用机器学习连接短期和中期天气预报

中文摘要 AI 辅助

美国国家海洋和大气管理局（NOAA）针对不同预报产品采用独立的预测系统。尽管一定的分离是可行的，但我们认为将短期和中期天气预报整合到单一预测系统中，能为公众提供全球天气及其影响的有用提炼。为此，我们提出Nested-EAGLE（Experimental Artificial intelligence Global and Limited-area Ensemble，即实验性人工智能全球与区域集合模型）：这是一个全球分辨率0.25°的天气模型，在美国本土（CONUS）区域细化至6km。该模型在CONUS区域的近地面和低层变量上，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系统（HRRR），同时在全球其他大气区域表现相当。我们表明，近地面场的技巧提升源于通过嵌套过程将高分辨率区域分析数据纳入训练。由于确定性训练，其降水量预报的技巧低于HRRR；但我们发现，尽管极值模糊，Nested-EAGLE在较长预报时效下对风暴位置的预报最为准确。我们的结果为未来扩展技巧至CONUS以外区域、改进降水表征的研究提供了动力。

英文摘要

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.

发表机构

• National Oceanic and Atmospheric Administration (NOAA)（美国国家海洋和大气管理局）
• Nansen Environmental and Remote Sensing Center（南森环境与遥感中心）
• Bjerknes Center for Climate Research（比耶克内斯气候研究中心）
• Earth Prediction Innovation Center (EPIC)（地球预测创新中心）
• Cooperative Institute for Research in Environmental Sciences (CIRES) at the University of Colorado Boulder（科罗拉多大学博尔德分校环境科学合作研究所）
• University of Colorado Boulder（科罗拉多大学博尔德分校）

机构由 AI 辅助整理，请以论文原文为准。