By coupling large-eddy simulation (LES) codes to weather data from large-scale models, previous studies showed the viability of “real-weather” LES. However, when simulating extended periods (up to one year) of weather, a number of them diagnosed an underestimation of the simulated temporal spectrum (of wind and solar irradiance) at timescales of a few hours (i.e., the atmospheric mesoscale). This study presents simulations aimed at reproducing the observed wind spectrum from timescales of one year to one minute, including the mesoscale. Reanalysis data (European Centre for Medium-Range Weather Forecasts Reanalysis Version 5) are used as boundary conditions to a mesoscale simulation with either a local or a non-local formulation of vertical diffusion, which then drives an LES (resolution of 50 m). Several domain sizes are used to simulate the weather during 2022 over a meteorological tower in The Netherlands. It is shown that, when increasing the size of the mesoscale simulation from 64 km to 1024 km, the LES wind spectrum at the mesoscale approaches the observed spectrum. The spectrum is also sensitive to the mesoscale diffusion formulation, which either resolves or suppresses explicit convection, resulting in a different LES wind spectrum. In addition, it is shown that the higher order statistics (structure functions) improve by using a large enough mesoscale simulation. The results indicate that LES can be used as a tool to simulate the temporal dynamics of the wind at all timescales between one minute and one year, if the atmospheric mesoscales are taken into account appropriately.

Forecasting solar radiation is critical for balancing the electricity grid due to increasing production from solar energy. To this end, we need precise simulation of clouds, which is traditionally done by numerical weather prediction. However, these large-scale (LS) models struggle especially with forecasting stratocumulus clouds because their coarse vertical resolution cannot capture the sharp inversion present at stratocumulus cloud top. To address this issue, we employ large eddy simulation (LES), which operates at high resolution and has demonstrated superior accuracy in simulating stratocumulus clouds. However, LES relies on input data from a LS model, which is imperfect. To reduce the uncertainty caused by the LS data, we integrate a single ensemble Kalman filter step at the start of simulation in the LES model, utilizing local observations. Our results show that this approach is computationally feasible, robust, and reduces prediction error at assimilation by 50%. The improvement diminishes after approximately 1 hour of simulation due to the influence of large-scale forcing. Future work will focus on enhancing the LS inflow through nested simulations with realistic lateral boundary conditions to sustain the improvements in forecasting accuracy.

Models used in wind resource assessment (WRA) range from engineering wake models and computational fluid dynamics models to mesoscale weather models with wind farm parameterizations and, more recently, large-eddy simulation (LES). The latter two produce time series of wind farm power of a certain period. This simulation period is, in the case of LES, mostly limited to ≤ 1 year due to the computational costs. However, estimates of long-term (O(10 years)) power production are of high value to many parties involved in WRA. To address the need to calculate long-term annual energy production from ≤ 1-year model runs, therefore, this paper presents methods to estimate the long-term (O(10 years)) power production of a wind farm using a ≤ 1-year simulation. To validate the methods, a 10-year LES of a hypothetical large offshore wind farm is performed. The methods work by estimating the conditional probability densities between wind farm power from the LES and wind speed from reanalysis data (ERA5) from a short (≤ 1 year) LES run. The conditional probability densities are then integrated over 10 years of ERA5 wind speed, yielding an estimate of the long-term mean power production. This "long-term correction"method is validated on varying simulation periods, selected with four different day-selection techniques. When applied to a simulation period of 365 consecutive days, the methods can estimate the 10-year mean power production with a mean absolute error of around 0.35 % of the long-term mean. When choosing the simulation period with day-selection techniques that represent the long-term climate, only roughly 200 simulation days are needed to achieve the same accuracy. Finally, a method to also include wind observations in the long-term correction is presented and tested. This requires an additional "free stream"LES run without active turbines and gives estimates of long-term power and wind that are corrected for a potential LES bias. Although validation of this final approach is difficult in the employed modeling strategy, it gives valuable insights and fits within the common WRA practice of combining models and observations. The presented techniques are based on physical arguments, computationally cheap, and simple to implement. Furthermore, they are not limited to LES but can be applied to other time-series-based models. As such, they could be a useful extension for the diverse set of modeling, observational, and statistical techniques used in WRA.

