Numerical weather prediction is often used to supply the mean wind and turbulence fields for atmospheric transport and dispersion plume models as they provide dense geographic coverage in comparison to typically sparse monitoring networks. Here, the Weather Research and Forecasting (WRF) model 4.0 was run over the month-long period of the Joint Urban 2003 field campaign conducted in Oklahoma City. We compare three different simulations in their ability to reproduce the observations, each using a different boundary-layer parametrization. Specifically, we examine the Mellor–Yamada–Janjic (MYJ), Yonsei University (YSU), and Mellor–Yamada–Nakanishi–Niino (MYNN) boundary-layer parametrizations. All three predict the wind speed well during the day but overpredict it at night. The MYNN parametrization is better than MYJ at predicting the daytime turbulence in the surface layer, but both underpredict the nocturnal turbulence. The MYJ parametrization is best at predicting the reciprocal Obukhov length, while MYNN and YSU both significantly overpredict thermal stability. Reconstructing the reciprocal Obukhov length from other simulated parameters produces more accurate values for both parametrizations. All three models overpredict the boundary-layer height, particularly under convective conditions. The MYJ parametrization overestimates boundary-layer height the most, while YSU and MYNN have comparable performance with MYNN having an advantage in predicting the stable boundary-layer height. Several days were found where the WRF simulations predict significant deviations from the prevailing diurnal pattern in wind direction, which are not found in the observations.

数值天气预报通常用于为大气传输和扩散羽流模型提供平均风场和湍流场，因为与通常稀疏的监测网络相比，它们提供了密集的地理覆盖范围。在这里，天气研究和预报 (WRF) 模型 4.0 在俄克拉荷马城进行的为期一个月的 2003 年联合城市实地活动期间运行。我们比较了三种不同的模拟重现观察结果的能力，每一种都使用不同的边界层参数化。具体来说，我们检查了 Mellor-Yamada-Janjic (MYJ)、延世大学 (YSU) 和 Mellor-Yamada-Nakanishi-Niino (MYNN) 边界层参数化。这三个人都很好地预测了白天的风速，但在晚上却高估了它。MYNN 参数化在预测表层白天湍流方面优于 MYJ，但两者都低估了夜间湍流。MYJ 参数化最适合预测倒数 Obukhov 长度，而 MYNN 和 YSU 都显着高估了热稳定性。从其他模拟参数重建倒数 Obukhov 长度可以为两个参数化生成更准确的值。所有三个模型都高估了边界层高度，特别是在对流条件下。MYJ 参数化最高估了边界层高度，而 YSU 和 MYNN 具有与 MYNN 相当的性能，在预测稳定的边界层高度方面具有优势。几天发现，WRF 模拟预测风向与盛行的昼夜模式存在显着偏差，