AI could stretch hail warnings from minutes to hours. Here’s how to make a forecast pay off

On Tuesday, public radio’s Mountain West News Bureau reported on a Colorado State University project using AI to tackle one of weather forecasting’s hardest problems: hail. Current systems give about 30 minutes of warning. The researchers want to give hours, or even days.

What the researchers are building

Hail takes a strong updraft, freezing temperatures and liquid water droplets, all at once. “It’s a combination of many factors that are really hard for models to get right,” said Kristen Rasmussen, the Colorado State associate professor leading the project.

The project, funded by the National Science Foundation, pairs two AI models:

• One watches storms as they form. It combines several radar sources with a model from NVIDIA, trained to focus on the patterns that matter for hail. A trade publication reported in June that it forecasts severe hail two to three hours ahead, compared with 30 to 60 minutes for earlier methods.
• The other learns from the past. It studies the ingredients of past destructive storms in historical weather data to make predictions up to eight days out.

“With AI models, we can really hone in on specific phenomena like hail,” Rasmussen said.

Hail is driving up insurance costs by damaging roofs, cars and crops. In Colorado, it’s the biggest cost driver of property insurance premiums.

The real-world test is still ahead. Next year, the team will install sensors near a weather station in Greeley, Colorado, to check the models against hail that actually falls, a step researchers call ground-truthing.

Why it matters for your business

The most useful line in the story isn’t about AI. It’s Rasmussen’s example of a solar farm: “A couple-day warning can sometimes give better preparations and can allow for some of that equipment to either be moved, or sometimes you can tilt up the solar panels to withstand hail better.”

A forecast only pays off when it changes what someone does, and that’s as true of a sales forecast as a storm forecast. A prediction of a slow month is worth something only if you adjust orders, schedules or staffing.

The second model also shows something any business can borrow: it learns from the record of what happened before. You don’t need radar for that. You need records.

Closer to home

Hail isn’t only a Rocky Mountain problem. Spring thunderstorms bring it to the Wiregrass, too. Picture a Dothan car lot with a few hundred vehicles parked in the open. Two hours of warning could mean moving the newest ones under cover. Or picture an Enterprise roofing contractor whose phone rings nonstop after every storm. Two days of warning could mean shingles ordered and crews lined up before the calls come in.

The same thinking works on calmer days. Picture a Wiregrass feed-and-seed store whose sales history shows when the spring rush really starts, or an HVAC company whose records show when the first heat wave fills the schedule.

Five ways to make a forecast pay off

1. Start with the decision. Ask what you’d do differently with two hours’ or two days’ notice. If the answer is “nothing,” a better forecast won’t help.
2. Write the plan before you need it. Decide what triggers action and who does what, whether it’s moving vehicles, covering stock or calling in extra staff.
3. Match the action to the odds. Forecasts are probabilities. Cheap precautions can start at lower odds, while costly ones, like closing early, can wait for a stronger signal.
4. Keep score. Do your own ground-truthing. Write down what a forecast said and what happened, and after a season you’ll know how far to trust it.
5. Put your history to work. Sales, service calls, invoices and maintenance logs hold patterns, like the work that follows a storm. Keep a few years of them in one place.

None of this requires a supercomputer. It requires knowing which decisions a better guess would improve.

Where we can help

Our predictive analytics service builds forecasts from your own data. We start from the decision a prediction should improve, like how much to order or which machine to service first, and tell you honestly whether your data is enough to predict well. We test models on periods they haven’t seen, deliver predictions in the tools your team already uses, and watch accuracy and retrain as your business changes. Book a free strategy call.

Sources

• KUNC, Mountain West News Bureau: To better predict destructive hail, these scientists are turning to AI, November 3, 2026
• Meteorological Technology International: Colorado State University and Nvidia partner to extend severe storm prediction lead times, June 2, 2026