WindINR achieves continuous high-resolution local wind queries and sparse-observation correction in complex terrain by updating only a compact latent state, delivering 2.6x speedup over full-network fine-tuning in OSSEs over Senja.
Lo-sda: Latent optimization for score-based atmospheric data assimilation
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HealDA supplies ML-based initial conditions for AI weather models that produce forecasts trailing ERA5-initialized runs by less than one day of effective lead time, with the skill gap arising mainly from initial error size.
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WindINR: Latent-State INR for Fast Local Wind Query and Correction in Complex Terrain
WindINR achieves continuous high-resolution local wind queries and sparse-observation correction in complex terrain by updating only a compact latent state, delivering 2.6x speedup over full-network fine-tuning in OSSEs over Senja.
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HealDA: Highlighting the importance of initial errors in end-to-end AI weather forecasts
HealDA supplies ML-based initial conditions for AI weather models that produce forecasts trailing ERA5-initialized runs by less than one day of effective lead time, with the skill gap arising mainly from initial error size.