Tracking Matthew – The Devil in the Detail

Hurricane Matthew aptly demonstrated that slight shifts in a tropical cyclone’s timing, track, and wind field extent can make a huge difference in its overall impact to exposures at risk.

As Matthew bore down on the U.S. after devastating Haiti, it had the makings of another industry-altering event. Had the storm made landfall along the Florida coast, likely as a category 4 storm, insured losses could have been ten times larger than the $1.5 billion to $5 billion range that is currently projected by RMS.

Given that Matthew’s strongest winds were confined to a small area within its inner core, its path proved to be critical. A difference in track of just a few dozen miles translated to a material reduction in wind impacts along the coastline and into interior portions of Florida. The fact that the storm stayed just offshore helped to minimize overall damages significantly throughout the state and the (re)insurance industry at large.

Storms like Matthew signify the importance of being able to track dynamic tropical cyclone characteristics, position, and damage potential accurately as the storm unfolds in order to help communities and businesses adequately prepare and respond.

There is a wealth of public and private data to inform real-time tropical cyclone wind field assessments and event response processes, but some data provides more insight than others. Commonly used public sources include worldwide and national tropical cyclone centers, numerical weather prediction models, and numerous forecast offices or research organizations.

In the U.S., one of the better-known public sources for tropical cyclone data is the National Hurricane Center (NHC) in Miami, Florida. A branch of the National Oceanic and Atmospheric Administration, the NHC provides a range of tropical cyclone data, tools, analyses, and forecasts to inform real-time tropical cyclone assessments in the Atlantic and East Pacific basins.

There are also private sources of tropical cyclone wind field data that span a wide breadth and depth of useful information, few of which provide insight that goes beyond what is provided by the NHC.

One exception to that is HWind, formally known as HWind Scientific. Acquired by RMS in 2015, the provider of tropical cyclone wind field data develops observation-based data products for both real-time and historical wind field analyses in the Atlantic, East Pacific, and Central Pacific Basins.

During a real-time event, HWind provides regularly-derived snapshots of wind field conditions leading up to and following landfall, as well as post-event wind hazard footprints 1-3 days after the storm impacts land. Each analysis is informed by access to an observational data network spanning more than 30 land, air, and sea-based platforms, all of which are subject to stringent independence and quality control testing.

On average, tens of thousands of observations are used for each event, depending on the availability and the storm’s proximity to land.

HWind products tend to represent wind hazard characteristics with more frequency, accuracy, and granularity than many publically available sources, including the NHC.

From a frequency perspective, HWind snapshots are created and refreshed as often as every three hours throughout the event as soon as aircraft reconnaissance begins, allowing users to track changing storm conditions as the event evolves.

The data also discerns important factors such as storm location with a high degree of granularity and precision, often correcting for center-position errors and biases that are evident in some observational data sources, or adjusting wind speeds to account for the impact of terrain.

Each snapshot also includes a high-resolution representation of local wind speeds and hazard bands.

During events like Hurricane Matthew and the events that are yet to come, private sources like HWind can provide additional and timely insight needed to understand the aspects of wind hazard that matter most to a (re)insurer’s business and event response processes.

Using this information, risk managers can more accurately quantify exposure accumulations at risk during or immediately following landfall. Crucially, this allows them to anticipate the potential severity of loss claims with more precision, and position claims adjusters or recovery assets more effectively.

Collectively, it could mean the difference between being proactive vs. reactive when the next event strikes.