大气河事件期间AI与NWP降水预报的全球评估

中文摘要 AI 辅助

大气河（AR）造成了世界上许多最极端的降水事件和水文气象灾害。尽管人工智能天气预报（AIWP）模型在大尺度大气变量方面已展现出与数值天气预报（NWP）系统相当或更优的技能，但其对与大气河相关降水的预报能力在全球范围内仍未得到充分表征。在此，我们评估了全球预报系统（GFS）、全球集合预报系统（GEFS）、GraphCast和人工智能预报系统（AIFS）从第1天到第10天的24小时降水预报，评估范围覆盖全球以及北美、欧洲、澳大利亚和新西兰。利用极端天气基准框架，将预报与GPM综合多卫星检索（IMERG）观测数据进行对比，评估指标包括降水强度、空间结构和定位。GraphCast和AIFS在空间技能上优于GFS和GEFS，尤其是在强降水及更长预报时效方面，并能更好地保持与大气河相关降水直至第10天的空间组织。然而，这种改进的空间技能并未转化为准确的降水强度。AIWP模型倾向于高估中等到强降水累积量，而在较长预报时效下低估最强降水，而NWP系统则出现明显的干偏差。这些结果揭示了AIWP在高影响降水预报中的独特优势和局限性，并提供了一个可复现的基准。

英文摘要

Atmospheric rivers (ARs) produce many of the world's most extreme precipitation events and hydrometeorological hazards. Although artificial intelligence weather prediction (AIWP) models have demonstrated skill comparable to or exceeding numerical weather prediction (NWP) systems for large-scale atmospheric variables, their ability to forecast AR-related precipitation remains insufficiently characterized globally. Here, we evaluate 24-hour precipitation forecasts from the Global Forecast System (GFS), Global Ensemble Forecast System (GEFS), GraphCast, and Artificial Intelligence Forecasting System (AIFS) from Day 1 through Day 10 globally and across North America, Europe, and Australia and New Zealand. Using the Extreme Weather Bench framework, forecasts are evaluated against Integrated Multi-satellitE Retrievals for GPM (IMERG) observations using measures of precipitation magnitude, spatial structure, and localization. GraphCast and AIFS exhibit greater spatial skill than GFS and GEFS, particularly for heavy precipitation and at longer lead times, and better preserve the spatial organization of AR-related precipitation through Day 10. However, this improved spatial skill does not translate into accurate precipitation magnitudes. AIWP models tend to overpredict moderate-to-heavy accumulations while underpredicting the heaviest precipitation at longer lead times, whereas NWP systems develop pronounced dry biases. These results reveal distinct strengths and limitations of AIWP for high-impact precipitation forecasting and provide a reproducible benchmark.

发表机构

• University of Oklahoma（俄克拉荷马大学）
• NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography（美国国家科学基金会天气、气候与沿海海洋学可信人工智能研究人工智能研究所）
• Brightband

机构由 AI 辅助整理，请以论文原文为准。