Improving Tropical Cyclone Forecasting With Video Diffusion Models

2025-01-27

AI Summary

To address data scarcity and spatiotemporal inconsistency arising from frame-wise modeling in long-term tropical cyclone (TC) forecasting, this work proposes the first end-to-end video diffusion model framework tailored for TC prediction. Methodologically, it introduces a novel temporal attention mechanism to enable joint multi-frame generation and adopts a two-stage training strategy to enhance single-frame accuracy and few-shot robustness. Experiments demonstrate a significant extension of reliable forecast lead time—from 36 to 50 hours—and substantial improvements over Nath et al.’s approach: 19.3% reduction in mean absolute error (MAE), 16.2% increase in peak signal-to-noise ratio (PSNR), 36.1% improvement in structural similarity index (SSIM), and a marked decrease in Fréchet Video Distance (FVD). This is the first application of video diffusion models to TC dynamical modeling, achieving longer-lead, more temporally consistent, and better generalizable physically interpretable forecasts under limited observational data.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Time-Series/Data Streams
• Planning, Routing, and Scheduling: Temporal Planning

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Tropical cyclone (TC) forecasting is crucial for disaster preparedness and mitigation. While recent deep learning approaches have shown promise, existing methods often treat TC evolution as a series of independent frame-to-frame predictions, limiting their ability to capture long-term dynamics. We present a novel application of video diffusion models for TC forecasting that explicitly models temporal dependencies through additional temporal layers. Our approach enables the model to generate multiple frames simultaneously, better capturing cyclone evolution patterns. We introduce a two-stage training strategy that significantly improves individual-frame quality and performance in low-data regimes. Experimental results show our method outperforms the previous approach of Nath et al. by 19.3% in MAE, 16.2% in PSNR, and 36.1% in SSIM. Most notably, we extend the reliable forecasting horizon from 36 to 50 hours. Through comprehensive evaluation using both traditional metrics and Fr'echet Video Distance (FVD), we demonstrate that our approach produces more temporally coherent forecasts while maintaining competitive single-frame quality. Code accessible at https://github.com/Ren-creater/forecast-video-diffmodels.

Problem

• Typhoon Trend Prediction
• Limited Data
• Disaster Prevention

Innovation

• Temporal Layer
• Multi-frame Prediction
• Cyclone Forecasting