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Quantitative knowledge of the surface energy balance is essential for the prediction of weather and climate. However, a multitude of studies from around the world indicate that the turbulent heat fluxes are generally underestimated using eddy-covariance measurements, and hence, the energy balance is not closed. This energy-balance-closure problem, which has been heavily covered in the literature for more than 25 years, is the topic of the present review, in which we provide an overview of the potential reason for the lack of closure. We demonstrate the effects of the diurnal cycle on the energy balance closure, and address questions with regard to the partitioning of the energy balance residual between the sensible and the latent fluxes, and whether the magnitude of the flux underestimation can be predicted based on other variables typically measured at micrometeorological stations. Remaining open questions are discussed and potential avenues for future research on this topic are laid out. Integrated studies, combining multi-tower experiments and scale-crossing, spatially-resolving lidar and airborne measurements with high-resolution large-eddy simulations, are considered to be of critical importance for enhancing our understanding of the underlying transport processes in the atmospheric boundary layer.

Developing urban land surface models for modeling cities at high resolutions needs to better account for the city‐specific multi‐scale land surface heterogeneities at a reasonable computational cost. We propose using an encoder‐decoder convolutional neural network to develop a computationally efficient model for predicting the mean velocity field directly from urban geometries. The network is trained using the geometry‐resolving large eddy simulation results. Systematic testing on urban structures with increasing deviations from the training geometries shows the prediction error plateaus at 15%, compared to errors sharply increasing up to 35% in the null models. This is explained by the trained model successfully capturing the effects of pressure drag, especially for tall buildings. The prediction error of the aerodynamic drag coefficient is reduced by 32% compared with the default parameterization implemented in mesoscale modeling. This study highlights the potential of combining computational fluid dynamics modeling and machine learning to develop city‐specific parameterizations. Plain Language Summary Predicting the velocity field in the urban area with fine resolution at the meter scale is computationally expensive. Yet a detailed velocity field is necessary for improving the accuracy of urban land surface representation in weather and climate models. We propose using a convolutional neural network to predict the velocity field from the three‐dimensional (3D) building distribution. The similarity between the predicted velocity fields and LES simulations in the testing geometries illustrates the prediction capability of the trained model. We also investigate the aerodynamic drag coefficient, a key parameter for quantifying the land‐atmosphere momentum exchange. The results indicate that the trained model prediction is much closer to values derived from large‐eddy simulation models than those from the default parameterization scheme, showing the promise of using machine learning to improve urban land surface modeling. Key Points Machine learning (ML) can help develop city‐specific parameterization that fully utilizes urban form data It is a first attempt to develop an ML model for high‐Reynolds number urban canopy flow with multiple bluff‐body obstacles Limitation of the geometry to flow field approach is quantified by accessing the extrapolative capability of the trained model

Over the last 50 years the large-eddy simulation (LES) technique has developed into one of the most prominent numerical tools used to study transport processes in the atmospheric boundary layer (ABL). This review examines development of the technique as a tool for ABL research, integration with state-of-the-art scientific computing resources, and some key application areas. Analysis of the published literature indicates that LES research across a broad range of applications accelerated starting around 1990. From that point in time, robust research using LES developed in several different application areas and based on a review of the papers published in this journal, we identify seven major areas of intensive ABL–LES research: convective boundary layers, stable boundary layers, transitional boundary layers, plant canopy flows, urban meteorology and dispersion, surface heterogeneity, and the testing and development of subgrid-scale (SGS) models. We begin with a general overview of LES and then proceed to examine the SGS models developed for use in ABL–LES. After this overview of the technique itself, we review the specific model developments tailored to the identified application areas and the scientific advancements realized using the LES technique in each area. We conclude by examining the computational trends in published ABL–LES research and identify some resource underutilization. Future directions and research needs are identified from a synthesis of the reviewed literature.

The design and financing of commercial‐scale floating offshore wind projects require a better understanding of how power generation differs between newer floating turbines and well‐established fixed‐bottom turbines. In floating turbines, platform mobility causes additional rotor motion that can change the time‐averaged power generation. In this work, OpenFAST simulations examine the power generated by the National Renewable Energy Laboratory's 5‐MW reference turbine mounted on the OC3‐UMaine spar and OC4‐DeepCWind semisubmersible floating platforms, subjected to extreme irregular waves and below‐rated turbulent inflow wind from large‐eddy simulations of a neutral atmospheric boundary layer. For these below‐rated conditions, average power generation in floating turbines is most affected by two types of turbine displacements: an average rotor pitch angle that reduces power, caused by platform pitch; and rotor motion upwind‐downwind that increases power, caused by platform surge and pitch. The relative balance between these two effects determines whether a floating platform causes power gains or losses compared to a fixed‐bottom turbine; for example, the spar creates modest (3.1%–4.5%) power gains, whereas the semisubmersible creates insignificant (0.1%–0.2%) power gains for the simulated conditions. Furthermore, platform surge and pitch motions must be analyzed concurrently to fully capture power generation in floating turbines, which is not yet universal practice. Finally, a simple analytical model for predicting average power in floating turbines under below‐rated wind speeds is proposed, incorporating effects from both the time‐averaged pitch displacement and the dynamic upwind‐downwind displacements.

We present a review of existing wind–wave coupling models and parameterizations used for large-eddy simulation of the marine atmospheric boundary layer. The models are classified into two main categories: (i) the wave-phase-averaged, sea surface–roughness models and (ii) the wave-phase-resolved models. Both categories are discussed from their implementation, validity, and computational efficiency viewpoints, with emphasis given on their applicability in offshore wind energy problems. In addition to the various models discussed, a review of laboratory-scale and field-measurement databases is presented thereafter. The majority of the presented data have been gathered over many decades of studying air–sea interaction phenomena, with the most recent ones compiled to reflect an offshore wind energy perspective. Both provide valuable data for model validation. We also discuss the modeling knowledge gaps and computational challenges ahead.

