89th American Meteorological Society Annual Meeting

Tuesday, 13 January 2009: 4:00 PM
Developing geospatial decision support tools for a local NWS office and other regional decision makers
Room 121BC (Phoenix Convention Center)
J. Greg Dobson, UNC-Asheville, Asheville, NC; and T. Pierce and M. Phillips
Poster PDF (1.3 MB)
In recent years, the National Weather Service (NWS) and other regional Decision Makers (i.e. Emergency Managers, Community Planners, Media, General Public) whom have not traditionally utilized GIS have begun to fully integrate this and other Geospatial technologies into their daily operations. This is due, in part at least, to the evolution of these technologies, which have become much more user-friendly, web-based, fully customizable, more affordable, and in some cases, free to integrate. This presentation will focus on four specific GIS and Geospatial decision support tools developed for a local NWS Weather Forecast Office (WFO), which as a result also benefited many other Decision Makers in the region. The work was completed by a multi-agency collaboration directed by the UNC-Asheville Renaissance Computing Institute (RENCI) Engagement Center, with support from NCDC and the Greenville-Spartanburg NWS WFO located in Greer, SC.

The decision support tools included standard GIS applications (i.e. ArcIMS, ArcGIS Server), as well as customized Google Mashup and Open Source applications. The GIS applications provided critical, yet standard GIS information such as infrastructure, cadastral, emergency management, and physical terrain datasets, data that are not necessarily easily obtainable by the NWS. An entire ArcSDE database was developed for use with these applications as well as for in-house use with AWIPS and other programs. The Mashups and Open Source applications were an effort to provide easy and spatially based access to such information as live weather and transportation webcams, weather stations (regardless of type in which real-time readings are observed), and historical precipitation and stream flow data. Techniques, examples, and lessons learned will be highlighted in this presentation.

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