ICCS 2012: “Empowering Science through Computing”

Third Worskhop on
Data Mining in Earth System Science (DMESS 2012)

Co-convened by: Forrest M. Hoffman, Jitendra Kumar, J. Walter Larson, Richard Tran Mills, and Miguel D. Mahecha

June 4–6, 2012

Papers and Abstracts from the Workshop:

• Prabhat, Oliver Rübel, Surendra Byna, Kesheng Wu, Fuyu Li, Michael Wehner, and Wes Bethel (2012), TECA: A Parallel Toolkit for Extreme Climate Analysis, Procedia Comput. Sci., 9, 866–876, doi:10.1016/j.procs.2012.04.093.
• Steed, Chad A., Galen Shipman, Peter Thornton, Daniel Ricciuto, David Erickson, and Marcia Branstetter (2012), Practical Application of Parallel Coordinates for Climate Model Analysis, Procedia Comput. Sci., 9, 877–886, doi:10.1016/j.procs.2012.04.094.
• Kumar, Jitendra, Bjørn-Gustaf J. Brooks, Peter E. Thornton, and Michael C. Dietze (2012), Sub-daily Statistical Downscaling of Meteorological Variables Using Neural Networks, Procedia Comput. Sci., 9, 887–896, doi:10.1016/j.procs.2012.04.095.
• Zscheischler, Jakob, Miguel D. Mahecha, and Stefan Harmeling (2012), Climate Classifications: the Value of Unsupervised Clustering, Procedia Comput. Sci., 9, 897–906, doi:10.1016/j.procs.2012.04.096.
• Vatsavai, Ranga R., and Jordan Graesser (2012), Probabilistic Change Detection Framework for Analyzing Settlement Dynamics Using Very High-resolution Satellite Imagery, Procedia Comput. Sci., 9, 907–916, doi:10.1016/j.procs.2012.04.097.
• Larson, J. Walter (2012), Visualizing Climate Variability with Time-Dependent Probability Density Functions, Detecting It Using Information Theory, Procedia Comput. Sci., 9, 917–926, doi:10.1016/j.procs.2012.04.098.

Workshop Description:

Spanning many orders of magnitude in time and space scales, Earth science data are increasingly large and complex, and often represent very long time series, making such data difficult to analyze, visualize, interpret, and understand. Moreover, advanced electronic data storage technologies have enabled the creation of large repositories of observational data, while modern high performance computing capacity has enabled the creation of detailed empirical and process-based models that produce copious output across all these time and space scales. The resulting “explosion” of heterogeneous, multi-disciplinary Earth science data has rendered traditional means of integration and analysis ineffective, necessitating the application of new analysis methods and the development of highly scalable software tools for synthesis, comparison, and visualization. This workshop explores various data mining approaches to understand Earth science data, emphasizing the technological challenges associated with utilizing very large and long time series geospatial data sets. Especially encouraged are original research papers describing applications of data mining methods—including cluster analysis, empirical orthogonal functions (EOFs), genetic algorithms, neural networks, and other machine-learning techniques—that support analysis and discovery in climate, water resources, geology, ecology, and environmental sciences research.

Previous workshops:

• Second Workshop on Data Mining in Earth System Science (ICCS 2011)
• First Workshop on Data Mining in Earth System Science (ICCS 2009)

Program Committee Members:

