The development of Time-Series Forecasting (TSF) models is often constrained by the lack of comprehensive datasets, especially in Global Station Weather Forecasting (GSWF), where existing datasets are small, temporally short, and spatially sparse. To address this, we introduce WEATHER-5K, a large-scale observational weather dataset that better reflects real-world conditions, supporting improved model training and evaluation. While recent TSF methods perform well on benchmarks, they lag behind operational Numerical Weather Prediction systems in capturing complex weather dynamics and extreme events. We propose PhysicsFormer, a physics-informed forecasting model combining a dynamic core with a Transformer residual to predict future weather states. Physical consistency is enforced via pressure-wind alignment and energy-aware smoothness losses, ensuring plausible dynamics while capturing complex temporal patterns. We benchmark PhysicsFormer and other TSF models against operational systems across several weather variables, extreme event prediction, and model complexity, providing a comprehensive assessment of the gap between academic TSF models and operational forecasting. The dataset and benchmark implementation are available at: https://github.com/taohan10200/WEATHER-5K.

翻译：[translated abstract in Chinese] 时间序列预测（TSF）模型的发展常受限于缺乏综合数据集，尤其是在全球站点天气预报（GSWF）领域——现有数据集规模小、时间跨度短且空间分布稀疏。为解决此问题，我们提出WEATHER-5K——一个更真实反映实际环境、支持改进模型训练与评估的大规模观测气象数据集。尽管近期TSF方法在基准测试中表现良好，但在捕获复杂天气动态与极端事件方面仍落后于业务化数值天气预报系统。我们提出物理信息预测模型PhysicsFormer，该模型融合动态核心与Transformer残差模块以预测未来天气状态。通过气压-风速一致性约束与能量感知平滑损失确保物理一致性，从而在捕获复杂时间模式的同时实现合理动力学过程。我们针对多种气象变量、极端事件预测及模型复杂度，将PhysicsFormer与其他TSF模型与业务化系统进行基准比对，全面评估了学术TSF模型与业务化预报之间的差距。数据集与基准实现请参见：https://github.com/taohan10200/WEATHER-5K。