This study investigates how wind shear and momentum fluxes in the surface- and boundary layer vary across wind and cloud regimes. We use a 9-year-long data set from the Cabauw observatory complemented by (8.2 × 8.2 (Formula presented.)) daily Large Eddy Simulation (LES) hindcasts. An automated algorithm classifies observed and simulated days into different cloud regimes: (a) clear-sky days, (b) days with shallow convective clouds rooted in the surface layer, with two ranges of cloud cover, and (c) non-convective cloud days. Categorized days in observations and LES do not always match, particularly the number of non-convective cloud days are underestimated in the LES, which likes to develop convection. However, the climatology and diurnal cycle of winds for each regime are very similar in LES and observations, strengthening our confidence in LES’ skill to reproduce certain clouds for certain atmospheric states. Along-wind momentum flux profiles are similar across all regimes, but large cloud cover (convective and non-convective) days have larger total momentum flux distributed over a deeper layer, with up to 30% of the surface flux still present near cloud base. The clear-sky and especially shallow cumulus regime with low cloud cover have notably larger crosswind momentum fluxes in the boundary layer. Surface-layer wind shear at daytime is smallest in the shallow cumulus regimes, having deeper boundary layers and a steady increase in surface layer wind speed during daytime. Compared to clear-sky days at a similar stability, convective cloud regimes have smaller surface-layer wind shear and larger surface friction than estimated by Monin-Obukhov Similarity Theory.

This study investigated the surface temperature, air temperature and mean radiant temperature inside an idealized 2D street geometry during daytime. The goal was to unravel the relative impact of radiative transfer, heat conduction and ventilation to the urban heat budget. A building-resolving simulation model has been used, which represents these processes at a 1 m spatial resolution. Different combinations of the canyon height to width ratio (H/W) and physical mechanisms were investigated. Shortwave radiation is the main source of energy, and for small H/W can be higher at the canyon ground level compared to flat terrain due to multiple reflections. The longwave trapping effect has the second largest contribution and becomes relatively more important with increasing H/W ratio. The influence of the interior building temperature is small. Surface temperature and mean radiant temperature are closely related, since both are largely controlled by radiative properties. No straightforward relation was found between surface temperature and air temperature, since air temperature is dependent on the competing mechanisms of forced and natural convection. A small increase in air temperature inside the canyon was observed compared to the ambient temperature above roof level. The inclusion of all key physical processes in high detail resulted in large computational requirements. If multiple reflections by the building facades are small, the more traditional, yet much simpler view factor approach will strongly reduce the computational costs as compared to the Monte Carlo technique. The influence of using the view factors on the results must be investigated.

The authors regret to inform the readers that a programming error was found in the numerical code described and used in the original submission [1]. Longwave trapping was not correctly scaled with the local cell size and is underpredicted for deep canyons. Calculations have been repeated and this corrigendum provides an overview of the new results for a deep canyon. All new results will be published in a forthcoming Phd thesis [2] that will made publicly available through the TU Delft website (https://repository.tudelft.nl/). The authors would like to apologise for any inconvenience caused.

Accurate short-term power forecasts are crucial for the reliable and efficient integration of wind energy in power systems and electricity markets. Typically, forecasts for hours to days ahead are based on the output of numerical weather prediction models, and with the advance of computing power, the spatial and temporal resolutions of these models have increased substantially. However, high-resolution forecasts often exhibit spatial and/or temporal displacement errors, and when regarding typical average performance metrics, they often perform worse than smoother forecasts from lower-resolution models. Recent computational advances have enabled the use of large-eddy simulations (LESs) in the context of operational weather forecasting, yielding turbulence-resolving weather forecasts with a spatial resolution of 100 m or finer and a temporal resolution of 30 seconds or less. This paper is a proof-of-concept study on the prospect of leveraging these ultra high-resolution weather models for operational forecasting at Horns Rev I in Denmark. It is shown that temporal smoothing of the forecasts clearly improves their skill, even for the benchmark resolution forecast, although potentially valuable high-frequency information is lost. Therefore, a statistical post-processing approach is explored on the basis of smoothing and feature engineering from the high-frequency signal. The results indicate that for wind farm forecasting, using information content from both the standard and LES resolution models improves the forecast accuracy, especially with a feature selection stage, compared with using the information content solely from either source.

