WIND-Bench：美国本土近地表风速原位观测基准数据集

中文摘要 AI 辅助

准确的风速预报对于业务决策和公共安全至关重要，然而在复杂地形中，预报往往难以捕捉近地表的高风速。为此，机器学习（ML）天气预报方法的进展已证明其能够提升预报技巧，超越传统的数值天气预报（NWP）模型。然而，缺乏一个基准数据集来利用复杂地形中足够且质量受控的风速观测数据评估NWP和ML模型，这对美国本土（CONUS）高质量地表风速预报的开发与相互比较构成了挑战。我们开发了风原位数据基准（WIND-Bench），这是一个源自气象同化数据摄取系统（MADIS）观测网络中原地观测的基准数据集。WIND-Bench整合了多个传感器网络，并通过质量控制区分传感器故障与高风条件，其框架将观测数据与美国国家海洋和大气管理局（NOAA）高分辨率快速刷新（HRRR）模型的预报进行验证。WIND-Bench为评估ML和NWP模型以及量化预报技巧提供了标准化基准，加速了高技巧近地表风速预报的开发、评估和业务部署。

英文摘要

Accurate wind forecasts are essential for operational decision-making and public safety, yet forecasts tend to miss near-surface high wind speeds in complex terrain. In response, advances in machine learning (ML) weather prediction methods have demonstrated the ability to improve forecast skill beyond traditional numerical weather prediction (NWP) models. However, the absence of a benchmark dataset to evaluate NWP and ML models with sufficient, quality-controlled wind speed observations in complex terrain poses challenges to the development and intercomparison of high-quality surface wind forecasts across the Conterminous United States (CONUS). We develop the Wind IN-situ Data Benchmark (WIND-Bench), a benchmark dataset from in-situ observations in the Meteorological Assimilation Data Ingest System (MADIS) observational network. WIND-Bench integrates multiple sensor networks with quality control that distinguishes sensor failures from high-wind conditions, using a framework that validates observations against forecasts from the National Oceanic and Atmospheric Administration (NOAA) High-Resolution Rapid Refresh (HRRR) model. WIND-Bench provides a standardized benchmark for evaluating ML and NWP models and for quantifying forecast skill, accelerating the development, evaluation, and operational deployment of skilled near-surface wind forecasts.

发表机构

• NSF ASCEND Engine（美国国家科学基金会ASCEND引擎）
• Strategic Energy Analysis Center, National Laboratory of the Rockies（落基山国家实验室战略能源分析中心）
• Global Systems Laboratory, National Oceanic and Atmospheric Administration（美国国家海洋和大气管理局全球系统实验室）
• Cooperative Institute for Research in the Atmosphere（大气合作研究所）

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