Advances in data capture are helping to give (re)insurers an unparalleled insight into weather-related activity

Weather-related data is now available on a much more localized level than ever before. Rapidly expanding weather station networks are capturing terabytes of data across multiple weather-related variables on an almost real-time basis, creating a “ground-truth” clarity multiple times sharper than that available only a few years ago.

In fact, so hyperlocalized has this data become that it is now possible to capture weather information “down to a city street corner in some cases,” according to Earth Networks’ chief meteorologist Mark Hoekzema.

“The greater the resolution of the data, the more accurate the damage verification”— Mark Hoekzema, earth networks

This ground-level data is vital to the insurance industry given the potential for significant variations in sustained damage levels from one side of the street to the other during weather-related events, he adds.

“Baseball-sized hail can fall on one side of the street while just a block over there might be only pea-sized hail and no damage. Tornados and lightning can decimate a neighborhood and leave a house untouched on the same street. The greater the resolution of the data, the more accurate the damage verification.”

High-resolution perils

This granularity of data is needed to fuel the high-resolution modeling capabilities that have become available over the last five to ten years. “With the continued increase in computational power,” Hoekzema explains, “the ability to run models at very high resolutions has become commonplace. Very high-resolution inputs are needed for these models to get the most out of the computations.”

In July 2017, RMS teamed up with Earth Networks, capitalizing on its vast network of stations across North America and the Caribbean and reams of both current and historical data to feed into RMS HWind tropical cyclone wind field data products.

“Through our linkup with Earth Networks, RMS has access to data from over 6,000 proprietary weather stations across the Americas and Caribbean, particularly across the U.S.,” explains Jeff Waters, senior product manager of model product management at RMS. “That means we can ingest data on multiple meteorological variables in almost real time: wind speed, wind direction and sea level pressure.

“By integrating this ground-level data from Earth Networks into the HWind framework, we can generate a much more comprehensive, objective and accurate view of a tropical cyclone’s wind field as it progresses and evolves throughout the Atlantic Basin.”

Another key advantage of the specific data the firm provides is that many of the stations are situated in highly built-up areas. “This helps us get a much more accurate depiction of wind speeds and hazards in areas where there are significant amounts of exposure,” Waters points out.

According to Hoekzema, this data helps RMS gain a much more defined picture of how tropical cyclone events are evolving. “Earth Networks has thousands of unique observation points that are available to RMS for their proprietary analysis. The network provides unique locations along the U.S. coasts and across the Caribbean. These locations are live observation points, so data can be ingested at high temporal resolutions.”

Across the network

Earth Networks operates the world’s largest weather network, with more than 12,000 neighborhood-level sensors installed at locations such as schools, businesses and government buildings. “Our stations are positioned on sturdy structures and able to withstand the worst weather a hurricane can deliver,” explains Hoekzema.

Being positioned at such sites also means that the stations benefit from more reliable power sources and can capitalize on high-speed Internet connectivity to ensure the flow of data is maintained during extreme events.

In September 2017, an Earth Networks weather station located at the Naples Airport in Florida was the source for one of the highest-recorded wind gusts from Hurricane Irma, registering 131 miles per hour. “The station operated through the entire storm,” he adds.

“Through our linkup with Earth Networks … we can ingest data on multiple meteorological variables in almost real time” — Jeff waters, RMS

This network of stations collates a colossal amount of data, with Earth Networks processing some 25 terabytes of data relating to over 25 weather variables on a daily basis, with information refreshed every few minutes.

“The weather stations record many data elements,” he says, “including temperature, wind speed, wind gust, wind direction, humidity, dew point and many others. Because the stations are sending data in real time, Earth Networks stations also send very reliable rate information — or how the values are changing in real time. Real-time rate information provides valuable data on how a storm is developing and moving and what extreme changes could be happening on the ground.”

Looking further ahead

For RMS, such pinpoint data is not only helping ensure a continuous data feed during major tropical cyclone events but will also contribute to efforts to enhance the quality of insights delivered prior to landfall.

“We’re currently working on the forecasting component of our HWind product suite,” says Waters. “Harnessing this hyperlocal data alongside weather forecast models will help us gain a more accurate picture of possible track and intensity scenarios leading up to landfall, and allow users to quantify the potential impacts to their book of business should some of these scenarios pan out.”

RMS is also looking at the possibility of capitalizing on Earth Networks’ data for other perils, including flooding and wildfire, with the company set to release its North America Wildfire HD Models in the fall.

For Earth Networks, the firm is capitalizing on new technologies to expand its data reach. “Weather data is being captured by autonomous vehicles such as self-driving cars and drones,” explains Hoekzema.

“More and more sensors are going to be sampling areas of the globe and levels of the atmosphere that have never been measured,” he concludes. “As a broader variety of data is made available, AI-based models will be used to drive a broader array of decisions within weather-influenced industries.”