By
Sierra Steele
NASA Disasters Science Writer Intern
Published

Tornadoes, tsunamis, and hurricanes are considered perils worthy of their own disaster movies. Hollywood producers might be missing a more likely threat within the genre: hailstorms. In the real world, hailstorms are much more likely to form than tornadoes, resulting in $10’s of billions in damage each year worldwide. In the U.S. alone, NOAA estimates that 152 billion-dollar weather disasters from 1980 to 2021 resulted from severe storms, and the majority of these disasters included significant hail damage.

Kristopher Bedka. Credits: Kristopher Bedka / NASA

The NASA Applied Sciences A.37 ROSES research project, “Hailstorm Risk Assessment Using Space-Borne Remote Sensing Observations and Reanalysis Data,” focuses on developing satellite-based tools to better understand and track where hail has most often occurred, and to identify areas damaged by hail. Kris Bedka serves as principal investigator on the project, leading the “Satellite Mapping and Analysis of Severe Hailstorms” team. For those who love deciphering NASA acronyms, that one is apt. They are the SMASH team.

 

Reinsurance Industry and CatModel Development

The project is a partnership between NASA’s Langley Research Center and NASA’s Marshall Space Flight Center, Willis Towers Watson (WTW) reinsurance, international partners in South America and Germany, and several academic partners throughout the United States.

Reinsurance is insurance for insurance companies. Insurers carry cash to make payouts, but if a disaster causes billions of dollars in damage, insurance companies don’t always have enough money on hand to pay everyone all at once. Reinsurance companies use catastrophe models (CatModels) to assess risk to insurers.

WTW, now Gallagher Re, asked Bedka’s team to develop a long-term climatology of hailstorm pattern detections using data from a variety of satellites, including Meteosat Second Generation (MSG), Tropical Rainfall Measuring Mission (TRMM), and Global Precipitation Measurement Mission (GPM) over South Africa.

“We delivered a 14-year database of observations from different satellites to them, and in March 2021, they released an operational hailstorm CatModel,” explains Bedka.

A map of the relative hailstorm frequency over South Africa derived by Willis Re using SMASH team satellite datasets including overshooting cloud top detections from a 14-year database of 15-minute resolution Meteosat Second Generation infrared brightness temperatures, TRMM and GPM hailstorm detections, and also ERA5 reanalysis data. Eastern South Africa is most often affected by severe hailstorms based on this analysis. Credits: Wills Re / Kris Bedka
A map of the relative hailstorm frequency over South Africa derived by Willis Re using SMASH team satellite datasets including overshooting cloud top detections from a 14-year database of 15-minute resolution Meteosat Second Generation infrared brightness temperatures, TRMM and GPM hailstorm detections, and also ERA5 reanalysis data. Eastern South Africa is most often affected by severe hailstorms based on this analysis. Credits: Wills Re

South Africa is a growing insurance market where hail damage is relatively frequent. Without a long-term and seamless weather radar network, it is difficult to determine the climatology of hailstorm frequency and severity. Bedka explains that “ground-based radars scan about every five minutes and create a picture of storm structure and where hail is probably occurring.” The United States is fortunate to generally know where hail has most often occurred due to its comprehensive radar network and human severe-storm spotters, but the same can’t be said for all regions worldwide.

Before the project started, the team developed hail CatModels over Europe and Australia, resulting in an Application Readiness Level (ARL) 7 – which means the prototype functionality was demonstrated and used for decision-making. The team continued to improve their modeling and storm detection techniques with the South Africa dataset, and by the end of last year, WTW’s clients were using the South Africa CatModel, increasing the ARL to 8. The team is looking forward to demonstrating an ARL 9 with sustained industry usage for decision-making.

Bedka shares that South America is the next frontier for CatModel development. “Regions of South America, including Argentina, Paraguay, Uruguay, Brazil, and Colombia, are frequently impacted by hailstorms, with severity comparable to or sometimes exceeding those over the U.S. Great Plains. We seek to combine historical Geostationary Operational Environmental Satellite (GOES) data with information from reanalysis such as how unstable the atmosphere was and atmospheric wind shear to estimate hailstorm frequency,” Bedka says.

Processing the entire globe at once would be very impractical. Bedka and his team can acquire images about every 15 minutes from geostationary satellites. With over 20 years of observation data, the team must prioritize. The project processes data in regions and will be pursuing a 20+ year data record over South America.

