Guided Diffusion Sampling for Precipitation Forecast Interventions

2026-05-13

AI Summary

This work addresses the limitation of existing precipitation intervention methods, which predominantly rely on adversarial perturbations and often neglect atmospheric physical consistency. To reconcile intervention efficacy with physical plausibility, we propose a gradient-guided diffusion sampling framework that reduces precipitation by steering the latent-space sampling trajectory within a diffusion-based weather forecasting model, rather than directly perturbing atmospheric states. As the first study to apply guided diffusion sampling to weather intervention, our approach significantly attenuates precipitation intensity in extreme rainfall events from WeatherBench2. Comprehensive evaluations—including cross-model transferability tests and vertical profile analyses of perturbed variables—demonstrate superior physical credibility and generalization capability compared to conventional adversarial methods.

Abstract

Extreme precipitation causes severe societal and economic damage, and weather control has long been discussed as a potential mitigation strategy. However, to the best of our knowledge, perturbation-based interventions for weather control using data-driven weather forecasting models have not yet been explored. While adversarial attacks also generate perturbations that alter forecasts, they aim to exploit model artifacts and do not account for physical plausibility. In this paper, we propose a gradient-based guidance framework for precipitation-reduction interventions through diffusion sampling in diffusion-based weather forecasting models. Instead of directly perturbing atmospheric states, our method steers the diffusion sampling trajectory, enabling precipitation reduction while maintaining consistency with the atmospheric distribution. To assess physical plausibility, we evaluate from three perspectives: (i) vertical and variable-wise perturbation profiles, (ii) latent-space trajectory deviation, and (iii) cross-model transferability. Experiments on extreme precipitation events from WeatherBench2 demonstrate that our method achieves effective precipitation reduction while yielding more physically plausible interventions than adversarial perturbations.

Problem

• precipitation control
• weather intervention
• physical plausibility
• extreme precipitation
• diffusion models

Innovation

• guided diffusion sampling
• precipitation reduction
• weather intervention
• physical plausibility
• diffusion-based forecasting