无需物理模型数据，直接从观测数据生成高精度短期天气预报。
Skillful high-resolution weather forecasting independent of physical models
- 纯观测数据训练，不依赖任何气象数值模型结果。
- 在美国和欧洲地区，18小时内预报精度超越现有业务数值模型。
- 适合缺乏历史再分析数据的区域，部署成本低、适应性强。
精准及时的天气预报对现代社会至关重要。基于机器学习的气象预测正在成为生成初始条件、预报结果甚至端到端系统的替代方案，其速度更快且性能通常优于传统数值天气预报（NWP）。然而，即使端到端模型也常依赖NWP生成的再分析数据进行监督，继承了其偏差与分辨率限制，并难以在缺乏合适再分析产品、更新频率低或生产成本高的场景中应用。本文提出ObsCast，一个区域系统，可在训练和推理中完全不使用任何来自NWP的数据，同时仍实现短时高分辨率区域建模的最先进性能。在美利坚合众国及欧洲地区，ObsCast在近地面变量上18小时内的预报表现优于现有业务级NWP系统，并能生成有效降水预报。该方法为直接从本地观测数据构建和优化区域预报服务提供了更简洁、更具适应性的路径，无需建立复杂且昂贵的传统预报流程。
原文摘要 · Abstract (English)
Accurate and timely weather forecasts are critical for high-impact decisions in modern society. Machine-learning-based weather prediction is emerging as an alternative for producing initial conditions, forecasts, and even both in end-to-end systems. These methods deliver predictions faster and often with higher skill than traditional numerical weather prediction (NWP). However, even end-to-end models typically rely on NWP-generated reanalyses for supervision, thereby inheriting the biases and resolution limitations of those NWPs, and limiting adaptation to settings where suitable reanalysis products are unavailable, infrequently updated, or expensive to produce. Here we introduce ObsCast, a regional system that generates both analysis and predictions, without using any NWP-derived data in either training or inference, while still achieving state-of-the-art performance in short-term high-resolution regional modeling. Over the contiguous United States and Europe, ObsCast outperforms operational NWP for near-surface variables through 18 h and produces skillful precipitation forecasts. It provides a simpler and more adaptable route to build and refine regional forecasting services directly from local observations, without the need to develop complex and costly traditional forecasting pipelines.
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