• Michael W. Berry (Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, Tennessee, USA)
• Auroop R. Ganguly (Department of Civil and Environmental Engineering, Northeastern University, Boston, Massachusetts, USA)
• William W. Hargrove (Eastern Forest Environmental Threat Assessment Center, USDA Forest Service, Asheville, North Carolina, USA)
• Forrest M. Hoffman (Computational Earth Sciences Group, Computer Science & Mathematics Division and Ecosystem Science Group, Environmental Sciences Division and Oak Ridge Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA; Department of Earth System Science, University of California, Irvine, California, USA)
• Jian Huang (Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, Tennessee USA)
• Robert L. Jacob (Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, Illinois, USA)
• Ashu Jain (Department of Civil Engineering, Indian Institute of Technology, Kanpur, INDIA)
• Chris Johnson (Department of Computer Science, University of Wisconsin - Eau Claire, Eau Claire, Wisconsin, USA)
• Jitendra Kumar (Computational Earth Sciences Group, Computer Science & Mathematics Division and Ecosystem Science Group, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA)
• Vipin Kumar (Department of Computer Science and Engineering, University of Minnesota, Minneapolis, Minnesota, USA)
• J. Walter Larson (Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, Illinois, USA; Computation Institute, University of Chicago, Chicago, Illinois, USA; and School of Computer Science, The Australian National University, Canberra ACT 0200, AUSTRALIA)
• Miguel D. Mahecha (Biogeochemical Model-Data Integration Group, Max Planck Institute for Biogeochemistry, Jena, GERMANY)
• Kumar Mahinthakumar (Department of Civil, Construction, & Environmental Engineering, North Carolina State University, Raleigh, North Carolina, USA)
• Richard T. Mills (Computational Earth Sciences Group, Computer Science & Mathematics Division and Aquatic Sciences Group, Environmental Sciences Division and Oak Ridge Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA; and Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, Tennessee, USA)
• Habib N. Najm (Sandia National Laboratories, Livermore, California, USA)
• Markus Reichstein (Biogeochemical Model-Data Integration Group, Max Planck Institute for Biogeochemistry, Jena, GERMANY)
• Karsten Steinhaeuser (Department of Computer Science and Engineering, University of Minnesota, Minneapolis, Minnesota, USA)
• R. Raju Vatsavai (Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA)

Paper Submission:

Authors are invited to submit manuscripts of up to 10 (A4) pages reporting unpublished, mature, and original research and recent developments/theoretical considerations in applications of data mining to Earth sciences by January 9, 2012 January 20, 2012 February 10, 2012. Accepted papers will printed in the conference proceedings published by Elsevier Science in the open-access Procedia Computer Science series. Submitted papers must be camera-ready and formatted according to the rules of Procedia Computer Science. Submission implies the willingness of at least one of the authors to register and present the paper.

Please submit your paper via the conference website at http://www.iccs-meeting.org/iccs2012/papers/upload.php and select the workshop “Third Data Mining in Earth System Science (DMESS 2012)”.

Important Dates:

Contact:

Contribution to Computational Science:

This workshop will contribute to the field of Computational Science by creating a forum for original research papers and presentations from leading computational and earth scientists who are applying data mining techniques on advanced computing platforms (HPC systems, clusters, grids and clouds) to distill knowledge from the massive—and growing—data sets created by the earth science community.

About the Workshop Co-conveners:

Forrest M. Hoffman has been developing software for data mining using high performance computing (HPC) and apply data mining methods to problems in landscape ecology, remote sensing, and climate analyses for more than a decade. Forrest co-convened the GeoComputation workshop at ICCS 2009 and the previous Second Workshop on Data Mining in Earth System Science at ICCS 2011. Forrest's publication list is available at http://www.climatemodeling.org/~forrest/pubs.

Jitendra Kumar conducts research at the intersection of high performance computing, environmental and earth sciences, and systems analysis and data mining. His research entails data mining, large-scale global optimization, computational hydrology and hydrogeology, and development of parallel algorithms for large-scale supercomputers.

J. Walter Larson is a leader in the development of coupling software for simulation of complex systems, most notably as the co-lead developer of the Model Coupling Toolkit (http://mcs.anl.gov/mct) and as one of the developers of the coupling infrastructure in the Community Climate System Model. He has published papers in the fields of mathematical and plasma physics, climate, data assimilation, and computational science (http://people.physics.anu.edu.au/~jwl105/Pubs).

Richard Tran Mills has conducted research in high performance computing, software for iterative solution of sparse algebraic systems, computational hydrology, geospatiotemporal data mining, computational hydrology, and execution context-aware software. He has contributed to the PETSc PDE-solver framework and is a principal author of the subsurface flow and reactive transport code PFLOTRAN, which is being used on the largest-scale supercomputers in the world to study topics such as radionuclide transport and geologic carbon sequestration.

Miguel D. Mahecha conducts research on ecosystem-atmosphere interactions and related topics. He investigates the potential of novel data mining and time series analysis methods for exploring multidimensional spatiotemporal Earth observations and in situ monitoring data. He is particularly interested in nonlinear dimensionality reduction, multivariate time series analysis, and data assimilation. Publications available at https://www.bgc-jena.mpg.de/bgc-mdi/index.php/Main/MiguelMahecha.