The use of high-performance and high-throughput computing for the fertilization of digital earth and global change studies

Abstract The study of global climate change seeks to understand: (1) the components of the Earth's varying environmental system, with a particular focus on climate; (2) how these components interact to determine present conditions; (3) the factors driving these components; (4) the history of global change and the projection of future change; and (5) how knowledge about global environmental variability and change can be applied to present-day and future decision-making. This paper addresses the use of high-performance computing and high-throughput computing for a global change study on the Digital Earth (DE) platform. Two aspects of the use of high-performance computing (HPC)/high-throughput computing (HTC) on the DE platform are the processing of data from all sources, especially Earth observation data, and the simulation of global change models. The HPC/HTC is an essential and efficient tool for the processing of vast amounts of global data, especially Earth observation data. The current trend involves running complex global climate models using potentially millions of personal computers to achieve better climate change predictions than would ever be possible using the supercomputers currently available to scientists.

[1]  Jason Cope,et al.  Grid-BGC: A Grid-Enabled Terrestrial Carbon Cycle Modeling System , 2005, Euro-Par.

[2]  Ben Shneiderman,et al.  The end of zero-hit queries: query previews for NASA’s Global Change Master Directory , 1999, International Journal on Digital Libraries.

[3]  Mark Gahegan,et al.  Exploratory geospatial analysis using GeoVISTA Studio: from a desktop to the Web , 2001, Proceedings of the Second International Conference on Web Information Systems Engineering.

[4]  Jianya Gong,et al.  Efficient global data model for the digital Earth , 2007, International Symposium on Multispectral Image Processing and Pattern Recognition.

[5]  D.A Bretherton,et al.  Running climate models on grids using G-Rex , 2008, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[6]  Ying Wang,et al.  Workload and task management of Grid-enabled quantitative aerosol retrieval from remotely sensed data , 2010, Future Gener. Comput. Syst..

[7]  Bruce Gittings,et al.  Towards a framework for High Performance Geocomputation: Handling Vector Topology within a Distributed Service Environment , 2000 .

[8]  Terence R. Smith,et al.  Developing an Infrastructure for Sharing Environmental Models , 2003 .

[9]  Tetsuya Sato,et al.  The Earth Simulator: roles and impacts , 2004, Parallel Comput..

[10]  W. Collins,et al.  Description of the NCAR Community Atmosphere Model (CAM 3.0) , 2004 .

[11]  Yong Xue,et al.  A high performance remote sensing retrieval application on an institutional desktop Grid , 2009, 2009 IEEE International Geoscience and Remote Sensing Symposium.

[12]  Jim Chesney,et al.  EOS High Rate Telemetry Processing Components , 1993 .

[13]  David M. Mark,et al.  Next-Generation Digital Earth: A position paper from the Vespucci Initiative for the Advancement of Geographic Information Science , 2008, Int. J. Spatial Data Infrastructures Res..

[14]  Dietmar Saupe,et al.  ATLAS2000 - Atlases of the future on the Internet , 1998, Comput. Graph..

[15]  Yanguang Wang,et al.  Preliminary study of Grid computing for remotely sensed information , 2005 .

[16]  Michael Stonebraker,et al.  An overview of the Sequoia 2000 project , 1992, Digest of Papers COMPCON Spring 1992.

[17]  Tetsuya Sato The earth simulator: Roles and impacts , 2004 .

[18]  Larry S. Davis,et al.  High performance computing for land cover dynamics , 1994, Proceedings of the 12th IAPR International Conference on Pattern Recognition, Vol. 2 - Conference B: Computer Vision & Image Processing. (Cat. No.94CH3440-5).

[19]  Terence L. van Zyl,et al.  The Sensor Web: systems of sensor systems , 2009, Int. J. Digit. Earth.

[20]  Mitsuo Yokokawa,et al.  The Earth Simulator system , 2003 .

[21]  José Manuel Gutiérrez,et al.  Complex Workflow Management of the CAM Global Climate Model on the GRID , 2008, ICCS.

