CASA and LEAD: adaptive cyberinfrastructure for real-time multiscale weather forecasting

Two closely linked projects aim to dramatically improve storm forecasting speed and accuracy. CASA is creating a distributed, collaborative, adaptive sensor network of low-power, high-resolution radars that respond to user needs. LEAD offers dynamic workflow orchestration and data management in a We...

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Veröffentlicht in:	Computer (Long Beach, Calif.) Calif.), 2006-11, Vol.39 (11), p.56-64
Hauptverfasser:	Plale, B., Gannon, D., Brotzge, J., Droegemeier, K., Kurose, J., McLaughlin, D., Wilhelmson, R., Graves, S., Mohan Ramamurthy, Clark, R.D., Yalda, S., Reed, D.A., Joseph, E., Chandrasekar, V.
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Atmospheric modeling CASA Computer vision Convergence Data mining Decision support systems Dynamical systems Dynamics Global information infrastructure Grid computing Information technology LEAD Meteorological radar Multiscale modeling Networks Predictive models Probability Radar Radar remote sensing Real time Sensors Storms System-evel science Weather forecasting Web services Wind Workflow
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Beschreibung
Zusammenfassung:	Two closely linked projects aim to dramatically improve storm forecasting speed and accuracy. CASA is creating a distributed, collaborative, adaptive sensor network of low-power, high-resolution radars that respond to user needs. LEAD offers dynamic workflow orchestration and data management in a Web services framework designed to support on-demand, real-time, dynamically adaptive systems
ISSN:	0018-9162 1558-0814
DOI:	10.1109/MC.2006.375