Conditional diffusion models for weather and wave ensemble forecasting

Abstract

This study aims to develop a high-precision, low-cost probabilistic weather and wave forecasting system using deep learning to enhance the safety of offshore construction projects. We developed a model based on conditional diffusion models to predict weather and wave fields for the next timestep. Furthermore, we propose a framework for short-term ensemble forecasting that suppresses computational costs by introducing an ensemble pruning method based on clustering and applying the model autoregressively. The results demonstrate that appropriate variable selection contributes to improved prediction accuracy. In typhoon case studies, the model generated diverse track patterns, showing a spread of predictions that cannot be captured by a single forecast. We also confirmed its low-resource and high-speed computational performance, indicating that the method is practical even in environments with limited computational resources.