WindBorne Wins U.S. Air Force Contract Worth Up to $49 Million for Balloon Weather Data

The agreement is structured so the Air Force pays only as it needs data, and can place orders for specific regions at specific times. It carries a ceiling of $49 million. So far the service has ordered $1.7 million worth, and company executives expect more orders to follow.

WindBorne co-founder and CTO Andrey Sushko told TechCrunch the contract is largely a vehicle to support continued growth of the company's balloon constellation, along with research and development work to improve the hardware and performance of the balloons. He said 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."

WindBorne, founded in 2015 and based in Redwood City, California, currently has about 700 weather balloons flying around the globe. The balloons collect atmospheric sensor readings that the company sells to customers and also feeds into its own AI weather forecasting model. The company is one of the first organizations to feed empirical data directly into a deep-learning weather model. It has worked closely with the U.S. government, particularly the National Weather Service and the Air Force and Navy, but the new contract is the single largest the company has participated in.

CEO John Dean told TechCrunch in August that the company had raised a $37 million Series B round to keep scaling the business. Earlier this summer WindBorne released the latest version of its forecasting model, WeatherMesh 6, which the company says produces more accurate forecasts than the traditional model run by the European Centre for Medium-Range Weather Forecasts, which simulates the atmosphere using the laws of physics.

Since transformer model architectures arrived and the ECMWF released a large 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 approximating how the atmosphere behaves. End-to-end learning can now 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, though the frontier remains uneven and both types of forecast 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.

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 steers its balloons by adjusting their altitude, bleeding air or dropping sand carried as ballast, so that they enter winds that carry them where they need to go. Sushko said the typhoon flight was a novel step resting on two ingredients: good wind and weather models, and knowing how to navigate them; and a constellation large enough that balloons are in the right places. He described it as one of the first times a deep-learning model was used for actual operational decisions.