Mosaic introduces mesh-aligned block-sparse attention and learned functional perturbations to preserve spectral fidelity in probabilistic 1.5° weather forecasts, matching or exceeding finer-resolution models while producing well-calibrated ensembles.
All metrics are evaluated at 1.5° resolution
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(Sparse) Attention to the Details: Preserving Spectral Fidelity in ML-based Weather Forecasting Models
Mosaic introduces mesh-aligned block-sparse attention and learned functional perturbations to preserve spectral fidelity in probabilistic 1.5° weather forecasts, matching or exceeding finer-resolution models while producing well-calibrated ensembles.