Air Force Commits Up to $49 Million to WindBorne's Balloon Weather Data

The U.S. Air Force will pay WindBorne Systems up to $49 million for atmospheric data gathered by its fleet of roughly 700 autonomous weather balloons — the largest contract in the startup's decade-long history.

The deal, awarded without competitive bidding, works like a standing purchasing agreement: the Air Force pays only for the data it orders, by region and by time. So far it has ordered $1.7 million worth, and WindBorne's executives expect more orders to follow.

WindBorne, founded in 2015 and based in Redwood City, Calif., flies balloons that collect atmospheric sensor readings. The company sells that data to customers and feeds it into its own AI weather forecasting model. It is one of the first organizations to directly feed empirical observations into a deep-learning weather model.

The company has worked with the U.S. government before — notably the National Weather Service, the Air Force and the Navy — but never on a deal of this size.

Why did the Air Force skip competitive bidding?

Co-founder and CTO Andrey Sushko framed the no-bid award as a verdict on the company's uniqueness.

"This is largely a vehicle through which continued growth of the constellation can be supported, as well as some work on research and development to continue to improve the hardware and performance of the balloons," Sushko told TechCrunch. He added that the contract is "a determination by the government that for this capability of collecting global data, we were the only company able to provide it in the way that met their needs for these operations."

In plain terms: Washington looked at the market and concluded only WindBorne could do the job.

How does the money fit WindBorne's broader push?

The contract lands weeks after CEO John Dean told TechCrunch in August that WindBorne had raised a $37 million Series B to keep scaling the business. The Air Force ceiling of $49 million tops that entire round.

Earlier this summer the company released WeatherMesh 6, the latest version of its forecasting model. WindBorne claims it delivers more accurate forecasts than the traditional model run by the European Centre for Medium-Range Weather Forecasts (ECMWF), which simulates the atmosphere using the laws of physics rather than learned patterns.

The context matters. Since transformer architectures arrived and the ECMWF released a massive weather dataset a few years ago, AI forecasting has become one of the more productive applications of deep learning. Meteorologists once relied on expensive supercomputers solving detailed equations; end-to-end learning can now ingest decades of weather data and issue predictions faster.

Models from frontier labs and government agencies increasingly beat traditional forecasts, though the frontier is jagged — forecasters still need both approaches. Google DeepMind's latest weather model and its cyclone forecasting tools are becoming central to how the U.S. government responds to hurricane season.

What did the balloons do inside a typhoon?

In August, WindBorne became the first organization to send an unpowered flight system — no engine — to collect data in the eye of a typhoon. The company steers its balloons by adjusting altitude, bleeding air, or dropping ballast sand to catch winds heading where the balloons need to go.

"That's a pretty novel step, and the two ingredients for that are one, having good wind and weather models, and knowing how to navigate them; and two, having a large enough constellation that you have balloons in the right places," Sushko said. "It's one of the first times that a deep-learning model was used for actual operational decisions."

That typhoon flight shows the strategy in one line: a bigger constellation plus a better model lets WindBorne go where no one else can — and now the Air Force has a $49 million line of credit to keep it flying.

By Grace Kim