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

Earth Observation and Data Services Center

Indiana University Bloomington
University of North Carolina at Chapel Hill
University of Alabama in Huntsville
University of Oklahoma
University of Illinois at Urbana-Champaign

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

39 Scopus citations

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
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
Event	5th International Conference on Computational Science - ICCS 2005 - Atlanta, GA, United States Duration: May 22 2005 → May 25 2005

Access to Document

10.1007/11428848_81

Cite this

@article{b3fc0cbe2fa14c408e6b3a8ff2c263db,

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",

year = "2005",

doi = "10.1007/11428848\_81",

language = "English",

volume = "3515",

pages = "624--631",

journal = "Lecture Notes in Computer Science",

issn = "0302-9743",

publisher = "Springer Science and Business Media Deutschland GmbH",

number = "II",

note = "5th International Conference on Computational Science - ICCS 2005 ; Conference date: 22-05-2005 Through 25-05-2005",

}

TY - JOUR

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

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.

UR - https://www.scopus.com/pages/publications/25144438107

U2 - 10.1007/11428848_81

DO - 10.1007/11428848_81

M3 - Conference article

AN - SCOPUS:25144438107

SN - 0302-9743

VL - 3515

SP - 624

EP - 631

JO - Lecture Notes in Computer Science

JF - Lecture Notes in Computer Science

IS - II

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

Access to Document

Other files and links

Fingerprint

Cite this