The risk of weather data sabotage is rising

Types of Manipulation: The risks of manipulating weather station data escalate from individual speculators to groups of traders and potentially state actors, impacting personal gain, disaster preparedness, and national security.
Data Security Measures: To counteract potential data manipulation, continuous station monitoring, anomaly detection, data homogenization, and human oversight are essential to maintain data integrity and support real-time decisions by AI systems.
Protecting AI: Data defense mechanisms must be embedded throughout the AI pipeline, with AI explainability and adversarial robustness tools to understand data and model outputs, identify issues, and enhance resilience against attacks.
Accountability Chain: Ensuring continuous accountability is crucial across all parties from station operators to forecasting centers; communication of any anomaly-related data issues must span the entire chain to safeguard data integrity.
Adaptation to Evolving Threats: Given the growing reliance on observational data in weather forecasting, enhancing existing oversight and accountability and improving coordination across key partners are necessary to anticipate and mitigate evolving threats.