WindBorne wins a hefty US Air Force contract for its balloon data

A Silicon Valley startup that uses weather balloons and artificial intelligence to predict weather has won a US military contract worth nearly $50 million.

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

WindBorne currently has about 700 weather balloons flying around the world collecting readings from atmospheric sensors that the company sells to customers and also feeds into its AI weather forecasting model. Founded in 2015 and based in Redwood City, California, WindBorne is one of the first organizations to directly feed experimental data into a deep learning climate 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 if the data is needed. Can order for specific areas at specific times. So far, it has ordered $1.7 million, and executives expect more orders to follow.

WindBorne co-founder and CTO Andriy Sushko told TechCrunch: “This is largely a vehicle to support the continued growth of Constellation, as well as some work on research and development to continue improving the hardware and performance of the balloons. The contract, which was awarded without competitive bidding, was "the government's decision that for this global data collection capability, we were the only company that could deliver it in a way that met their needs for this operation," he added.

WindBorne CEO John Dean told TechCrunch in August that the company had raised $37 million in Series B funding to continue expanding its business. Earlier this summer, the company released the latest version of its forecast model, WeatherMesh 6, which it says provides more accurate forecasts than the traditional model run by the European Center for Medium-Range Weather Forecasts (ECMWF), which simulates the atmosphere using the laws of physics.

Since the introduction of transformer model architectures and the release of massive weather datasets by ECMWF a few years ago, AI weather forecasting has become one of the more useful applications of deep learning technology. Meteorologists previously relied on expensive supercomputers that ran precise equations that approximated how the atmosphere behaved. Now, machine learning can take decades of weather data and issue predictions faster.

Models built by frontier labs and government agencies are more accurate than traditional forecasts, though it's still a rough frontier, with both types of forecasts needed to make a useful understanding of the climate. Google's latest DeepMind weather model offers significant new capabilities, and its hurricane forecasting tools are increasingly central to how the US government responds to hurricane season.

In August, WindBorne became the first organization to send an unpowered flight system — a system without engines — to collect data in the eye of a storm. The company maneuvers its balloons by adjusting altitude, bleeding air or dropping sand carried as ballast to send the vehicles into the winds that will take them where they need to go.

"That's a new step, and two components to that one, having good wind and weather models, and knowing how to navigate them," Sushko said. And second, having a constellation big enough to have the balloons in the right places." "This is one of the first times a deep learning model has been used to make real operational decisions."

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