AI Analysis Report
Forecasting Fails: Unveiling Evasion Attacks in Weather Prediction Models
This report delves into the critical vulnerabilities of AI-driven weather forecasting models to adversarial perturbations, and introduces WAAPO as a novel framework for targeted attacks that are both effective and stealthy.
Executive Impact
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Vulnerability Rate Identified
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Forecast Manipulation Window
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Impact Amplification
Deep Analysis & Enterprise Applications
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Overview
Forecast Process Flow
Vulnerability Metrics
Attack Scenarios
Overview of Weather Prediction Vulnerabilities
AI models like FourCastNet exhibit impressive accuracy in weather forecasting but are highly susceptible to adversarial attacks. Small, targeted perturbations to initial conditions can significantly alter predicted weather patterns, leading to severe consequences such as false heatwaves or masked storms.
This research introduces WAAPO (Weather Adaptive Adversarial Perturbation Optimization), a novel framework designed to generate effective and stealthy adversarial perturbations. These findings underscore the critical need for robust safeguards to protect operational forecasting systems from malicious exploitation, particularly as reliance on AI models grows.
Enterprise Process Flow
Data Collection
→
Data Assimilation
→
Weather Forecast Models
→
Refined Analysis
→
Broadcast to Users
The flowchart above illustrates the typical weather forecasting process, from initial data collection to final dissemination. Our study highlights that adversarial attacks can exploit the data collection phase to introduce perturbations. These subtle alterations then propagate through the entire forecasting pipeline, influencing the final predictions delivered to users.
The WAAPO framework specifically targets this initial input stage, demonstrating how minor, localized modifications can dramatically alter output predictions, creating false events or masking real ones, while remaining physically realistic and imperceptible.
Quantifying AI Model Vulnerability
The study introduces the Perturbation Magnitude Ratio (PMRG) to quantify the impact of WAAPO. PMRG compares the Frobenius norm of WAAPO perturbations to that of a scaled Gaussian perturbation. A lower PMRG indicates a smaller perturbation relative to random noise.
| Attack Type | PMRG | Impact Description |
|---|---|---|
| WAAPO (patch-based) | 0.105 | Significant deviations in localized, targeted regions. |
| WAAPO (channel-based) | 0.565 | Significant deviations across specific weather variables (e.g., temperature). |
| Random Gaussian Noise | ~1.0 (baseline) | Minimal impact on overall forecasts, model robustness. |
Key finding: WAAPO perturbations, despite being *smaller* in magnitude than typical random Gaussian noise, exert a disproportionately large and targeted effect on AI-based weather forecasts, highlighting the effectiveness of carefully crafted adversarial examples.
Real-World Attack Scenarios
Scenario: Fabricating a Heatwave
Objective: To generate forecasts predicting a non-existent heatwave in a specific region.
Perturbation: WAAPO subtly adjusts initial temperature fields in the targeted area. These changes are localized, channel-sparse, and smooth, ensuring they remain imperceptible and physically realistic. The model then forecasts significantly elevated temperatures, potentially triggering public alerts and resource mobilization unnecessarily.
Scenario: Suppressing a Storm Warning
Objective: To prevent the model from predicting an actual severe weather event, such as a hurricane.
Perturbation: Small, targeted changes are introduced to key atmospheric variables (wind speed, pressure, humidity) around the developing storm system. These perturbations disrupt the model's ability to accurately form and track the storm in its forecast, leading to a false sense of security and leaving communities unprepared.
These scenarios illustrate the tangible threat of cyberattacks on forecasting models, emphasizing the need for advanced detection and defense mechanisms to safeguard critical infrastructure and public safety.
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