We present a proof-of-concept for the adaptive mesh refinement method applied to atmospheric boundary-layer simulations. Such a method may form an attractive alternative to static grids for studies on atmospheric flows that have a high degree of scale separation in space and/or time. Examples include the diurnal cycle and a convective boundary layer capped by a strong inversion. For such cases, large-eddy simulations using regular grids often have to rely on a subgrid-scale closure for the most challenging regions in the spatial and/or temporal domain. Here we analyze a flow configuration that describes the growth and subsequent decay of a convective boundary layer using direct numerical simulation (DNS). We validate the obtained results and benchmark the performance of the adaptive solver against two runs using fixed regular grids. It appears that the adaptive-mesh algorithm is able to coarsen and refine the grid dynamically whilst maintaining an accurate solution. In particular, during the initial growth of the convective boundary layer a high resolution is required compared to the subsequent stage of decaying turbulence. More specifically, the number of grid cells varies by two orders of magnitude over the course of the simulation. For this specific DNS case, the adaptive solver was not yet more efficient than the more traditional solver that is dedicated to these types of flows. However, the overall analysis shows that the method has a clear potential for numerical investigations of the most challenging atmospheric cases.

Cloud processes relevant to radiative and precipitation properties depend on the shape of the cloud droplet size distribution. Recent holographic observations revealed that cloud droplet populations do not have the same size distribution shapes throughout but form regions of characteristic distributions with similar microphysical properties. We investigate the existence and properties of these characteristic distributions within Large‐Eddy Simulations of stratocumulus clouds using Lagrangian and bin microphysics schemes. Distribution types are identified, revealing localized characteristic distributions that vary on the scale of the largest convective cell for simulations with bin microphysics. The results from the Lagrangian microphysics scheme hint at similar behavior. Compared to observations, the simulated clouds are much more uniform. Analysis of the LES results suggests a connection to the local entrainment rate, so the poorly resolved entrainment interface in LES may be a cause of the uniformity. The uniformity of the large‐scale forcing could also be a factor.

Large-eddy simulations (LES) are conducted to study the transport of momentum and passive scalar within and over a real urban canopy in the City of Boston, USA. This urban canopy is characterized by complex building layouts, densities and orientations with high-rise buildings. Special attention is given to the magnitude, variability and structure of dispersive momentum and scalar fluxes and their relative importance to turbulent momentum and scalar fluxes. We first evaluate the LES model by comparing the simulated flow statistics over an urban-like canopy to data reported in previous studies. In simulations over the considered real urban canopy, we observe that the dispersive momentum and scalar fluxes can be important beyond 2–5 times the mean building height, which is a commonly used definition for the urban roughness sublayer height. Above the mean building height where the dispersive fluxes become weakly dependent on the grid spacing, the dispersive momentum flux contributes about 10–15% to the sum of turbulent and dispersive momentum fluxes and does not decrease monotonically with increasing height. The dispersive momentum and scalar fluxes are sensitive to the time and spatial averaging. We further find that the constituents of dispersive fluxes are spatially heterogeneous and enhanced by the presence of high-rise buildings. This work suggests the need to parameterize both turbulent and dispersive fluxes over real urban canopies in mesoscale and large-scale models.

Large-eddy simulations are widely used to study flows in the atmospheric boundary layer. As atmospheric boundary-layer flows of different atmospheric stratification have very different flow characteristics on different length scales, well-resolved simulations of these flows require very different meshes. The Parallelized Large-Eddy Simulation Model combined with a realizable dynamic subgrid model is used to identify the best method for evaluating the resolution requirements for boundary-layer flows simulated by large-eddy simulations. In particular, we consider three atmospheric boundary-layer set-ups with different stratifications (stable, neutral, convective) to investigate how the quality of the simulation changes with the grid resolution. By following the work of Davidson (Int J Heat Fluid Flow 30(5):1016–1025, 2009), the results are examined using criteria such as the convergence of mean profiles, the ratio of modelled and resolved turbulence kinetic energy, and the two-point correlation. We conclude that the two-point correlation is the best measure to evaluate whether the resolution demands for a specific flow are fulfilled.

Mixed-phase clouds affect precipitation and radiation differently from liquid and ice clouds, posing greater challenges to their representation in numerical simulations. Recent laboratory experiments using the Pi Cloud Chamber explored cloud glaciation conditions based on increased injection of ice-nucleating particles. In this study, we use two approaches to reproduce the results of the laboratory experiments: a bulk scalar mixing model and large-eddy simulation (LES) with bin microphysics. The first approach assumes a well-mixed domain to provide an efficient assessment of the mean cloud properties for a wide range of conditions. The second approach resolves the energy-carrying turbulence, the particle size distribution, and their spatial distribution to provide more details. These modeling approaches enable a separate and detailed examination of liquid and ice properties, which is challenging in the laboratory. Both approaches demonstrate that, with an increased ice number concentration, the flow and microphysical properties exhibit the same changes in trends. Additionally, both approaches show that the ice integral radius reaches the theoretical glaciation threshold when the cloud is subsaturated with respect to liquid water. The main difference between the results of the two approaches is that the bulk model allows for the complete glaciation of the cloud. However, LES reveals that, in a dynamic system, the cloud is not completely glaciated as liquid water droplets are continuously produced near the warm lower boundary and subsequently mixed into the chamber interior. These results highlight the importance of the ice mass fraction in distinguishing the mixed-phase clouds and ice clouds.