Satellites and Data Consistency

NASA recently released a database of hailstorm detections from TRMM and GPM microwave imager data extending from January 1998 through March 2021. “We developed a consistent and long-term database that clearly shows where hailstorms have most frequently occurred and released it for anyone in the research community, the public, or industry to visualize and use. That is a pretty exciting milestone for us,” says Bedka.

Data consistency over a long time is essential to climatological research. SMASH researchers have developed pattern recognition algorithms to identify possible hailstorms. As technology advances, images from satellites become crisper and sharper, which can lead to discrepancies in algorithm performance.

“If you go from blurry images to much sharper images, your algorithms are naturally going to perform better on the really good imagery,” Bedka notes.

Geostationary Operational Environmental Satellite (GOES) 8 through 15 have consistent technology to maintain coherent decades-long data records. The newest generation of GOES satellites, GOES-16 through –18, provides images four times crisper than their predecessors, which adds a slight challenge for climatological research.

A GOES-16 IR brightness temperature of a hailstorm approaching Hondo, Texas, that is producing baseball-sized hail at the time of the image. About 15 minutes later, the storm produced hailstones up to 6.4 inches in diameter.
(top) A GOES-16 IR brightness temperature of a hailstorm approaching Hondo, Texas, that is producing baseball-sized hail at the time of the image. About 15 minutes later, the storm produced hailstones up to 6.4 inches in diameter. Anvil-penetrating storm updrafts responsible for producing the severe hail, commonly called “overshooting cloud tops,” are evident by very cold temperatures (pink and white color shading). (lower-left) A map of maximum reflectivity in a vertical column, derived from the NOAA NEXRAD radar network using a multi-radar composite via the GridRad framework. Reflectivity exceeding 70 dBZ is associated with hail. (lower-right) A vertical cross-section through the severe hailstorm shows a tall column of high reflectivity indicative of hail and the presence of extremely intense updrafts that elevated the overshooting cloud tops to above 20 km (~66,000 feet) altitude. GridRad data is courtesy of Cameron Homeyer (University of Oklahoma).

Additional Applications

In August 2020, a massive windstorm, called a derecho, brought winds of up to 140 mph through Iowa and Illinois. Media reports said it was the costliest severe storm event in the United States history, wreaking over $11 billion in damage. With the help of satellite and radar data, the SMASH team was able to map out individual storm cells that caused the damage. Scientists can also leverage the vantage point of space to understand where wind and hail damage caused the browning of crops. A sensor, called synthetic aperture radar (SAR), can illustrate the change in crop orientation after storms strike.

Aqua MODIS True Color composite before (upper-left) and after (upper-right) the August 10, 2020 derecho storm that generated winds up to 140 mph, intermittent hail, and tornadoes across several Midwest U.S. states, most notably Iowa and northwestern Illinois. Only a very slight change in vegetation color caused by the severe weather is evident after the derecho. Sentinel-1 Synthetic Aperture Radar (SAR) RGB decomposition product before (lower-left) and after (lower-right) the derecho. Notable changes to the
Aqua MODIS True Color composite before (upper-left) and after (upper-right) the August 10, 2020 derecho storm that generated winds up to 140 mph, intermittent hail, and tornadoes across several Midwest U.S. states, most notably Iowa and northwestern Illinois. Only a very slight change in vegetation color caused by the severe weather is evident after the derecho. Sentinel-1 Synthetic Aperture Radar (SAR) RGB decomposition product before (lower-left) and after (lower-right) the derecho. Notable changes to the RGB product color shading are evident post-derecho (light green to orange), especially where winds exceeded 80 mph, which indicates where crops were knocked down by the high winds. Figures are adapted from Bell et al. (BAMS, 2022).

Bedka explains, “Our team members at NASA Marshall were able to precisely quantify the damage patterns using this synthetic aperture radar data because crops were blown over but did not turn brown very much. Then we compared that to agricultural industry estimates of the damage and found close agreement.”

This event allowed the team to demonstrate how SAR data can streamline the detection and mapping of damage swaths from severe storms. Now that the SMASH team has shown what’s possible, they can continue to develop those capabilities in the future. To scientists, the reinsurance industry, and decision-makers, the real-world images and data that provide an enhanced understanding of severe storm and hail damage can be even more exciting than this year’s blockbuster motion picture.

Cloud temperature data from the Advanced Baseline Imager (ABI) on the GOES-R satellite from a series of tornadic storms that struck Naperville, Chicago, and surrounding regions in June 2021. Credits: NASA Earth Observatory

 

Related Impact