87th AMS Annual Meeting

Tuesday, 16 January 2007: 8:45 AM
Building a Partnership and Infrastructure for Predicting Tropical Cyclones, Surge and Innundation for the Southeastern United States: The SCOOP Project
216AB (Henry B. Gonzalez Convention Center)
Gerald J. Creager, Texas A&M Univ., College Station, TX
The Southeastern Universities Research Association (SURA), Coastal Ocean Observing and Prediction Program (http://scoop.sura.org/) is a multi-institution collaboration whose mission is to create a distributed network of shared resources to advance the sciences of environmental prediction and hazard planning for our nation's coasts. The SCOOP Program combines domain expertise in ocean modeling, atmospheric modeling, storm surge and innundation prediction, cyber infrastructure (CI), and distributed computing to provide a framework for data-driven operations associated with the prediction of tropical cyclones and their effects in the Southeastern U.S. and Gulf of Mexico. Beyond regional forecasts, however, SCOOP serves as a prototype and testbed for a reliable, flexible, distributed ensemble-based forecasting system.

The SCOOP CI is based on a service-oriented architecture (SOA). Components of the SCOOP SOA include data archives, transport facilities, a catalog for metadata management and data discovery, resource management, data vizualization, and cluster- and grid-based computational facilities. Web-based map services promote effective visualization using data available as Open Geospatial Consortium (OGC) compliant web services (www.opengeospatial.org).

SCOOP offers routine, production runs of the ADCIRC, ElCIRC, WaveWatch-3 and CH3D models, with multiple daily runs. When the National Hurricane Center releases a notification of a tropical storm, SCOOP partners begin the process of creating storm-specific synthetic winds and an ensemble of ADCIRC and other models. In concert with the regular initialization wind and pressure model data (NCEP WRF/NMM: National Center for Environmental Prediction; Weather Research and Forecasting model; Nonhydrostatic Mesoscale Model), we also use numerical forecast data from GFDL (Geophysical Fluid Dynamics Laboratory), and from other sources.

The SCOOP Program is leveraging its relationship with SURA and the SURAgrid, to facilitate a distributed, CI approach to model operations. SCOOP partners in the Southeastern U.S. coastal zones are providing computational resources to allow on-demand model execution in a Grid environment. Traditional cluster distributed resources are also employed, as is extensive use of virtualization for model packaging and assignment.

SCOOP supports the OpenIOOS.org website to provide visualization of the forecasts and observations. OpenIOOS is a technology demonstration focusing on open specifications and standards for visualization and data dissemination. OpenIOOS uses the OGC's suite of open web services, including Web Map, Web Feature and Web Coverage services, to present and share data with geographical information system users. Over the next year, SCOOP anticipates initiating a formal OGC Interoperability Experiment to explore the capabilities of the ocean observing and prediction communities to work more completely with existing GIS technologies.

SCOOP is also collaborating with the Marine Metadata Interoperability Project to create standards for data and metadata in ocean observing and prediction, by fostering the concept of controlled vocabularies and specific ontology representations of the data. This work builds on that of the Integrated Ocean Observing System (IOOS), Data Management and Communications (DMAC), and the Geosciences Network (GEON).

The NOAA Coastal Services Center and the Office of Naval Research provide funding for the SURA/SCOOP Program. The following institutions are collaborators within the SCOOP Program: Bedford Institute of Marine Science, the Gulf of Maine Ocean Observing System, Louisiana State University Center for Computation and Technology, MCNC, Renaissance Computing Institute, Texas A&M Research Foundation, the University of Alabama, Huntsville, the University of Florida, the University of Miami Center for Southeastern Tropical Advance Remote Sensing, the University of North Carolina, and the Virginia Institute of Marine Science.

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