At the low end of the risk scale, an individual speculator manipulates a weather station for personal gain—that is the CDG Airport case. 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. 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.
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. 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 us understand the underlying data and the AI model outputs, help us identify data- or model-related issues, and potentially make us more resilient to adversarial attacks.
3. Ensure continuous accountability along the chain. Observational data passes through many hands: the operators who run the stations, the national weather services that steward the records, and the forecasting centers that turn them into predictions. No single one of them can protect data integrity alone—each guards its own link, and any anomaly needs to be communicated along the whole chain, from station operators to the people acting on the forecast.
It is fortunate that the situation at CDG Airport was caught, but it should serve as a wake-up call. As the role of observational data grows in weather forecasting, we need to adapt to evolving threats. This means protecting our data and models by strengthening existing oversight and accountability structures, and improving coordination among key partners.
This op-ed was written by:
- Monique Kuglitsch — Innovation Manager at Fraunhofer Heinrich Hertz Institute and Chair of the UN Global Initiative on Resilience to Natural Hazards through AI Solutions
- Jesper Dramsch — Scientist for Machine Learning at the European Centre for Medium-Range Weather Forecasts (ECMWF), where they work on AIFS (Artificial Intelligence Forecasting System), ECMWF’s data-driven weather prediction model
- Franz G. Kuglitsch — Climate Scientist and Executive Secretary of the International Union of Geodesy and Geophysics (IUGG) at the GFZ Helmholtz Centre for Geosciences in Potsdam
- Andrea Toreti — Senior Scientist at the European Commission’s Joint Research Centre (JRC), where he coordinates the European and Global Drought Observatory under the Copernicus Emergency Management Service
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