Flows in the atmospheric boundary layer are turbulent, characterized by a large Reynolds number, the existence of a roughness sublayer and the absence of a well-defined viscous layer. Exchanges with the surface are therefore dominated by turbulent fluxes. In numerical models for atmospheric flows, turbulent fluxes must be specified at the surface; however, surface fluxes are not known a priori and therefore must be parametrized. Atmospheric flow models, including global circulation, limited area models, and large-eddy simulation, employ Monin–Obukhov similarity theory (MOST) to parametrize surface fluxes. The MOST approach is a semi-empirical formulation that accounts for atmospheric stability effects through universal stability functions. The stability functions are determined based on limited observations using simple regression as a function of the non-dimensional stability parameter representing a ratio of distance from the surface and the Obukhov length scale (Obukhov in Trudy Inst Theor Geofiz AN SSSR 1:95–115, 1946), \(z/L\). However, simple regression cannot capture the relationship between governing parameters and surface-layer structure under the wide range of conditions to which MOST is commonly applied. We therefore develop, train, and test two machine-learning models, an artificial neural network (ANN) and random forest (RF), to estimate surface fluxes of momentum, sensible heat, and moisture based on surface and near-surface observations. To train and test these machine-learning algorithms, we use several years of observations from the Cabauw mast in the Netherlands and from the National Oceanic and Atmospheric Administration’s Field Research Division tower in Idaho. The RF and ANN models outperform MOST. Even when we train the RF and ANN on one set of data and apply them to the second set, they provide more accurate estimates of all of the fluxes compared to MOST. Estimates of sensible heat and moisture fluxes are significantly improved, and model interpretability techniques highlight the logical physical relationships we expect in surface-layer processes.

大气边界层中的流动是湍流的，其特点是雷诺数很大，存在粗糙子层，没有明确的粘性层。因此，与表面的交换以湍流为主。在大气流动的数值模型中，必须在表面指定湍流通量；然而，表面通量不是先验已知的，因此必须参数化。大气流动模型，包括全球环流、有限区域模型和大涡模拟，采用莫宁-奥布霍夫相似理论 (MOST) 来参数化表面通量。MOST 方法是一种半经验公式，它通过通用稳定性函数来解释大气稳定性的影响。\(z/L\). 然而，在 MOST 常用的广泛条件下，简单回归无法捕捉控制参数与表层结构之间的关系。因此，我们开发、训练和测试了两个机器学习模型，即人工神经网络 (ANN) 和随机森林 (RF)，以根据地表和近地表观测估计动量、显热和水分的地表通量。为了训练和测试这些机器学习算法，我们使用了荷兰 Cabauw 桅杆和爱达荷州国家海洋和大气管理局实地研究部塔楼多年的观测结果。RF 和 ANN 模型优于 MOST。即使我们在一组数据上训练 RF 和 ANN 并将它们应用于第二组，与 MOST 相比，它们提供了对所有通量的更准确估计。显热和湿气通量的估计得到显着改善，模型可解释性技术突出了我们在表层过程中预期的逻辑物理关系。