Combine NOAA weather data with user configurations to provide first responders with a streamlined view of future risk that simultaneously provides alerts about changing conditions likely to impact the response.
On 16th February, 2015, a CSX Transportation train hauling 107 tanks of Bakken crude oil from North Dakota to Virginia derailed in Mount Carbon, West Virginia. At the time of the accident—with temperatures of 15 °F and 8 inches of recent snow—West Virginia was under a winter storm warning. The crash ignited the spilled oil, caused subsequent violent fireball explosions, and contaminated local streams and water supply. The spill, fire, and eruptions destroyed one home, forced the evacuation of hundreds of families and caused the temporary shutdown of two nearby water treatment plants.
Train Wreck Model
The demo integrates Rapid Refresh (RAP) data into the existing FirstNet dashboard to provide near real-time data on current environmental conditions and probable weather forecasts. The data is pulled from the Softlayer cloud and processed through business rules via Bluemix which then supplies alerts to the user’s dashboard.
The power of the RAP data lies in its ability to forecast over 300 weather-related variables, such as, wind speed and direction, precipitation, surface visibility and temperature fluctuations. When combined with IBM’s SoftLayer and Bluemix services users will be able to solve countless problems that could potentially save lives.
First Responder's Considerations
Location of inhabited communities in relation to the disaster
Location of rivers, lakes, and other water resources
Location of livestock and wildlife
Past, current, and future weather conditions that will impact all of the above
The Value of NOAA Big Data
Integration of NOAA’s RAP data into existing alert systems, equips decision-makers (in this case, the Incident Commander and first response team) with the highest degree of situational awareness and confidence in preparing for, responding to and mitigating the impact of a real-life emergency.