发表机构
Massachusetts Institute of Technology; Brown University; ExxonMobil Technology and Engineering Company（麻省理工学院; 布朗大学; 埃克森美孚技术与工程公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对二叠纪盆地油气设施，提出多尺度WRF嵌套框架（3 km至40 m）结合观测松弛逼近同化现场风速，显著降低风速偏差（65%-87%）和均方根误差（33%-44%），为甲烷排放监测生成高分辨率风场。
AI 中文摘要
油气设施处准确的甲烷源定位和排放率估算需要能够解析站点尺度时空变异性的风场，而单个风速计或粗分辨率业务天气产品无法刻画这种变异性。我们为二叠纪盆地某设施开发了一个多尺度天气研究与预报（WRF）框架，该框架通过四个嵌套域（网格间距分别为3 km、1 km、200 m和40 m）动态降尺度每小时、3 km分辨率的高分辨率快速刷新（HRRR）场。两个外域使用行星边界层参数化，而两个内域以大涡模拟模式运行。来自两个现场风速计的一分钟风观测通过仅风观测松弛逼近在最内层域中被同化，并检验了嵌套反馈和湍流闭合方案选择的影响。针对2025年冬季和夏季时段的模拟，使用近地面风速时间序列、功率谱、空间场和统计指标进行评估。基线模拟再现了观测风事件的总体演变，但在弱风时段出现漂移。采用单向和双向嵌套的观测松弛逼近将一个传感器的风速绝对偏差降低了65%，另一个传感器降低了87%，同时相应的均方根误差分别降低了33%和44%。松弛逼近还增加了高频能量和解析的空间梯度，尽管湍流统计的改善并不均匀。这些结果展示了一条实用的基于物理的途径，用于生成高分辨率风场以支持甲烷羽流建模，同时强调需要独立的空间观测来验证同化传感器以外区域的准确性。
英文摘要
Accurate methane source localization and emission-rate estimation at oil and gas facilities require wind fields that resolve site-scale spatial and temporal variability, which cannot be characterized by a single anemometer or coarse operational weather products. We develop a multiscale Weather Research and Forecasting (WRF) framework for a Permian Basin facility that dynamically downscales hourly, 3 km High-Resolution Rapid Refresh (HRRR) fields through four nested domains with grid spacings of 3 km, 1 km, 200 m, and 40 m. The two outer domains use planetary-boundary-layer parameterization, whereas the two inner domains operate in large-eddy-simulation mode. One-minute wind observations from two on-site anemometers are assimilated through wind-only observational nudging in the innermost domain, and the effects of nesting feedback and turbulence-closure choices are examined. Simulations for winter and summer 2025 periods are evaluated using near-surface wind-speed time series, power spectra, spatial fields, and statistical metrics. The baseline simulation reproduces the broad evolution of observed wind events but drifts during weak-wind periods. Observational nudging with one- and two-way nesting reduces the absolute bias of wind-speed by %65 at one sensor and %87 at the other, while reducing the corresponding root-mean-square errors by %33 and %44. Nudging also increases high-frequency energy and resolved spatial gradients, although improvements in turbulence statistics are not uniform. These results demonstrate a practical physics-based pathway for generating high-resolution wind fields for methane-plume modeling while emphasizing the need for independent spatial observations to validate accuracy away from assimilated sensors.
