A Weather Ready Nation Decision Support Tool for Protecting Vulnerable Communities

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Tuesday, 6 January 2015: 4:45 PM
221A-C (Phoenix Convention Center - West and North Buildings)
Troy M. Anselmo, SAIC, Newport News, VA; and J. B. Frenzer

Handout (1.4 MB)

SAIC has developed a prototype decision support tool which demonstrates the use of new technologies to fuse and analyze large streams of disparate data. Our prototype was intended to raise the level of situational awareness for first responders during high impact weather events. Our prototype decision support system combined census data with real-time weather observations, radar data and tweets to identify at-risk residents during weather-related power outages. Our case study was based on the severe ice storm that occurred in Dallas, TX December 5-7, 2013. We identified areas where a significant number of residents were considered to be “at-risk” during power outages. Using the prototype system, we considered areas with confirmed power outages and dangerous wind chill values as areas with a significant threat. Census data were used to locate concentrations of elderly residents on a 1 km grid in the greater Dallas area. Radar data products were displayed on the same grid to indicate the areas with the highest ice accumulations (a potential precursor to power outages) combined with METAR observations of wind and temperature. Color coding was used to show the progressive threat of ice and wind chills. We took advantage of twitter to confirm in-situ power outages. The application was designed to process the METAR data, radar data, and tweets in real-time using a powerful data streaming tool from Amazon, named Kinesis. The historical data were fed into Kinesis in the same relative time sequences at it was collected, however, Kinesis was able to process significantly more data hours in a single clock than real-time. This demonstration addressed the ability to process data with high velocities, volume, and variety in near real-time. The value of this case study will be presented with emphasis on applicability to potential at-risk populations during any life-threatening hazard, such as flooding, wildfires, poor air quality, hurricanes, etc.