West-WRF AI 2-km：积分水汽输送与降水的高分辨率预测

中文摘要 AI 辅助

我们提出了一种拉伸网格人工智能（AI）天气预报模型，其在美国西部和东北太平洋部分区域的分辨率为2公里，在全球其他区域的分辨率约为31公里。由于复杂地形和大气河（ARs）强烈影响地形降水，美国西部的预报具有挑战性。West-WRF AI 2-km基于一个使用40年欧洲中期天气预报中心再分析v5（ERA5）数据集预训练的全球模型，并使用西部天气与水极端事件中心（CW3E）的2公里区域再分析数据进行微调，以生成自回归的6小时降水与积分水汽输送（IVT）预报。使用2020-2023年冬季的网格化降水观测、雨量计和AR侦察下投探空仪对预报进行评估，并与较粗分辨率的AI预报以及区域和全球数值天气预报（NWP）系统进行基准比较。West-WRF AI 2-km再现了观测到的降水强度分布，保留了细尺度谱变率，并产生了更清晰的狭窄沿海降水带和局地化、地形敏感的极端事件。尽管分辨率更高，其大尺度性能仍与较粗分辨率的配置相当，同时保持了大尺度技能。下投探空仪验证显示，在最极端的IVT阈值下，误差更低，分类技能有所提高。总体而言，West-WRF AI 2-km在局地化降水极端事件和强AR相关水汽输送方面提供了最大价值。

英文摘要

We introduce a stretched-grid artificial intelligence (AI) weather forecasting model with 2-km resolution over the western United States and part of the Northeast Pacific and approximately 31-km resolution elsewhere globally. Forecasting over the western U.S. is challenging because complex topography and atmospheric rivers (ARs) strongly influence orographic precipitation. West-WRF AI 2-km builds on a global model pretrained with a 40-year European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) dataset and is fine-tuned with the Center for Western Weather and Water Extremes (CW3E) 2-km regional reanalysis to produce autoregressive 6-hourly forecasts of precipitation and integrated vapor transport (IVT). Forecasts are evaluated over winters 2020-2023 using gridded precipitation observations, rain gauges, and AR Reconnaissance dropsondes and are benchmarked against coarser-resolution AI forecasts and regional and global numerical weather prediction (NWP) systems. West-WRF AI 2-km reproduces observed precipitation-intensity distributions, retains fine-scale spectral variability, and produces sharper narrow coastal precipitation bands and localized, terrain-sensitive extremes. Its broader-scale performance remains comparable to coarser-resolution configurations while preserving large-scale skill despite higher resolution. Dropsonde verification shows lower errors and improved categorical skill at the most extreme IVT threshold. Overall, West-WRF AI 2-km provides its greatest value for localized precipitation extremes and intense AR-related moisture transport.

发表机构

• University of California, Irvine（加州大学欧文分校）
• University of California, San Diego（加州大学圣地亚哥分校）
• Scripps Institution of Oceanography（斯克里普斯海洋研究所）

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