When we think of devastating weather events, hurricanes, wildfires, or tornadoes typically dominate the headlines. Yet, there's another type of severe weather that quietly racks up billions of dollars in damage each year: hail. Often underestimated and overshadowed, hail earns its ominous nickname—the "silent killer." In this blog, we:
Although hail rarely produces the catastrophic destruction of a hurricane, tornado, or wildfire, it occurs much more frequently—and the cumulative costs add up.
In 2024, insurance company Aon estimates that severe convective storms (SCS)—the technical term for the full range of threats caused by severe weather—accounted for about $54 billion in insured losses in the United States alone. And the primary driver was hail, which accounts for about 60%-80% of SCS losses in any given year. In fact, reinsurance company Gallagher Re reports that hail is responsible for more losses annually than tornadoes.
What’s more, the damage inflicted by hail is trending upward. In the 1990s, hail was estimated to cause just over $1 billion in damage each year to homes, cars, and crops. However, insured losses from hail have consistently topped $10 billion every year since 2008. And, as of 2020, hail was estimated to cause an average of $15 billion in damage annually.
Although the frequency of hail events may drive total hail damage, a shift toward more intense hailstorms is driving the upward trend. In 2024, 7,386 incidents of severe hail were reported in the U.S. (about the same number that was reported a decade earlier in 2014). But, 1,059 of the 2024 events had hail of two-plus inches, compared to only 671 of the 2014 events. In other words, 2024 had 1.6x as many of the most extreme hail events compared to a decade ago.
As with other natural hazards, early warnings are critical for mitigating the negative impacts of hail. For example, early warnings can give additional time for:
Radar remains the primary tool meteorologists use to detect and track hailstorms. It works by emitting radio waves that bounce off precipitation particles, and the returning signals (echoes) help determine the intensity and type of precipitation within a storm.
Unfortunately, despite important advances, radar technology still struggles to deliver actionable warnings for hail. Challenges specific to hail include:
The good news is that researchers are continuing to advance our understanding of hail with the hope of eventually overcoming current limitations. We’ll briefly look at one of the latest developments from NOAA’s National Severe Storms Laboratory, which is taking ground-level observations of hail to a whole new level.
However, until we do manage to overcome those limitations, it’s widely acknowledged that forecasters should generally incorporate supplemental sources of information. One of those is lightning data, which we’ll examine in some detail.
One of the key challenges with radar is connecting what the radar sees aloft with the hail we actually experience at ground level. Unfortunately, our direct ground observations don’t tell us what actually fell to the ground. That’s because direct ground observations are made after impact and melting have caused a hailstone to change shape and lose mass.
Addressing radar’s observational limitations, NOAA’s innovative hail camera captures detailed images of hailstones in mid-air, directly linking radar signatures to real-world hailstone characteristics like size, shape, and density. The system can even measure their velocity across all three dimensions.
To capture images of hailstones as they hurtle through the air, the camera captures 4k footage at 330 frames per second. Capturing high-definition imagery at that speed requires immense lighting power. The system uses an LED array that produces 30% more light than the sun.
| Image of NOAA hail camera provided courtesy of Dr. Sean Waugh, NOAA-NSSL |
This novel observational method promises to significantly refine radar interpretation and calibration. As NOAA researchers integrate these precise visual records with radar data, forecasting and detection capabilities are expected to become substantially more accurate. Still, it's crucial to clarify that this technology is currently observational; practical forecasting improvements will emerge gradually as this data informs and enhances existing predictive models. In the meantime, hail forecasters will need to continue relying on supplemental sources of information, such as lightning data.
Lightning activity has emerged as a valuable early indicator for predicting severe weather, including hailstorms. This should come as no surprise, since the phenomena are intimately related.
Common convective processes underpin the development of both hail and lightning:
In fact, detailed observational studies have supported the link between lightning rates and hail occurrence. For example, sudden increases in the total lightning activity of a thunderstorm (i.e., lightning jumps) have been shown to be a reliable precursor to severe weather events, including hail. These jumps most often occurred between 15-45 minutes prior to hailstorms and as far out as 90 minutes, which means they could provide critical lead-time for warnings.
What’s especially promising is how the timing of lightning jumps compares to warning triggers based on radar reflectivity. Dr. Themistoklis Chronis, an assistant professor at The University of Alabama in Huntsville, told Phys.org, “The current severe storms warning system is based on radar reflectivity. But this lightning jump typically precedes the trigger point for reflectivity by from five to 20 minutes, so we could have that much more time to issue warnings.”
Although sudden lightning jumps may not be able to replace radar, they clearly have a role to play in the prediction of hail and other forms of severe weather.
To leverage this predictive potential, AEM developed Dangerous Thunderstorm Alerts (DTAs). Based on the idea that total lightning provides the earliest indicator of storm severity, DTAs are generated by tracking lightning cells and flash rates through the Earth Networks Total Lightning Network®. Alerts are triggered when lightning rates exceed set thresholds. DTAs identify storms with potential for heavy rain, hail, strong winds, tornadoes, and microbursts while providing up to 45 minutes of lead time (50% faster than other methods).
As hailstorms increasingly impact communities and economies, it is critical to adopt a balanced, integrated approach to weather detection and forecasting. Improved radar technologies and the strategic use of supplemental data sources like lightning detection systems collectively pave the way toward a future where hailstorms can be anticipated with greater accuracy and reliability.
New research tools like NOAA’s hail camera are capturing more precise observations that could eventually help radar overcome its current limitations. In the meantime, lightning detection networks like the Earth Networks Total Lightning Network are capturing important supplemental data that can serve as an early indicator of storm severity. Leveraging that data, AEM’s Dangerous Thunderstorm Alerts may offer critical lead time that radar alone often can’t.
Forecasting hail will always involve some degree of uncertainty. But by combining diverse data sources—like radar and lightning detection—we’re beginning to see this hazard with greater clarity. Now is the time to put that clarity to work—to support smarter decisions, strengthen preparedness, and build more resilient communities.
For further insights, I suggest watching the complete episode of this recent podcast, where I had the opportunity to chat with the developer of NOAA's hail camera about the latest advancements and the future direction of hail detection and forecasting.