The risk of weather data sabotage is rising

气象数据遭破坏的风险正在上升

Every morning, airline dispatchers, grid operators, and farmers around the world make decisions based on the same thing: a weather forecast. While these forecasts are something that most people glance at for two seconds, weather predictions influence major strategic decisions in many industries, with real money, livelihoods, and even actual lives at stake. 每天早晨，全球各地的航空公司调度员、电网运营商和农民都在根据同一件事做出决策：天气预报。虽然大多数人对这些预报只是匆匆一瞥，但天气预测却影响着许多行业的重大战略决策，并直接关系到真金白银、生计，甚至是生命安全。

Farmers use them to determine which crop variety to sow, when to fertilize, how much to invest in irrigation infrastructure, and how long livestock should graze. Utilities use them to decide where to build solar and wind farms, as well as how to price wholesale electricity. Predictions are used to warn people about extreme weather and to trigger emergency response measures. 农民利用天气预报来决定种植何种作物、何时施肥、在灌溉基础设施上投入多少资金，以及牲畜放牧的时长。公用事业公司利用预报来决定太阳能和风能发电场的选址，以及如何制定电力批发价格。天气预测还被用于预警极端天气并触发应急响应措施。

More recently, weather predictions have become relevant for an emerging industry: prediction markets, where people bet money on all kinds of real-world events, including the weather. However, the temptation to manipulate weather data to get an edge in these markets, combined with a collective move toward data-driven AI weather forecasting, is starting to put the accuracy of weather predictions at risk. 最近，天气预测与一个新兴行业产生了关联：预测市场。人们在这些市场上对各种现实事件（包括天气）进行金钱投注。然而，为了在这些市场中获得优势而操纵气象数据的诱惑，加上全球向数据驱动型人工智能天气预报的集体转型，正开始使天气预测的准确性面临风险。

These risks are relatively manageable for now, but as experts in the field, we can foresee scenarios where they snowball into far bigger, more systemic problems. To develop weather predictions, we need accurate observations of current conditions. These are collected from several sources, including weather stations at airports, utilities, or transport services. 目前这些风险尚在可控范围内，但作为该领域的专家，我们可以预见，这些风险可能会像滚雪球一样演变成更大、更具系统性的问题。为了进行天气预测，我们需要对当前状况进行准确观测。这些观测数据来自多个来源，包括机场、公用事业部门或交通服务部门的气象站。

Traditional operational systems like the Weather Research and Forecasting model or the European Centre for Medium-Range Weather Forecast (ECMWF) Integrated Forecasting System combine these observations with numerical approximations in order to estimate future weather patterns. Sometimes, weather stations have issues because of, for example, instrument failures or upgrades in equipment. These can be caught either in real time (through checking and correction) or retroactively. 诸如“天气研究与预报模型”（WRF）或欧洲中期天气预报中心（ECMWF）综合预报系统等传统业务系统，会将这些观测数据与数值近似计算相结合，以估算未来的天气模式。有时，气象站会因仪器故障或设备升级等原因出现问题。这些问题可以通过实时检查与修正，或事后追溯来发现。

Traditional forecasting systems also have a built-in safeguard called data assimilation: Every incoming measurement is weighed against what the physical model says should be happening and against readings from nearby stations. Together, these mechanisms help keep weather observations reliable and predictions robust. 传统的预报系统还内置了一种名为“数据同化”的保障机制：每一项传入的测量数据都会与物理模型预测的预期情况以及附近气象站的读数进行比对。这些机制共同作用，确保了气象观测的可靠性和预报的稳健性。

However, new threats are putting observational accuracy at risk. Earlier this year, news outlets reported that the weather station at Paris Charles de Gaulle Airport (CDG) had been manipulated to record suspicious temperature spikes on April 6 and April 15, 2026. Authorities speculate that a hand-held hairdryer or lighter might have come into play. 然而，新的威胁正使观测数据的准确性面临风险。今年早些时候，新闻媒体报道称，巴黎戴高乐机场（CDG）的气象站遭到人为操纵，在2026年4月6日和4月15日记录到了可疑的温度飙升。当局推测，这可能是有人使用了手持吹风机或打火机所致。

Either way, it led to some big payouts for online prediction-market gamblers who had bet it would hit 22 °C (71.6 °F) on days when the actual average was around 18°C (64.4°F). One individual won $20,000. Fortunately, tampering with a single station like this can usually be caught by human monitoring or current statistical methods. In this case, members of a French climate nonprofit association noticed the anomalies by chance and raised the alarm. 无论如何，这让那些押注气温会达到22°C（实际平均气温约为18°C）的在线预测市场赌徒获得了巨额赔付。其中一人赢得了2万美元。幸运的是，这种对单个气象站的篡改通常可以通过人工监控或现有的统计方法发现。在此次事件中，法国一家气候非营利组织的成员偶然发现了这些异常并拉响了警报。

