Diffusion-Based Rollouts as a Stabilization Mechanism for Long-Horizon Environmental Forecasting

2026-09-27

AI Summary

This study addresses the error accumulation inherent in recursive rollout forecasting for environmental prediction, which often triggers instability in long-lead-time forecasts. To mitigate this, we propose a recursive forecasting framework that leverages diffusion models as a stabilization mechanism, incorporating numerical weather prediction (NWP) for conditional generation to constrain error growth in both low-dimensional water levels and high-dimensional precipitation fields. Our work elucidates the mechanism by which diffusion models suppress numerical instabilities, demonstrating that their effectiveness fundamentally depends on the constraining capacity of external predictive information. Experimental results confirm that the proposed approach effectively attenuates recursive error amplification while preserving NWP priors, significantly enhancing the spatial organization of precipitation and event detection skill.

Abstract

Extending forecast lead times while maintaining predictive skill remains a major challenge in environmental forecasting. We investigate diffusion-based rollouts as a stabilization mechanism for recursive forecasting using low-dimensional water-level time series and high-dimensional precipitation fields. Across both modalities, diffusion suppresses recursive error growth, with the largest stabilization occurring where deterministic rollouts are most unstable. However, stabilization does not guarantee forecast fidelity. In the water-level experiments, forecasts progressively lose event-level fidelity as the rollout loses access to external predictive information, and trajectory-level comparisons show that diffusion can remain numerically stable while contracting toward central values and exhibiting reduced variability. In the precipitation experiments, which retain conditioning from numerical weather prediction throughout the rollout, diffusion better preserves spatial organization and event-detection skill. Together, these contrasting experiments indicate that diffusion can control recursive error amplification, while its practical benefit also depends on the predictive information available to constrain future evolution.

Problem

• environmental forecasting
• long-horizon prediction
• recursive error growth
• diffusion-based rollouts

Innovation

• Diffusion-based rollouts
• Recursive forecasting
• Error stabilization
• Environmental forecasting
• Long-horizon prediction