Hurricane Ian and the Power of Data

Hurricanes – every utility company’s nightmare – are mercifully rare. In 2022, the National Oceanic and Atmoshperic Administration (NOAA) reported the year’s Atlantic tropical storm season as below-average with just 14 named storms and only three that made landfall in the U.S.

But when a hurricane hits, the impact can be catastrophic – and extremely costly. One of the year’s storms, Hurricane Ian, which hit Florida on September 28, became the third costliest weather disaster in U.S. history.

Preplanning and strategically positioning response crews are critical steps to reducing the duraction of outages when storms like Hurricane Ian occur – the more accurately this can be done, the better the outcome. But how can utilities predict where exactly the outages will hit in the days before landfall? Data-driven intelligence and machine learning is now advanced enough to achieve this.

DTN tested its Storm Risk solution during Hurricane Ian with promising results that demonstrated using data-driven intelligence to plan for outages before a storm.

 

The challenge of Ian

Hurricane Ian started as a tropical wave off Africa’s west coast, then moved across the Atlantic, reaching the south-east of Caribbean. It briefly reached Category 5 status before it finally made landfall in southern Florida as a high-end Category 4 hurricane with maximum sustained winds of around 145 mph.

Hurricane Ian’s predicted forecast track was expected to be parallel to the western coastline of Florida, but as DTN Risk Communicator Andrew Polk noted, a minor fluctuation in the forecast track of a tropical system can lead to significant changes to the storm impacts on businesses, emergency crews, service providers, and residents preparing for a hurricane.

“While the predicted track for Hurricane Ian within 24 hours was better than average,” Polk said, “the 36 through 72-hour forecasts were slightly below average further adding to the difficult decisions in hurricane preparedness.”

The storm produced catastrophic storm surge, damaging winds, and historic freshwater flooding across much of central and northern Florida.  More than 2 million customers were without power on the first evening and mandatory curfews were issued for communities along Florida’s west coast.

After briefly moving offshore in north-eastern Florida and downgrading to a tropical storm, Hurricane Ian quickly regained power and made a second landfall in South Carolina until it entirely dissipated days later in North Carolina.

NOAA concluded Hurricane Ian was responsible for over 150 direct and indirect deaths and over $112 billion in damage.

“A minor fluctuation in the forecast track of a tropical system can lead to significant changes to the storm impacts on businesses, emergency crews, service providers, and residents preparing for a hurricane.”
— DTN Risk Communicator Andrew Polk

 

Testing a solution

Hurricane Ian was the first major storm DTN used to test Storm Risk Analytics, a tool designed to help incident commanders at utilities make more informed and confident decisions about outage planning and response.

Using data-driven insights ahead of the storm’s landfall, the solution predicted nearly 4.6 million customers would experience outages during a week-long window when the storm was expected to be over land. This forecast, within 7% of the actual outage count, showcased the potential of data-driven insights – but didn’t stop there.

As the hurricane’s intensity and track changed, real-time information was continuously updated, with predictions delivered every six hours. A day before landfall, the revised prediction came within 3% of the actual 4 million outages, underscoring the tool’s adaptability to evolving weather conditions.

In the fight to minimize the cost and impact of storms, the more sources of information gathered, the better the outcome. At DTN we advise utility customers to use advanced weather intelligence, historic information and machine learning to give them the edge in mitigating as much damage as possible.

 

Conclusion

Extreme weather events, like Hurricane Ian, will always challenge the response and restoration services of utilities. But as utilities face the inevitable challenges posed by such events, innovative technological solutions offer a glimpse into a future where proactive planning can mitigate the impact on service areas and customers.

 

DTN Storm Risk Analytics consistently proves its worth demonstrating the power of data-driven intelligence in predicting, preparing for, and responding to extreme weather events. Learn more or request a demo with our team.