<ul><li>DEF (Diffusion-augmented Ensemble Forecasting) is a new approach for generating initial condition perturbations.</li><li>It aims to address limitations in existing methods primarily designed for numerical weather prediction solvers, making them less applicable to machine learning for weather prediction.</li><li>DEF utilizes a simple conditional diffusion model to generate structured perturbations iteratively, with a guidance term for controlling the perturbation level.</li><li>Validation on the ERA5 reanalysis dataset shows that DEF improves predictive performance and provides reasonable spread estimates for long-term forecasts.</li></ul>

DEF: Diffusion-augmented Ensemble Forecasting

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