZ German Res. Centre for Geosci., Potsdam, Germany
Volume
20
Issue
12
fYear
2014
fDate
Dec. 31 2014
Firstpage
1893
Lastpage
1902
Abstract
Researchers assess the quality of an ocean model by comparing its output to that of a previous model version or to observations. One objective of the comparison is to detect and to analyze differences and similarities between both data sets regarding geophysical processes, such as particular ocean currents. This task involves the analysis of thousands or hundreds of thousands of geographically referenced temporal profiles in the data. To cope with the amount of data, modelers combine aggregation of temporal profiles to single statistical values with visual comparison. Although this strategy is based on experience and a well-grounded body of expert knowledge, our discussions with domain experts have shown that it has two limitations: (1) using a single statistical measure results in a rather limited scope of the comparison and in significant loss of information, and (2) the decisions modelers have to make in the process may lead to important aspects being overlooked.
Keywords
data analysis; expert systems; geophysics computing; graphical user interfaces; learning (artificial intelligence); ocean waves; pattern clustering; statistical analysis; tides; clustering algorithms; clustering ensembles; data set differences; data set similarities; decision modelling; domain experts; expert knowledge; geographically referenced temporal profiles; geophysical process analysis; geophysical process detection; geophysical process representation; geospatial distribution; information loss; ocean currents; ocean model output; ocean model quality; parameterizations; reference data; statistical measure; statistical values; subjectivity reduction; temporal behavior; temporal profile aggregation; temporal profile cluster detection; temporal profiles; visual analytics; visual comparison; visual interface; Analytical models; Clustering algorithms; Computational modeling; Data models; Geospatial analysis; Oceans; Visualization; Ocean modeling; cluster ensembles; geospatial time; model assessment; series; visual analytics; visual comparison;
fLanguage
English
Journal_Title
Visualization and Computer Graphics, IEEE Transactions on
Publisher
ieee
ISSN
1077-2626
Type
jour
DOI
10.1109/TVCG.2014.2346751
Filename
6876007
Link To Document