[22]  Hiroshi Esaki,et al.  Live E! Project; Sensing the Earth with Internet Weather Stations , 2007, 2007 International Symposium on Applications and the Internet.

[23]  M. Nozawa,et al.  Model development for the global warming prediction by using the Earth Simulator , 2004, Proceedings. Seventh International Conference on High Performance Computing and Grid in Asia Pacific Region, 2004..

[24]  Mitsuo Yokokawa,et al.  The Development of the Earth Simulator , 2000 .

[25]  Yong Xue,et al.  GeoComputation 2009 , 2009, ICCS.

[26]  Franklin E. Kniskern The Navy/NOAA Joint Ice Center's role in the climate and global change program , 1991 .

[27]  Masaaki Shikada,et al.  Map renewal technique by using collaboration of GPS, GIS and remote sensing , 2007, 2007 IEEE International Geoscience and Remote Sensing Symposium.

[28]  Ying Wang,et al.  Grid-enabled high-performance quantitative aerosol retrieval from remotely sensed data , 2011, Comput. Geosci..

[29]  Lei Zheng,et al.  Remote Sensing Information Processing Grid Node with Loose-Coupling Parallel Structure , 2006, International Conference on Computational Science.

[30]  Robert Rank Enterprise IT support for NOAA archives , 2007, 2007 IEEE International Geoscience and Remote Sensing Symposium.

[31]  Zhangshi Yin Mapping EOS data on Web , 2000, IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment. Proceedings (Cat. No.00CH37120).

[32]  Shupeng Chen,et al.  Digital Earth in support of global change research , 2008, Int. J. Digit. Earth.

[33]  Zhou Huang,et al.  Toward an integrated framework for geosensor grid , 2010, Int. J. Digit. Earth.

[34]  A. Henderson‐sellers,et al.  Forty years of numerical climate modelling , 2001 .

[35]  Keith C. Clarke,et al.  GeoComputation in the Grid Computing Age , 2006, W2GIS.

[36]  John D. Moore,et al.  Applications of satellite imagery, remote sensing and computer visualizations: observing the earth visualizing the future , 2002, IEEE International Geoscience and Remote Sensing Symposium.

[37]  Keith C. Clarke,et al.  Geocomputation's future at the extremes: high performance computing and nanoclients , 2003, Parallel Comput..

[38]  Ying Wang,et al.  Quantitative Retrieval of Geophysical Parameters Using Satellite Data , 2008, Computer.

[39]  William Ribarsky,et al.  Building the visual Earth , 2002, SPIE Defense + Commercial Sensing.

[40]  Surya S. Durbha,et al.  A framework for semantic reconciliation of disparate earth observation thematic data , 2009, Comput. Geosci..

[41]  Huadong Guo,et al.  Overview and preliminary idea for building Digital Earth with Grid computing technology , 2008, Int. J. Digit. Earth.

[42]  Robert S. Chen,et al.  Cooperative Design, Development, and Management of Interdisciplinary Data to Support the Global Environmental Change Research Community , 2003 .

[43]  Yan Liu,et al.  SimpleGrid toolkit: Enabling geosciences gateways to cyberinfrastructure , 2009, Comput. Geosci..

[44]  Diego G. Loyola,et al.  Applications of neural network methods to the processing of Earth observation satellite data , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

[45]  C. Wang,et al.  A digital earth prototype system: DEPS/CAS , 2009, Int. J. Digit. Earth.

[46]  Stuart R. Phinn,et al.  Assessing the accuracy of high spatial resolution image data and derived products , 1997, IGARSS'97. 1997 IEEE International Geoscience and Remote Sensing Symposium Proceedings. Remote Sensing - A Scientific Vision for Sustainable Development.

[47]  Michael Stonebraker,et al.  Sequoia 2000: a next-generation information system for the study of global change , 1994, Proceedings Thirteenth IEEE Symposium on Mass Storage Systems. Toward Distributed Storage and Data Management Systems.

[48]  Richard R. Muntz,et al.  OASIS: an EOSDIS science computing facility , 1996, Optics & Photonics.