Simultaneous particle-image velocimetry and laser-induced fluorescence combined with large-eddy simulations are used to investigate the flow and pollutant dispersion behaviour in a rural-to-urban roughness transition. The urban roughness is characterized by an array of cubical obstacles in an aligned arrangement. A plane fence is added one obstacle height h upstream of the urban roughness elements, with three different fence heights considered. A smooth-wall turbulent boundary layer with a depth of 10h is used as the approaching flow, and a passive tracer is released from a uniform line source 1h upstream of the fence. A shear layer is formed at the top of the fence, which increases in strength for the higher fence cases, resulting in a deeper internal boundary layer (IBL). It is found that the mean flow for the rural-to-urban transition can be described by means of a mixing-length model provided that the transitional effects are accounted for. The mixing-length formulation for sparse urban canopies, as found in the literature, is extended to take into account the blockage effect in dense canopies. Additionally, the average mean concentration field is found to scale with the IBL depth and the bulk velocity in the IBL.

Large-eddy simulation (LES) models are widely used to study atmospheric turbulence. The effects of small-scale motions that cannot be resolved need to be modeled by a subfilter-scale (SFS) model. The SFS contribution to the turbulent fluxes is typically significant in the surface layer. This study presents analytical solutions of the classical Smagorinsky SFS turbulent kinetic energy (TKE) model including a buoyancy flux contribution. Both a constant length scale and a stability-dependent one as proposed by Deardorff are considered. Analytical expressions for the mixing functions are derived and Monin-Obukhov similarity relations that are implicitly imposed by the SFS TKE model are diagnosed. For neutral and weakly stable conditions, observations indicate that the turbulent Prandtl number (PrT) is close to unity. However, based on observations in the convective boundary layer, a lower value for PrT is often applied in LES models. As a lower Prandtl number promotes a stronger mixing of heat, this may cause excessive mixing, which is quantified from a direct comparison of the mixing function as imposed by the SFS TKE model with empirical fits from field observations. For a strong stability, the diagnosed mixing functions for both momentum and heat are larger than observed. The problem of excessive mixing will be enhanced for anisotropic grids. The findings are also relevant for high-resolution numerical weather prediction models that use a Smagorinsky-type TKE closure.

Both large-eddy simulations (LES) and water-tunnel experiments, using simultaneous stereoscopic particle image velocimetry and laser-induced fluorescence, have been used to investigate pollutant dispersion mechanisms in regions where the surface changes from rural to urban roughness. The urban roughness was characterized by an array of rectangular obstacles in an in-line arrangement. The streamwise length scale of the roughness was kept constant, while the spanwise length scale was varied by varying the obstacle aspect ratio l / h between 1 and 8, where l is the spanwise dimension of the obstacles and h is the height of the obstacles. Additionally, the case of two-dimensional roughness (riblets) was considered in LES. A smooth-wall turbulent boundary layer of depth 10h was used as the approaching flow, and a line source of passive tracer was placed 2h upstream of the urban canopy. The experimental and numerical results show good agreement, while minor discrepancies are readily explained. It is found that for (Formula presented.) the drag induced by the urban canopy is largest of all considered cases, and is caused by a large-scale secondary flow. In addition, due to the roughness transition the vertical advective pollutant flux is the main ventilation mechanism in the first three streets. Furthermore, by means of linear stochastic estimation the mean flow structure is identified that is responsible for street-canyon ventilation for the sixth street and onwards. Moreover, it is shown that the vertical length scale of this structure increases with increasing aspect ratio of the obstacles in the canopy, while the streamwise length scale does not show a similar trend.