Earth-o1 learns continuous atmospheric dynamics from ungridded observations and matches operational IFS forecast skill in hindcasts.
arXiv preprint arXiv:2402.00059 (2024)
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A generative 3D Gaussian splatting model with scale-aware attention enables unified arbitrary-resolution forecasting and downscaling of 87 atmospheric variables.
STCast introduces Spatial-Aligned Attention and Temporal Mixture-of-Experts modules to adaptively refine regional boundaries in data-driven weather forecasting and reports better performance than prior methods on global, regional, extreme-event, and ensemble tasks.
citing papers explorer
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Earth-o1: A Grid-free Observation-native Atmospheric World Model
Earth-o1 learns continuous atmospheric dynamics from ungridded observations and matches operational IFS forecast skill in hindcasts.
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Generative 3D Gaussian Splatting for Arbitrary-ResolutionAtmospheric Downscaling and Forecasting
A generative 3D Gaussian splatting model with scale-aware attention enables unified arbitrary-resolution forecasting and downscaling of 87 atmospheric variables.
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STCast: Adaptive Boundary Alignment for Global and Regional Weather Forecasting
STCast introduces Spatial-Aligned Attention and Temporal Mixture-of-Experts modules to adaptively refine regional boundaries in data-driven weather forecasting and reports better performance than prior methods on global, regional, extreme-event, and ensemble tasks.