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Towards accurate extreme event likelihoods from diffusion model climate emulators

Researchers have developed a method to estimate the likelihood of extreme weather events using diffusion models, which are typically used for image generation. The "Climate in a Bottle" (cBottle) model can be guided to simulate specific events like tropical cyclones. By comparing the probability densities of guided versus unguided simulations, scientists can quantify the increased likelihood of these extreme events and improve sampling efficiency for probability estimates. AI

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IMPACT This research could lead to more accurate climate change impact assessments and improved extreme weather event prediction.

RANK_REASON The cluster contains an academic paper detailing a new methodology for climate modeling.

Read on arXiv cs.LG →

COVERAGE [2]

  1. arXiv cs.LG TIER_1 · Peter Manshausen, Noah Brenowitz, Julius Berner, Karthik Kashinath, Mike Pritchard ·

    Towards accurate extreme event likelihoods from diffusion model climate emulators

    arXiv:2605.03802v1 Announce Type: cross Abstract: ML climate model emulators are useful for scenario planning and adaptation, allowing for cost-efficient experimentation. Recently, the diffusion model Climate in a Bottle (cBottle) has been proposed for generation of atmospheric s…

  2. arXiv cs.LG TIER_1 · Mike Pritchard ·

    Towards accurate extreme event likelihoods from diffusion model climate emulators

    ML climate model emulators are useful for scenario planning and adaptation, allowing for cost-efficient experimentation. Recently, the diffusion model Climate in a Bottle (cBottle) has been proposed for generation of atmospheric states compatible with boundary conditions of solar…