用机器学习统一短中程天气预报，提升美国地区预测精度。
Bridging short- and medium-range weather forecasting with machine learning
- 构建0.25°全球模型+6km区域细化的嵌套架构。
- 近地面变量误差低于美国气象局现有系统，风暴位置预测更准。
- 适合关注区域天气精准预报的研究者与气象应用开发者。
美国国家海洋和大气管理局（NOAA）采用独立系统分别进行短程与中程天气预报。我们主张将两者整合为统一预测体系，以更好呈现全球天气及其影响。为此，提出Nested-EAGLE（实验性人工智能全球与有限区域集合模型）：一个0.25°分辨率的全球天气模型，在美国本土（CONUS）实现6 km精细化模拟。该模型在CONUS地区近地面和低层大气变量上的均方误差显著低于NOAA的全球预报系统（GFS）和高分辨率快速刷新模型（HRRR），且在全球其他区域保持竞争力。近地面场技能提升源于嵌套训练中引入高分辨率区域分析数据。降水总量预测不如HRRR，因采用确定性训练；但长期预报中风暴位置预测最为准确，尽管极端值略有模糊。结果表明未来需拓展技能优势至非CONUS区域并改进降水表征。
原文摘要 · Abstract (English)
The National Oceanic and Atmospheric Administration (NOAA) employs independent prediction systems for distinct forecast products. While some separation is practical, we argue that combining short- and medium-range weather into a single prediction system would provide the public with a useful distillation of global weather and its impacts. To this end, we present Nested-EAGLE (Experimental Artificial intelligence Global and Limited-area Ensemble): a 0.25° global weather model with a 6 km refinement over the Contiguous United States (CONUS). The model achieves significantly lower mean-squared error in near-surface and low-level quantities over CONUS compared to NOAA's Global Forecast System and High-Resolution Rapid Refresh (HRRR), while remaining competitive throughout the rest of the global atmosphere. We show that the skill gains for near-surface fields stem from incorporating high-resolution regional analysis data into training through the nesting process. Forecasts of precipitation amounts are less skillful than those from HRRR, owing to deterministic training. However, we show that Nested-EAGLE provides the most accurate forecasts of storm locations at longer leads, despite blurred extrema. Our results motivate future work to extend the skill gains beyond CONUS and improve precipitation representation.
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