Super-Resolving Coarse-Resolution Weather Forecasts With Flow Matching

• URL: http://arxiv.org/abs/2604.00897v1
• Date: Wed, 01 Apr 2026 13:43:42 GMT
• Title: Super-Resolving Coarse-Resolution Weather Forecasts With Flow Matching
• Abstract summary: Machine learning-based weather forecasting models now surpass state-of-the-art numerical weather prediction systems.<n>We present a modular framework that decouples forecasting from resolution by applying learned generative super-resolution as a post-processing step to coarse-resolution forecast trajectories.<n>We evaluate (i) design consistency by re-coarsening super-resolved forecasts and comparing them to the original coarse trajectories, and (ii) high-resolution forecast quality using standard ensemble verification metrics and spectral diagnostics.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine learning-based weather forecasting models now surpass state-of-the-art numerical weather prediction systems, but training and operating these models at high spatial resolution remains computationally expensive. We present a modular framework that decouples forecasting from spatial resolution by applying learned generative super-resolution as a post-processing step to coarse-resolution forecast trajectories. We formulate super-resolution as a stochastic inverse problem, using a residual formulation to preserve large-scale structure while reconstructing unresolved variability. The model is trained with flow matching exclusively on reanalysis data and is applied to global medium-range forecasts. We evaluate (i) design consistency by re-coarsening super-resolved forecasts and comparing them to the original coarse trajectories, and (ii) high-resolution forecast quality using standard ensemble verification metrics and spectral diagnostics. Results show that super-resolution preserves large-scale structure and variance after re-coarsening, introduces physically consistent small-scale variability, and achieves competitive probabilistic forecast skill at 0.25° resolution relative to an operational ensemble baseline, while requiring only a modest additional training cost compared with end-to-end high-resolution forecasting.

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