用可变分辨率网格提升区域天气预测精度，尤其在北欧地区表现优异。
Regional data-driven weather modeling with a global stretched-grid
- 基于图神经网络设计可变分辨率网格，重点提升目标区域解析度。
- 在北欧实现2.5公里空间、6小时时间分辨率的短时预报，优于原有系统。
- 适合需要高精度区域天气预测的研究与气象机构使用。
提出一种适用于区域天气预报的数据驱动模型（DDM），通过引入拉伸网格架构，在关注区域提升分辨率而全球其余部分保持较低分辨率。该模型基于图神经网络，天然支持任意多分辨率网格配置。模型在北欧短时天气预测任务中应用，实现2.5公里空间和6小时时间分辨率的预报。模型首先在43年全球ERA5数据（31公里分辨率）上预训练，再用3.3年梅特科普集合预报系统（MEPS）的2.5公里实况分析数据进行微调。性能基于挪威地面观测站数据评估，并与MEPS的控制预报和集合平均对比。结果表明，该模型在2米气温预报上优于两者；降水与风速预报也具竞争力，但对极端事件存在低估现象。
原文摘要 · Abstract (English)
A data-driven model (DDM) suitable for regional weather forecasting applications is presented. The model extends the Artificial Intelligence Forecasting System by introducing a stretched-grid architecture that dedicates higher resolution over a regional area of interest and maintains a lower resolution elsewhere on the globe. The model is based on graph neural networks, which naturally affords arbitrary multi-resolution grid configurations. The model is applied to short-range weather prediction for the Nordics, producing forecasts at 2.5 km spatial and 6 h temporal resolution. The model is pre-trained on 43 years of global ERA5 data at 31 km resolution and is further refined using 3.3 years of 2.5 km resolution operational analyses from the MetCoOp Ensemble Prediction System (MEPS). The performance of the model is evaluated using surface observations from measurement stations across Norway and is compared to short-range weather forecasts from MEPS. The DDM outperforms both the control run and the ensemble mean of MEPS for 2 m temperature. The model also produces competitive precipitation and wind speed forecasts, but is shown to underestimate extreme events.
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