WindBorne Wins Landmark Air Force Deal
The U.S. Air Force will purchase data collected by WindBorne Systems' fleet of autonomous weather balloons under a new contract worth up to nearly $50 million. It marks the single largest contract the startup has secured to date.
WindBorne currently operates roughly 700 weather balloons worldwide, gathering atmospheric sensor readings that it sells to customers and feeds into its own AI weather forecasting model. Founded in 2015 and headquartered in Redwood City, California, the company is among the first organizations to feed empirical data directly into a deep-learning weather model.
Contract Structure and Government Relationship
While WindBorne has worked closely with the U.S. government—particularly the National Weather Service, the Air Force, and the Navy—this contract represents a significant step up in scale. The award is valued at up to $49 million, but the Air Force pays only as it requires data, placing orders for specific regions at specific times. So far, it has ordered $1.7 million worth, and executives expect further orders to follow.
"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," WindBorne co-founder and CTO Andrey Sushko told TechCrunch. He added that the contract, awarded without competitive bidding, reflects "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."
Funding and Forecasting Breakthroughs
WindBorne CEO John Dean told TechCrunch in August that the company had raised a $37 million Series B round to continue scaling its business. Earlier this summer, WindBorne released the latest version of its forecasting model, WeatherMesh 6, which the company says delivers more accurate forecasts than the traditional physics-based model run by the European Centre for Medium-Range Weather Forecasts (ECMWF).
The Rise of Deep-Learning Weather Prediction
Since the emergence of transformer model architectures and the ECMWF's release of a massive weather dataset a few years ago, AI weather forecasting has become one of the more productive applications of deep-learning technology. Meteorologists once relied on expensive supercomputers running detailed equations approximating atmospheric behavior. Today, end-to-end learning can ingest decades of weather data and issue predictions far more quickly.
Models built by frontier labs and government agencies are proving more accurate than traditional forecasts, though the frontier remains uneven—both approaches are still needed for a useful understanding of the weather. Google DeepMind's latest weather model offers important new capabilities, and its cyclone forecasting tools are becoming increasingly central to how the U.S. government responds to hurricane season.
Navigating a Balloon Into a Typhoon's Eye
In August, WindBorne became the first organization to send an unpowered flight system—one with no engine—to collect data inside the eye of a typhoon. The company steers its balloons by adjusting altitude, venting air, or dropping sand carried as ballast, allowing them to ride winds that carry them where they 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."
via TechCrunch AI