Increasingly large climate model simulations are enhancing our understanding of the processes and causes of anthropogenic climate change, thanks to very large public investments in high-performance computing at national and international institutions. Various climate models implement mathematical approximations of nature in different ways, which are often based on differing computational grids. These complex, parallelized coupled system codes combine numerous complex submodels (ocean, atmosphere, land, biosphere, sea ice, land ice, etc.) that represent components of the larger complex climate system.
Collaboration across research, government, academic, and private sectors is integrating more than 70 scientific computing libraries and applications through a tailorable provenance framework, empowering scientists to exchange and examine data in novel ways.
For the past three years, a large analysis and visualization effort funded by the Department of Energys Office of Biological and Environmental Research (BER), the National Aeronautics and Space Administration (NASA), and the National Oceanic and Atmospheric Administration (NOAA) has brought together a wide variety of industry-standard scientific computing libraries and applications to create Ultra-scale Visualization Climate Data Analysis Tools (UV-CDAT) to serve the global climate simulation and observational research communities. To support interactive analysis and visualization, all components connect through a provenance application programming interface to capture meaningful history and workflow. Components can be loosely coupled into the framework for fast integration or tightly coupled for greater system functionality and communication with other components. The overarching goal of UV-CDAT is to provide a new paradigm for access to and analysis of massive, distributed scientific data collections by leveraging data architectures located throughout the world. The UV-CDAT framework addresses challenges in analysis and visualization and incorporates new opportunities, including parallelism for better efficiency, higher speed, and more accurate scientific inferences. Today, it provides more than 600 users access to more analysis and visualization products than any other single source.
Dean N. Williams, Time Bremer, Charles Doutriaux, John Patchett, Galen Shipman, Blake Haugen, Ross Miller, Brian Smith, Chad Steed, E. Wes Bethel, Hank Childs, Harinarayan Krishnan, Michael Wehner, Claudio T. Silva, Emanuele Santos, David Koop, Tommy Ellqvist, Huy T. Vo, Jorge Poco, Berk Geveci, Aashish Chaudhary, Andrew Bauer, Alexander Pletzer, Dave Kindig, Gerald L. Potter, Thomas P. Maxwell, “The Ultra-scale Visualization Climate Data Analysis Tools: Data Analysis and Visualization for Geoscience Data”, IEEE Special Issue: Cutting-Edge Research in Visualization, passed peer-review, due out in 2013.
Luca Cinquini, Daniel Crichton, Chris Mattmann, Gavin M. Bell, Charles Doutriaux, Bob Drach, Dean Williams, John Harney, Galen Shipman, Feiyi Wang, Philip Kershaw, Stephen Pascoe, Rachana Ananthakrishnan, Neill Miller, Estanislao Gonzalez, Sebastian Denvil, Mark Morgan, Sandro Fiore, Zed Pobre, Roland Schweitzer, “The Earth System Grid Federation: An Open Infrastructure for Access to Distributed Geospatial Data”, IEEE special issue of FGCS (Future Generation Computing Systems), under peer-review, due out in 2013.
Dean N. Williams, Ian T. Foster, Bryan Lawrence, Michael Lautenschlager, Don E. Middleton, “Earth System Grid Federation: Infrastructure to Support Climate Science Analysis as an International Collaboration”, Chapter in Data Intensive Science: Critchlow, Terence and Kleese-Van Dam, Kerstin, Editors. Chapman & Hall/Crc Computational Science, due out in 2013.
Brian Summa, Attilay Gyulassy, Peer-Timo Bremer, and Valerio Pascucci, “Interactive Data Exploration” Chapter in Data Intensive Science: Terence Critchlow, and Kerstin Kleese van Dam, Editors. Chapman & Hall/Crc Computational Science, due out in 2013.
Brian Smith, Daniel M Ricciuto, Peter E Thornton, Galen Shipman, Chad Steed, Dean Williams, and Michael Wehner 2013 International Conference on Computational Science, Fourth Workshop on Data Mining in Earth System Science (DMESS) 2013.
Emanuele Santos, Jorge Poco, Yaxing Wei, Shishi Liu, Bob Cook, Dean N. Williams, and Claudio T. Silva, “UV-CDAT: Analyzing Climate Datasets from a User’s Perspecive”, IEEE Computing in Science and Engineering: Visualization Corner, vol 15 (1), pp. 94-103, January 1, 2013. ISBN No. 1521-9615. DOI: http://doi.ieeecomputersociety.org/10.1109/MCSE.2013.15.
