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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),$$z/L$$$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.

In this study we examined a data set of nearly two‐year collection and investigated the effects of low‐level jets (LLJ) on near‐surface turbulence, especially wind direction changes, in the nocturnal boundary layer. Typically, nocturnal boundary layer is thermally stratified and stable. When wind profiles exhibit low gradient (in the absence of LLJ), it is characterized by very weak turbulence and very large, abrupt, but intermittent wind direction changes (∆WD) in the layers near the surface. In contrast, presence of LLJs can cause dramatic changes through inducing wind velocity shears, enhancing vertical mixing, and weakening the thermal stratification underneath. Ultimately, bulk Richardson number (R_b) is reduced and weakly stable conditions prevail, leading to active turbulence, close coupling across the layers between the LLJ height and ground surface, relatively large vertical momentum and sensible heat fluxes, and suppressed ∆WD values.R_bcan be a useful parameter in assessing turbulence strength and ∆WD as well. The dependence of ∆WD onR_bappears to be well defined under weakly stable conditions (0.0 < R_b ≤ 0.25) and ∆WD is generally confined to small values. However, the relationship between ΔWD andR_bbreaks whenR_bincreases, especiallyR_b > 1.0 (very stable conditions), under which ΔWD varies across a very wide range and the potential for large ΔWD increases greatly. Our findings have provided important implications to the plume dispersion in the nocturnal boundary layers.