FitzRoy 用强化学习改进扩散模型，提升降水预报精度
Skillful and realistic precipitation forecasting with reinforced diffusion
一篇把强化学习和扩散模型结合做降水预报的论文，叫 FitzRoy，能预报极端暴雨还不会像传统 ML 那样把结果画糊，做气象 AI 的可以看看。
arXiv 论文提出降水预报系统 FitzRoy，采用条件离散扩散模型并通过强化学习微调。在美国大陆范围内，FitzRoy 在最长 24 小时预报时效内达到或超过现有 state-of-the-art 模型，同时保留降水的结构真实感。案例研究显示，在 2025 年德州丘陵地区洪灾和 2026 年密苏里洪灾中，FitzRoy 捕捉到了其他模型大多漏报的破纪录降水量。其低延迟推理支持实时业务部署。
Skillful and realistic precipitation forecasting with reinforced diffusion
Accurate precipitation forecasting remains elusive as physics-based models struggle to resolve mesoscale convection and machine learning often yields physically inconsistent, blurred results that consistently underestimate extreme events. We present FitzRoy, the first system that simultaneously achieves state-of-the-art predictive skill and physical realism over the continental United States. Through a reinforced diffusion framework, where a conditional discrete diffusion model is refined via reinforcement learning, FitzRoy effectively resolves the long-standing trade-off between pointwise accuracy and structural fidelity. Across lead times up to 24 hours, FitzRoy consistently matches or exceeds state-of-the-art models while preserving structural realism and capturing extreme events. Its low-latency inference further enables deployment in real-time operational settings. In case studies such as the 2025 Texas Hill Country floods and the 2026 Missouri floods, FitzRoy signals record-breaking accumulations that existing models largely missed. By bridging the gap between realism and skill, FitzRoy establishes a new frontier for high-fidelity precipitation forecasting.