WindBorne 获美国空军近 5000 万美元气象气球数据合同

The U.S. Air Force will buy data collected by WindBorne Systems‘ fleet of autonomous weather balloons under a new contract that could be worth nearly $50 million to the startup.

WindBorne currently has about 700 weather balloons flying around the globe, collecting atmospheric sensor readings that the company sells to customers and also feeds into its own AI weather forecasting model. WindBorne, founded in 2015 and based in Redwood City, Calif., is one of the first organizations to directly feed empirical data into a deep-learning weather model.

The company has worked closely with the U.S. government, particularly the National Weather Service and the U.S. Air Force and Navy, but this is the single largest contract the company has participated in. The contract is worth up to $49 million, but the Air Force pays only as it needs data. It can place orders for specific regions at specific times. So far, it has ordered $1.7 million worth, and executives expect more 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 adds that the contract, which was awarded without competitive bidding, 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.”

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, the company released the latest version of its forecasting model, WeatherMesh 6, which it says provides 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.

Since the arrival 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 uses of deep-learning technology. Meteorologists previously relied on expensive supercomputers running detailed equations that approximate how the atmosphere behaves. Now, end-to-end learning can ingest decades of weather data and issue predictions more quickly.

Models built by frontier labs and government agencies are proving more accurate than traditional forecasts, although it is still a jagged frontier, with both types of forecasts required to enable 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 US government responds to hurricane season.

In August WindBorne became the first organization to send an unpowered flight system — one with no engine — to collect data in the eye of a typhoon. The company maneuvers its balloons by adjusting their altitude, bleeding air, or dropping sand carried as ballast, to send the vehicles into winds that take 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.”

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Tim Fernholz is a journalist who writes about technology, finance and public policy. He has closely covered the rise of the private space industry and is the author of Rocket Billionaires: Elon Musk, Jeff Bezos and the New Space Race. Formerly, he was a senior reporter at Quartz, the global business news site, for more than a decade, and began his career as a political reporter in Washington, D.C. You can contact or verify outreach from Tim by emailing [email protected] or via an encrypted message to tim_fernholz.21 on Signal.

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