用于低海拔微气象学的近实时米级三维城市风建模：GPU加速格子玻尔兹曼框架的数值验证

中文摘要 AI 辅助

本研究提出了一个用于复杂城市气象环境中低空飞行事件的近实时米级三维城市风模拟框架。它通过将稀疏观测与高效的微尺度流建模相结合来重建高分辨率风场。该框架将格子玻尔兹曼方法大涡模拟（LBM-LES）、明确解析真实建筑细节的高保真城市形态重建以及观测驱动的边界同化集成到一个用于真实城市区域的快速端到端管道中。来自中国广州密集城区的多站点多普勒激光雷达测量数据用于评估。该系统在几分钟内就能在千米尺度区域上以5米分辨率重建三维风场。通过控制观测减少、针对保留的激光雷达站点进行独立验证以及对网格分辨率和前体区域范围进行敏感性分析来测试其鲁棒性和准确性。结果表明，在复杂形态和有限观测条件下，该系统能够稳定地再现垂直风结构和关键局部流特征，为近实时城市风重建提供了一条可扩展的途径。

英文摘要

This study presents a near-real-time, meter-scale three-dimensional urban wind simulation framework for low-altitude flight events in complex urban meteorological environments. It reconstructs high-resolution wind fields by combining sparse observations with efficient microscale flow modeling. The framework integrates lattice Boltzmann method large-eddy simulation (LBM-LES), high-fidelity urban morphology reconstruction that explicitly resolves real building details, and observation-driven boundary assimilation into a rapid end-to-end pipeline for realistic urban domains. Multi-site Doppler lidar measurements from dense urban Guangzhou, China, are used for evaluation. The system reconstructs three-dimensional wind fields at 5 m resolution over kilometer-scale domains within minutes. Robustness and accuracy are tested through controlled observation reduction, independent validation against withheld lidar stations, and sensitivity analyses of grid resolution and precursor domain extent. Results show stable reproduction of vertical wind structures and key local flow features under complex morphology and limited observations, providing a scalable pathway for near-real-time urban wind reconstruction.

发表机构

• National Meteorological Information Centre, China Meteorological Administration（中国气象局国家气象信息中心）
• Department of Mechanical and Aerospace Engineering, The University of Manchester（曼彻斯特大学机械与航空航天工程系）
• College of Automotive and Energy Engineering, Tongji University（同济大学汽车与能源工程学院）
• Department of Civil and Environmental Engineering, University of California Berkeley（加州大学伯克利分校土木与环境工程系）
• Department of Building Environment, National University of Singapore（新加坡国立大学建筑环境系）
• School of Artificial Intelligence, Sun Yat-sen University（中山大学人工智能学院）
• Ralph O’Connor Sustainable Energy Institute, Johns Hopkins University（约翰斯·霍普金斯大学 Ralph O'Connor 可持续能源研究所）
• The Key Laboratory of Road and Traffic Engineering of Ministry of Education, Tongji University（同济大学道路与交通工程教育部重点实验室）

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