Since its inception in the 1940s, Monin-Obukhov similarity theory (MOST), which relates turbulent fluxes to mean vertical gradients in the lower atmosphere, has become ubiquitous for predicting surface fluxes of quantities transported by the flow in numerical weather, climate, and hydrological forecasting models. Despite its widespread use, MOST does not account for the effects of large coherent structures in the flow, which modulate the amplitude of turbulent fluctuations, and are responsible for a large fraction of the total transport. Herein, we demonstrate that the incorporation of the large-scale streamwise velocity $u_l(\mathit{x},t)=G_{\delta }\star u(\mathit{x},t)$, where $G_{\delta }$ is a low-pass filtering kernel, into dimensional analysis leads to an additional dimensionless parameter $\alpha (\mathit{x},t)$, which captures the modulating influence of these structures on flux-gradient relationships. Atmospheric observations and large-eddy simulations are used to demonstrate that observed deviations from MOST can indeed be explained by this new parameter; coherent structures induce an alternating loading and unloading of the mean velocity gradient near the surface.

中文翻译：

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相干结构调节大气表层通量-梯度关系

自1940年代创立以来，将湍流与低层大气中的平均垂直梯度相关联的Monin-Obukhov相似性理论（MOST）在预测数值天气，气候和水文中由水流传输的表面通量时已无处不在。预测模型。尽管已广泛使用，但MOST并未考虑流动中大型相干结构的影响，后者会调节湍流涨落的幅度，并占总运输量的很大一部分。在这里，我们证明了大规模流向速度的结合${ü}_{升}（\mathit{X}，Ť）=G_{\delta }\star ü（\mathit{X}，Ť）$，在哪里 $G_{\delta }$ 是低通滤波内核，进入维分析会导致附加的无量纲参数 $\alpha （\mathit{X}，Ť）$，它捕获了这些结构对通量-梯度关系的调制影响。大气观测和大涡模拟被用来证明观测到的与MOST的偏差确实可以用这个新参数来解释。相干结构在表面附近引起平均速度梯度的交替加载和卸载。