But what if there are no human monitoring systems in place? And what about other types of manipulation? What if, instead of tampering with one station, someone remotely nudged the readings at many stations at once—making each change small enough to look plausible on its own? Existing quality controls struggle to catch this kind of coordinated manipulation. 但如果没有人工监控系统呢？如果存在其他类型的操纵呢？如果有人不是篡改一个气象站，而是远程同时微调多个气象站的读数，使每一次变动都小到看起来合乎情理，那该怎么办？现有的质量控制手段很难发现这种协同操纵。

And time works against us; careful checks of data and metadata take hours or days, but forecasts have to go out on schedule, whatever the weather is doing. The shift toward artificial intelligence in weather prediction raises the stakes. These methods are even more dependent on accurate, reliable weather observations; in fact, they are known as “data-driven models.” 时间对我们不利；对数据和元数据进行仔细核查需要数小时甚至数天，但无论天气如何，预报都必须按时发布。向人工智能天气预报的转型提高了风险。这些方法更加依赖准确、可靠的气象观测；事实上，它们被称为“数据驱动模型”。

For example, researchers at ECMWF are exploring whether high-quality weather forecasts can be produced directly from raw observations, skipping the assimilation step that currently acts as a quality filter. Other researchers are going one step further; combining geospatial data (including weather station data) with large language models and agentic AI to support real-time, autonomous decision-making during extreme events such as storms. 例如，ECMWF的研究人员正在探索是否可以直接从原始观测数据中生成高质量的天气预报，从而跳过目前作为质量过滤器的同化步骤。其他研究人员则更进一步，将地理空间数据（包括气象站数据）与大语言模型和智能体AI相结合，以支持在风暴等极端事件期间进行实时的自主决策。

Possible benefits are improvements in accuracy, efficiency, and speed. But removing humans from the equation introduces a vast range of new risks. At the low end of the risk scale, an individual speculator manipulates a weather station for personal gain—that is the CDG Airport case. 其潜在的好处是提高准确性、效率和速度。但将人类排除在决策过程之外，会带来一系列新的风险。在风险等级的低端，是个体投机者为个人利益操纵气象站——这就是戴高乐机场的案例。

One step up: A group of traders could coordinate to bias forecasts of renewable energy output, moving wholesale electricity prices and leaving whoever is on the other side of the trade holding the loss. And at the far end, a state actor or saboteur could manipulate one or many stations to set off an early warning system or even keep one silent when it should sound. 高一级：一群交易员可以协同操纵可再生能源产量的预报，从而影响电力批发价格，让交易对手方蒙受损失。而在极端情况下，国家行为体或破坏者可以操纵一个或多个气象站，触发预警系统，甚至在应该发出警报时保持沉默。

Step by step, the risk grows, from fraud to compromised disaster preparedness to a matter of national security. As long as there are financial (or other) incentives to manipulate observational data, adversaries will search for new opportunities, and it is our task to stay one step ahead. Here are three ways. 风险一步步升级，从欺诈到破坏防灾准备，再到国家安全问题。只要存在操纵观测数据的经济（或其他）动机，对手就会寻找新的机会，而我们的任务就是领先一步。以下是三种方法：

1. Watch the stations. Data quality controls should include station security, anomaly detection and correction, and human oversight. Weather stations should be monitored continuously to deter tampering. Data homogenization methods that clean up weather records also need to get faster, with the goal of catching problems in real time. This will become increasingly important as agentic AI systems use these data to deliver real-time decisions. Finally, human oversight is needed to flag questionable data and model outcomes. After all, it was humans who caught the CDG Airport manipulation.

2. 监控气象站。数据质量控制应包括气象站安全、异常检测与修正以及人工监督。应持续监控气象站以防止篡改。用于清理气象记录的数据同质化方法也需要加快速度，目标是实时发现问题。随着智能体AI系统利用这些数据进行实时决策，这一点将变得愈发重要。最后，需要人工监督来标记可疑数据和模型结果。毕竟，正是人类发现了戴高乐机场的操纵行为。

3. Protect the data to safeguard the AI. Data defense mechanisms must be positioned throughout the AI pipeline. AI explainability and adversarial robustness tools can help.

4. 保护数据以捍卫人工智能。数据防御机制必须部署在整个AI流程中。AI可解释性和对抗性稳健工具可以提供帮助。