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.
Appa: Bending weather dynamics with latent diffusion models for global data assimilation
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ForcingDAS is a diffusion-based data assimilation framework that learns joint-trajectory priors to unify filtering and smoothing while reducing error accumulation on non-Markovian observations.
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.
DroughtFormer predicts soil moisture, vegetation health, and related variables in Africa with skill out to 90 days that matches or exceeds climatology for most targets, but shows lower accuracy for precipitation and flash drought indices.
The paper reviews data sources, physical models, downstream applications, and AI techniques to outline considerations for building a foundation model for the Martian atmosphere.
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ForcingDAS: Unified and Robust Data Assimilation via Diffusion Forcing
ForcingDAS is a diffusion-based data assimilation framework that learns joint-trajectory priors to unify filtering and smoothing while reducing error accumulation on non-Markovian observations.