Guided Generative Models for Rare Event Likelihood Estimation

This feature thread tracks the development and application of guided generative models, particularly diffusion models, for efficiently estimating the likelihood of rare, high-impact events. Initial work focused on climate science, using models like NVIDIA cBottle and tools like Earth2Studio, to steer generative processes towards extreme weather phenomena (e.g., tropical cyclones) and then employing odds-ratio diagnostics to correct for sampling bias. Subsequent developments have demonstrated the effectiveness of Score-Based Data Assimilation (SDA) for regional models, constraining diffusion models with point observations to improve forecasts and reduce error metrics like wind-speed RMSE. HealDA has been introduced for global atmospheric state estimation in seconds by mapping heterogeneous remote-sensing and in situ observations onto a HEALPix grid. Earth2Studio data sources provide unified access to various observation types for developing and validating weather models.