AI-based weather forecasting now rivals traditional physics-based ensembles, but state-of-the-art (SOTA) models rely on specialized architectures and massive computational budgets, creating a high barrier to entry. We demonstrate that such complexity is unnecessary for frontier performance. We introduce U-Cast, a probabilistic forecaster built on a standard U-Net backbone trained with a simple recipe: deterministic pre-training on Mean Absolute Error followed by short probabilistic fine-tuning on the Continuous Ranked Probability Score (CRPS) using Monte Carlo Dropout for stochasticity. As a result, our model matches or exceeds the probabilistic skill of GenCast and IFS ENS at 1.5$^\circ\$ resolution while reducing training compute by over 10$\times$ compared to leading CRPS-based models and inference latency by over 10$\times$ compared to diffusion-based models. U-Cast trains in under 12 H200 GPU-days and generates a 60-step ensemble forecast in 11 seconds. These results suggest that scalable, general-purpose architectures paired with efficient training curricula can match complex domain-specific designs at a fraction of the cost, opening the training of frontier probabilistic weather models to the broader community. Our code is available at: https://github.com/Rose-STL-Lab/u-cast.

翻译：基于AI的天气预报现已能与传统物理集合预报相媲美，但当前最先进的模型依赖于专门架构和巨大计算预算，构成了高准入门槛。我们证明前沿性能无需这种复杂性。本文提出U-Cast——一种基于标准U-Net主干构建的概率预报器，采用简洁的训练方案：先以平均绝对误差进行确定性预训练，随后利用蒙特卡洛丢包法引入随机性，基于连续分级概率评分（CRPS）进行短时概率微调。结果表明，在1.5°分辨率下，我们的模型在概率技能上与GenCast和IFS ENS相当或更优，同时相比基于CRPS的领先模型训练计算量降低超10倍，相比基于扩散模型推理延迟降低超10倍。U-Cast在不到12个H200 GPU天内完成训练，并在11秒内生成60步集合预报。这些结果表明，可扩展的通用架构与高效训练策略相结合，能以极低成本匹敌复杂的领域专用设计，从而向更广泛社区开放前沿概率天气模型的训练。代码已开源：https://github.com/Rose-STL-Lab/u-cast。