Towards dynamically adaptive weather analysis and forecasting in LEAD

Beth Plale; Dennis Gannon; Dan Reed; Sara Graves; Kelvin Droegemeier; Bob Wilhelmson; Mohan Ramamurthy

doi:10.1007/11428848_81

Towards dynamically adaptive weather analysis and forecasting in LEAD

Beth Plale, Dennis Gannon, Dan Reed, Sara Graves, Kelvin Droegemeier, Bob Wilhelmson, Mohan Ramamurthy

Research output: Contribution to journal › Conference article › peer-review

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.

Original language	English (US)
Pages (from-to)	624-631
Number of pages	8
Journal	Lecture Notes in Computer Science
Volume	3515
Issue number	II
DOIs	https://doi.org/10.1007/11428848_81
State	Published - 2005
Externally published	Yes
Event	5th International Conference on Computational Science - ICCS 2005 - Atlanta, GA, United States Duration: May 22 2005 → May 25 2005

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

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10.1007/11428848_81

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title = "Towards dynamically adaptive weather analysis and forecasting in LEAD",

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.",

author = "Beth Plale and Dennis Gannon and Dan Reed and Sara Graves and Kelvin Droegemeier and Bob Wilhelmson and Mohan Ramamurthy",

note = "Funding Information: 7LEAD is funded by the National Science Foundation under the following Coop-erative Agreements: ATM-0331594 (Oklahoma), ATM-0331591 (Colorado State), ATM-0331574 (Millersville), ATM-0331480 (Indiana), ATM-0331579 (Alabama in Huntsville), ATM03-31586 (Howard), ATM-0331587 (UCAR), and ATM-0331578 (Illinois at Urbana-Champaign). CASA is funded by the NSF under Cooperative Agreement ECE-0313747 to the University of Massachusetts at Amherst; 5th International Conference on Computational Science - ICCS 2005 ; Conference date: 22-05-2005 Through 25-05-2005",

year = "2005",

doi = "10.1007/11428848_81",

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T1 - Towards dynamically adaptive weather analysis and forecasting in LEAD

AU - Plale, Beth

AU - Gannon, Dennis

AU - Reed, Dan

AU - Graves, Sara

AU - Droegemeier, Kelvin

AU - Wilhelmson, Bob

AU - Ramamurthy, Mohan

N1 - Funding Information: 7LEAD is funded by the National Science Foundation under the following Coop-erative Agreements: ATM-0331594 (Oklahoma), ATM-0331591 (Colorado State), ATM-0331574 (Millersville), ATM-0331480 (Indiana), ATM-0331579 (Alabama in Huntsville), ATM03-31586 (Howard), ATM-0331587 (UCAR), and ATM-0331578 (Illinois at Urbana-Champaign). CASA is funded by the NSF under Cooperative Agreement ECE-0313747 to the University of Massachusetts at Amherst

PY - 2005

Y1 - 2005

N2 - 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.

AB - 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.

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JF - Lecture Notes in Computer Science

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T2 - 5th International Conference on Computational Science - ICCS 2005

Y2 - 22 May 2005 through 25 May 2005

ER -

Towards dynamically adaptive weather analysis and forecasting in LEAD

Abstract

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