Article

Towards dynamically adaptive weather analysis and forecasting in LEAD

Authors:

Mohan RamamurthyAuthors Info & Claims

ICCS'05: Proceedings of the 5th international conference on Computational Science - Volume Part II

Pages 624 - 631

https://doi.org/10.1007/11428848_81

Published: 22 May 2005 Publication History

Abstract

LEAD is a large-scale effort to build a service-oriented infrastructure that allows atmospheric science researchers to dynamically and adaptively respond to weather patterns to produce better-than-real time predictions of tornadoes and other “mesoscale” weather events. In this paper we discuss an architectural framework that is forming our thinking about adaptability and give early solutions in workflow and monitoring.

References

[1]

Teragrid. http://www.teragrid.org, 2005.

Google Scholar

[2]

Luiz DeRose, Ying Zhang, and Daniel A. Reed. Svpablo: A multi-language performance analysis system. In 10th International Conference on Computer Performance Evaluation - Modeling Techniques and Tools - Performance Tools'98, pages 352-355, 1998.

Crossref

Google Scholar

[3]

R. J. Donaldson. Vortex signature recognition by a doppler radar. Journal Applied Meteorology, 9:661-670, 1970.

Google Scholar

[4]

K. Droegemeier J. Kurose D. McLaughlin B. Philips M. Preston S. Sekelsky J. Brotzge, V. Chandresakar. Distributed collaborative adaptive sensing for hazardous weather detection, tracking, and predicting. In Computational Science - ICCS 2004: 4th International Conference. June.

Google Scholar

[5]

K. Kennedy, M. Mazina, J. Mellor-Crummey, K. Cooper, L. Torczon, F. Berman, A. Chien, Holly Dail, O. Sievert, D. Angulo, I. Foster, D. Gannon, L. Johnsson, C. Kesselman, J. Dongarra, S. Vadhiyar, R. Wolski, R. Aydt, and D. Reed. Toward a framework for preparing and executing adaptive grid programs. In Proceedings of the International Parallel and Distributed Processing Symposium Workshop (IPDPS NGS). IEEE Computer Society Press, April 2002.

Crossref

Google Scholar

[6]

Xiang Li, Rahul Ramachandran, John Rushing, and Sara Graves. Mining nexrad radar data: An investigative study. In American Meteorology Society annual meeting, 2004.

Google Scholar

[7]

Randy Ribler, Jeffrey Vetter, Huseyin Simitci, and Daniel Reed. Autopilot: Adaptive control of distributed applications. In 7th IEEE Symposium on High- Performance Distributed Computing, July 1998.

Crossref

Google Scholar

[8]

Rich Wolski. Dynamically forecasting network performance using the network weather service. Journal of Cluster Computing, 1:119-132, January 1998.

Crossref

Google Scholar

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Published In

ICCS'05: Proceedings of the 5th international conference on Computational Science - Volume Part II

May 2005

1088 pages

ISBN:3540260439

Editors:
Vaidy S. Sunderam
Department of Mathematics and Computer Science, Emory University, Atlanta, Georgia
,
Geert Dick Albada
Department of Mathematics and Computer Science, University of Amsterdam, Kruislaan 403, Amsterdam, SJ, The Netherlands
,
Peter A. Sloot
Faculty of Sciences, Section of Computational Science, University of Amsterdam, Kruislaan 403, Amsterdam, SJ, The Netherlands
,
Jack J. Dongarra
Computer Science Department, University of Tennessee, Kruislaan 403, TN, Knoxville, The Netherlands

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 22 May 2005

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Fujimoto R(2016)Research Challenges in Parallel and Distributed SimulationACM Transactions on Modeling and Computer Simulation10.1145/286657726:4(1-29)Online publication date: 2-May-2016
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