4.3 StormSurgeViz: a Community-based Approach for Data Analysis and Visualization of Storm Surge and Wave Forecasts

Wednesday, 13 January 2016: 9:00 AM
Room 342 ( New Orleans Ernest N. Morial Convention Center)
Brian Blanton, University of North Carolina, Chapel Hill, NC; and R. Luettich

There are several coastal storm surge, inundation, and waves prediction systems operating for risk-prone areas of the eastern US and Gulf coasts, based on models such as ADCIRC, FVCOM, ROMS, POM, and SLOSH.  In most cases, the models require substantial computational resources, particularly in a forecast, real-time mode.  The variety of models and their output makes consistent access, analysis and visualization a challenge, particularly for providing such information in real-time to decision-makers. However, community standards, conventions, and software components have emerged that can provide seamless interaction with coastal models and enable rapid development of applications that substantially bridge the gap between research and operations.  

In this presentation, we demonstrate StormSurgeViz, a project funded by NOAA's Joint Hurricane Testbed 2013 program. We describe the "community on-boarding" process that enables models to participate in collectively providing real-time information to end-users.  StormSurgeViz is a MATLAB-based graphical user interface developed as an end-user application for visualizing and analyzing storm surge and wave forecasts.  There are four essential building blocks to StormSurgeViz: 1) THREDDS/OPeNDAP servers to host and archive model output; 2) NetCDF Climate and Forecast metadata conventions and the extension for unstructured grids (UGRID); 3) NCTOOLBOX, a community-based MATLAB toolbox that accesses common data model datasets using NetCDF-Java as the data access layer; and 4) NcML, the netCDF markup language.  StormSurgeViz can access, display, and analyze any model dataset available on a THREDDS server and with the minimum, community-defined metadata description, independent of the models' implementation details (such as the nature of the underlying computational grid).

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