Author

N. Mücke

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#machine learning Preprint Oct 2026

Xaurora: Generative Weather Forecasting with Denoising Stochastic Interpolants from a Foundation Model Prior

Deep learning has revolutionised weather forecasting in recent years, especially through atmospheric foundation models, which offer competitive skill for a fraction of the computational costs of classic physics-based models. However, most existing foundation models are deterministic, limiting the generation of large en...

Eliot Walt, Miltiadis Kofinas, N. Mücke et al. · 0 citations

#machine learning Preprint Oct 2026

A Unified Framework for Bayesian Data Assimilation with Generative Models and Observation Interpolants

Bayesian data assimilation combines model forecasts with noisy observations, but sampling high-dimensional, non-Gaussian posteriors remains challenging. We introduce an observation-interpolant framework that turns pretrained stochastic interpolant, flow matching, and diffusion models into posterior samplers without ret...

N. Mücke, Benjamin Sanderse · 0 citations