Towards Dynamically Adaptive Weather Analysis and Forecasting in LEAD*

Beth Plale1, Dennis Gannon1, Dan Reed2, Sara Graves3, Kelvin Droegemeier4, Bob Wilhelmson5, and Mohan Ramamurthy6

1Indiana University

2University of North Carolina, Chapel Hill

3University of Alabama Huntsville

4Oklahoma University

5University of Illinois Urbana Champaign

6UCAR, Unidata

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.

* LEAD is funded by the National Science Foundation under the following Cooperative 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.

LNCS 3515, pp. 624-631.

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