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

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.

根据一份新合同，美国空军将购买由 WindBorne Systems 公司的自动气象气球群所收集的数据，该合同对这家初创公司而言价值可能接近 5000 万美元。WindBorne 目前在全球范围内拥有约 700 个气象气球，负责收集大气传感器读数，公司不仅将这些数据出售给客户，还将其输入到自有的 AI 天气预报模型中。

WindBorne, founded in 2015 and based in Redwood City, California, 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.

WindBorne 成立于 2015 年，总部位于加利福尼亚州红木城，是首批将实证数据直接输入深度学习天气模型的机构之一。该公司一直与美国政府密切合作，特别是与国家气象局、美国空军和海军的合作，但这却是该公司参与过的金额最大的一份合同。

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.

该合同价值高达 4900 万美元，但空军仅在需要数据时才进行支付。空军可以针对特定时间和特定区域下订单。到目前为止，空军已订购了价值 170 万美元的数据，高管们预计后续还会有更多订单。

“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, 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 联合创始人兼首席技术官 Andrey Sushko 对 TechCrunch 表示：“这在很大程度上是一个支持卫星群持续增长的载体，同时也用于一些研发工作，以继续改善气球的硬件和性能。”他补充说，这份未经竞争性招标而授予的合同，“是政府认定在收集全球数据这一能力方面，我们是唯一一家能够以满足其行动需求的方式提供该服务的公司。”

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.

WindBorne 首席执行官 John Dean 在 8 月份告诉 TechCrunch，该公司已筹集了 3700 万美元的 B 轮融资，以继续扩大业务规模。今年夏天早些时候，该公司发布了其预报模型的最新版本 WeatherMesh 6。据称，该模型比欧洲中期天气预报中心（ECMWF）运行的传统模型提供更准确的预报，而传统模型是利用物理定律来模拟大气的。

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.

自 Transformer 模型架构出现以及 ECMWF 几年前发布海量天气数据集以来，AI 天气预报已成为深度学习技术最具成效的应用之一。气象学家此前依赖昂贵的超级计算机运行详细的方程来近似模拟大气的行为。现在，端到端学习可以摄取数十年的天气数据，并更快速地发布预测。

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 U.S. government responds to hurricane season.

前沿实验室和政府机构构建的模型已被证明比传统预报更准确，尽管这仍是一个充满挑战的领域，需要结合两种类型的预报才能对天气形成有用的理解。Google DeepMind 最新的天气模型提供了重要的新功能，其气旋预报工具正日益成为美国政府应对飓风季节的核心手段。

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.

8 月份，WindBorne 成为首个派遣无动力飞行系统（即没有发动机的系统）进入台风眼收集数据的机构。该公司通过调整气球高度、放气或抛弃作为压舱物的沙子来操纵气球，从而将飞行器送入风中，使其到达预定位置。

“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.”

Sushko 说：“这是一个非常新颖的步骤，实现它的两个要素是：第一，拥有良好的风力和天气模型，并知道如何导航；第二，拥有足够大的气球群，确保气球处于正确的位置。这是深度学习模型首次被用于实际的运营决策。”