Hurricane Ian, a monstrous Category 4 storm that made landfall in the United States on Sept. 28, 2022, is credited as being one of the deadliest hurricanes to hit Florida since the “Labor Day” Hurricane of 1935.
While Hurricane Ian claimed over 140 lives, the vast network of emergency and recovery services that sprang into action likely saved countless more with their critical decisions about where people should evacuate, what support services would be affected by the storm and where to stage relief efforts.
To make these decisions, officials at dozens of locations across the state and country needed the best information possible about the storm, local capabilities and response resources. In particular, they needed to know where the storm would hit, what direction it would be coming from and how powerful a punch it would pack.
Above all, they needed this information in real time because a delay of even a couple of hours would be too late.
Fortunately, scientists at the University of North Carolina, Chapel Hill and The Water Institute of the Gulf were up to the challenge. Thanks, in part, to an early research allocation via ACCESS, the team was able to use the Pittsburgh Supercomputing Center’s Bridges-2 system to make storm surge predictions. These accurate, real-time predictions were shared with emergency management decision-makers, aiding them in evacuation and disaster-response decisions that may have saved lives.
Because PSC has set us up with a dedicated virtual machine we were able to test the latest forecast system developments in the months leading up to an event like Ian. It’s exciting that in its first real time usage, we were able to deliver a product that was usable [by the stakeholders].Zach Cobell, The Water Institute of the Gulf
You can read more about this story here (published Dec. 14, 2022): Storm Surge Model Runs on Bridges-2 in Real Time, Predicting Hurricane Ian’s Flooding Impacts.
Institution: PSC (Pittsburgh Supercomputing Center)
University: University of North Carolina, Chapel Hill and The Water Institute of the Gulf
Funding Agency: NSF
The science story featured here is enabled by the ACCESS program, which is supported by National Science Foundation grants #2138259, #2138286, #2138307, #2137603, and #2138296.