美国空军将根据一份新合同，购买 WindBorne Systems 自主气象气球机群收集的数据，该合同对这家初创公司而言价值可能接近 5000 万美元。
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 目前在全球范围内有约 700 个气象气球在飞行，收集大气传感器读数，这些数据既出售给客户，也输入到公司自研的 AI 气象预报模型中。WindBorne 成立于 2015 年，总部位于加利福尼亚州雷德伍德城，是最早将实测数据直接输入深度学习气象模型的机构之一。
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, California, is one of the first organizations to directly feed empirical data into a deep-learning weather model.
该公司一直与美国政府密切合作，尤其是国家气象局、美国空军和海军，但这是该公司参与的单笔最大合同。合同价值最高达 4900 万美元，但空军仅在需要数据时付费，可针对特定地区、特定时间下单。迄今为止，空军已下单 170 万美元，高管预计后续还会有更多订单。
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.
“这在很大程度上是一个支持星座持续增长的载体，同时也支持一些研发工作，以继续改进气球的硬件和性能，”WindBorne 联合创始人兼首席技术官 Andrey Sushko 告诉《TechCrunch》。他补充说，这份未经竞争性招标授予的合同，“是政府作出的认定：就这种收集全球数据的能力而言，我们是唯一能以满足其行动需求的方式提供该能力的公司。”
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.
WindBorne 首席执行官 John Dean 8 月告诉《TechCrunch》，公司已完成 3700 万美元的 B 轮融资，以继续扩大业务规模。今夏早些时候，公司发布了最新版预报模型 WeatherMesh 6，据称其预报准确度优于欧洲中期天气预报中心（ECMWF）运行的传统模型，后者利用物理定律模拟大气。
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 CEO John Dean told TechCrunch in August that the company had raised a $37 million Series B round to continue scaling its business.
自从Transformer模型架构的出现以及欧洲中期天气预报中心（ECMWF）几年前发布了庞大的天气数据集以来，人工智能在天气预报领域的应用已成为深度学习技术最成功的应用之一。过去，气象学家依赖昂贵的超级计算机来运行复杂的数学模型，以模拟大气的运动规律；如今，通过端到端的学习流程，人工智能能够快速处理数十年的天气数据并生成预测结果。
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.
由前沿研究机构及政府机构开发的天气预报模型已经显示出比传统方法更高的准确性。不过，这一领域仍处于发展初期，两种预报方式仍需结合使用才能实现对天气状况的全面理解。谷歌DeepMind的最新天气预报模型带来了许多创新功能，其飓风预测工具在美国政府应对飓风季节的决策过程中发挥着越来越重要的作用。
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.
今年8月，WindBorne成为首个派遣无人驾驶飞行系统（即无需发动机的飞行器）前往台风中心收集数据的机构。该公司通过调节气球的高度、释放空气或投放沙子作为压舱物，使气球能够被风吹送到目标位置。
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 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.
“这是一个非常新颖的尝试。实现这一目标需要两个关键条件：首先是拥有精确的风力和天气预测模型，其次是拥有足够数量的飞行气球，确保它们能够被部署在正确的位置，”Sushko说道。“这是首次将深度学习模型应用于实际的操作决策中。”
“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.”