arXiv AI
1d ago
Pushing the Limits of High-Resolution Weather Forecasting through Data Scaling
arXiv:2608. 14652v1 Announce Type: cross Abstract: The development of 0.
Scaling Laws of Global Weather Models
arXiv:2602. 22962v2 Announce Type: replace Abstract: Data-driven models are revolutionizing weather forecasting.
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Can Deep Neural Networks Improve Compression of Very Large Scientific Data?
arXiv:2606. 14353v1 Announce Type: new Abstract: Error-bounded lossy compression is a fundamental technique for managing the rapidly growing volumes of scientific data produced by modern simulations and observational instruments.
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Benchmarking Physics-Informed Time-Series Models for Operational Global Station Weather Forecasting
arXiv:2406. 14399v4 Announce Type: replace Abstract: The development of Time-Series Forecasting (TSF) models is often constrained by the lack of comprehensive datasets, especially in Global Station Weather Forecasting (GSWF), where existing datasets are small, temporally short, and spatially sparse.
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Otter Weather: Skillful and Computationally Efficient Medium-Range Weather Forecasting
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From Global to Local: Efficient Regional Weather Downscaling with Global Weather Foundation Model
arXiv:2607. 03279v1 Announce Type: new Abstract: Accurate regional weather prediction requires resolving fine-scale structure while remaining consistent with global dynamics.
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U-Cast: A Surprisingly Simple and Efficient Frontier Probabilistic AI Weather Forecaster
arXiv:2604. 09041v2 Announce Type: replace-cross Abstract: AI-based weather forecasting now rivals traditional physics-based ensembles, but state-of-the-art (SOTA) models rely on specialized architectures and massive computational budgets, creating a high barrier to entry.
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Otter Weather: Skillful and Computationally Efficient Medium-Range Weather Forecasting
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arXiv Machine Learning
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Weather Emulators at the Frontier of Heat Extremes Predictability
arXiv:2607. 28220v1 Announce Type: cross Abstract: Atmospheric predictability declines rapidly beyond the next ten days, such that forecasts at longer lead times primarily convey large-scale trends rather than specific states.
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Weather Emulators at the Frontier of Heat Extremes Predictability
Atmospheric predictability declines rapidly beyond the next ten days, such that forecasts at longer lead times primarily convey large-scale trends rather than specific states. Yet in a warming world, improving early warnings of extreme heat is an increasingly critical challenge.
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Nipping the Butterfly Effect in the Bud: Self-Output Fine-Tuning for Autoregressive Weather Prediction
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Breaking the Statistical Similarity Trap in Extreme Convection Detection
arXiv:2509. 09195v2 Announce Type: replace Abstract: Current evaluation metrics for deep learning weather models create a "Statistical Similarity Trap", rewarding blurry predictions while missing rare, high-impact events.
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Timestep-Conditioned Transformers for Global Weather Forecasting
arXiv:2608. 06241v1 Announce Type: new Abstract: Existing machine-learning weather forecasting models rely on predetermined and fixed autoregressive timesteps.