S. Kumar, V. Vishwanath, P. Carns, J.A. Levine, R. Latham, G. Scorzelli, H. Kolla, R. Grout, R. Ross, M.E. Papka, J. Chen, V. Pascucci. “Efficient data restructuring and aggregation for I/O acceleration in PIDX,” In Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis (SC12), IEEE Computer Society Press, pp. 50:1–50:11. 2012.
Shusen Liu; Levine, J.A.; Bremer, P.; Pascucci, V.; , “Gaussian mixture model based volume visualization,” Large Data Analysis and Visualization (LDAV), 2012 IEEE Symposium on , vol., no., pp.73-77, 14-15 Oct. 2012. BEST PAPER AWARD.
Wong, Pak Chung; Shen, Han-Wei; Pascucci, Valerio, Editors of special issue on “Extreme-Scale Visual Analytics,” Computer Graphics and Applications, IEEE , vol.32, no.4, July-Aug. 2012.
V. Pascucci, G. Scorzelli, B. Summa, P.-T. Bremer, A. Gyulassy, C. Christensen, S. Philip, S. Kumar. “The ViSUS Visualization Framework,” In High Performance Visualization: Enabling Extreme-Scale Scientific Insight, Chapman & Hall/CRC Computational Science, Ch. 19, Edited by E. Wes Bethel; Hank Childs, Lawrence Berkeley National Laboratory, Berkeley, California, USA; Charles Hansen, University of Utah, Salt Lake City, USA, Chapman and Hall/CRC, 2012.
Gyulassy, A.; Kotava, N.; Kim, M.; Hansen, C.D.; Hagen, H.; Pascucci, V.; , “Direct Feature Visualization Using Morse-Smale Complexes,” Visualization and Computer Graphics, IEEE Transactions on , vol.18, no.9, pp.1549-1562, Sept. 2012.
Harsh Bhatia, Shreeraj Jadhav, Peer-Timo Bremer, Guoning Chen, Joshua A. Levine, Luis Gustavo Nonato, and Valerio Pascucci. Flow Visualization with Quantified Spatial and Temporal Errors using Edge Maps. IEEE Transactions on Visualization and Computer Graphics (TVCG), vol. 18, no. 9, pp. 1383-1396, Sept. 2012.
Widanagamaachchi, W.; Christensen, C.; Bremer, P.-T.; Pascucci, V.; , “Interactive exploration of large-scale time-varying data using dynamic tracking graphs,” Large Data Analysis and Visualization (LDAV), 2012 IEEE Symposium on , vol., no., pp.9-17, 14-15 Oct. 2012. BEST PAPER AWARD FINALIST.
Tierny, J.; Pascucci, V.; , “Generalized Topological Simplification of Scalar Fields on Surfaces,” Visualization and Computer Graphics, IEEE Transactions on , vol.18, no.12, pp.2005-2013, Dec. 2012.
E. Santos, D. Koop, T. Maxwell, C. Doutriaux, T. Ellqvist, G. Potter, J. Freire, D. N. Williams, and C. T. Silva, “Designing a provenance-based climate data analysis application.” In Provenance and Annotation of Data and Processes, vol. 7525 of Lecture Notes in Computer Science, Springer, 2012.
Chad A. Steed, Galen Shipman, Peter Thornton, Daniel Ricciuto, David Erickson, and Marcia Branstetter, “Practical Application of Parallel Coordinates for Climate Model Analysis,” In Proceedings of the International Conference on Computer Science-Workshop on Data Mining in Earth System Science. June 2012.
Williams, S., Petersen, M., Hecht, M., Maltrud, M., Patchett, J., Ahrens, J. and Hamann, B. (2012), “Interface Exchange as an Indicator for Eddy Heat Transport”, Computer Graphics Forum, 31: 1125-1134. doi: 10.1111/j.1467-8659.2012.03105.x.
Williams, S., Hecht, M., Petersen, M., Strelitz, R., Maltrud, M., Ahrens, J., Hlawitschka, M. and Hamann, B. (2011), “Visualization and Analysis of Eddies in a Global Ocean Simulation,” Computer Graphics Forum, 30: 991-1000. doi: 10.1111/j.1467-8659.2011.01948.x
Williams, S.; Petersen, M.; Bremer, P.-T.; Hecht, M.; Pascucci, V.; Ahrens, J.; Hlawitschka, M.; Hamann, B.; , “Adaptive Extraction and Quantification of Geophysical Vortices,” Visualization and Computer Graphics, IEEE Transactions on , vol.17, no.12, pp.2088-2095, Dec. 2011oi: 10.1109/TVCG.2011.162