# Adversarial Observations in Weather Forecasting

arXiv: https://arxiv.org/abs/2504.15942 · HTML: https://mlsanity.com/paper/2504.15942/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 39 references that are in the map.
- First posted on arXiv 22 Apr 2025.

## Builds on (most cited first, 11 of 11)

- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [Adversarial examples in the physical world](https://mlsanity.com/paper/1607.02533/) (2016)
- [Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks](https://mlsanity.com/paper/2003.01690/) (2020)
- [Adversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examples](https://mlsanity.com/paper/2302.04578/) (2023)
- [DiffDA: a Diffusion Model for Weather-scale Data Assimilation](https://mlsanity.com/paper/2401.05932/) (2024)
- [Exploiting Vulnerabilities of Load Forecasting Through Adversarial Attacks](https://mlsanity.com/paper/1904.06606/) (2019)
- [Diffusion Policy Attacker: Crafting Adversarial Attacks for Diffusion-based Policies](https://mlsanity.com/paper/2405.19424/) (2024)
- [Targeted Adversarial Attacks on Wind Power Forecasts](https://mlsanity.com/paper/2303.16633/) (2023)
- [Estimating Solar and Wind Power Production using Computer Vision Deep Learning Techniques on Weather Maps](https://mlsanity.com/paper/2103.08727/) (2021)
- [Theoretically Principled Trade-off for Stateful Defenses against Query-Based Black-Box Attacks](https://mlsanity.com/paper/2307.16331/) (2023)

## Built on by (most cited first, 4 of 4)

- [FABLE: A Localized, Targeted Adversarial Attack on Weather Forecasting Models](https://mlsanity.com/paper/2505.12167/) (2025)
- [Adversarial Attacks on Downstream Weather Forecasting Models: Application to Tropical Cyclone Trajectory Prediction](https://mlsanity.com/paper/2510.10140/) (2025)
- [Evaluating Adversarial Attacks on Federated Learning for Temperature Forecasting](https://mlsanity.com/paper/2512.13207/) (2025)
- [Guided Diffusion Sampling for Precipitation Forecast Interventions](https://mlsanity.com/paper/2605.14317/) (2026)

## Abstract (arXiv)

AI-based systems, such as Google's GenCast, have recently redefined the state of the art in weather forecasting, offering more accurate and timely predictions of both everyday weather and extreme events. While these systems are on the verge of replacing traditional meteorological methods, they also introduce new vulnerabilities into the forecasting process. In this paper, we investigate this threat and present a novel attack on autoregressive diffusion models, such as those used in GenCast, capable of manipulating weather forecasts and fabricating extreme events, including hurricanes, heat waves, and intense rainfall. The attack introduces subtle perturbations into weather observations that are statistically indistinguishable from natural noise and change less than 0.1% of the measurements - comparable to tampering with data from a single meteorological satellite. As modern forecasting integrates data from nearly a hundred satellites and many other sources operated by different countries, our findings highlight a critical security risk with the potential to cause large-scale disruptions and undermine public trust in weather prediction.