We present two new models for wind turbine interaction effects and a recipe for combining them. The first model is an extension of the Park model, which explicitly incorporates turbulence, both the ambient atmospheric turbulence and the turbulence generated in the wake itself. This Turbulence Optimized Park model is better equipped to describe wake recovery over long distances such as between wind farms, where the wake expansion slows down as the turbine-generated turbulence decays. The second model is a first version of a full engineering wind farm blockage model. In the same vein as the wake model it adds blockage contributions from the individual wind turbines to form an aggregated wind farm scale blockage effect that can be incorporated directly into the park power curve and annual energy calculations. The wake model and the blockage model describe downstream and upstream turbine interaction effects, respectively. They are coupled as the outputs of one model are the inputs to the other model and vice versa. We describe how this coupling is achieved through an iterative process. We give early stage examples of the validation of the two models and discuss how they might be further validated and improved in the future.

As a complement to measurements, numerical modeling facilitates improved understanding of the complex turbulent processes in the stratocumulus-topped boundary layer (STBL). Due to limited computational resources simulations are often run at too coarse resolutions to resolve details of cloud-top turbulence and potentially in computational domains too small to account for the largest scales of boundary layer circulations. The effects of such deficiencies are not fully understood. Here the influence of resolution/ anisotropy of the computational grid and domain size in under-resolved implicit large-eddy simulation of the STBL is investigated. The performed simulations are based on data from the first research flight of the DYCOMS-II campaign. Regarding cloud cover and domain-averaged liquid water path, simulations with horizontal/vertical grid spacing of 35/5 m, 70/10 m, and 105/15 m are found to agree better with measurements than more computationally expensive simulations with isotropic grid boxes, e.g., with 10/10 m or 15/15 m grid spacing. While decreasing the vertical grid spacing allows more representative simulation of the thin, turbulent, stably stratified interfacial layer between the STBL and the free troposphere, coarsening the horizontal resolution dampens vertical velocity fluctuations in this region and mimics the observed anisotropy of stably stratified small-scale turbulence near the cloud top. The size of the computational domain is found to have almost no impact on mean cloud properties. However, increasing it from 3:533:5 km 2 to 14314 km 2 does lead to the occurrence of larger coherent updraft structures. Increasing it further to 21321 km 2 shows little or no increase in the updraft size.

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Abstract. The aim of the presented study was to investigate the impact on the radiation budget of a biomass-burning plume, transported from Alaska to the High Arctic region of Ny-Ålesund, Svalbard, in early July 2015. Since the mean aerosol optical depth increased by the factor of 10 above the average summer background values, this large aerosol load event is considered particularly exceptional in the last 25 years. In situ data with hygroscopic growth equations, as well as remote sensing measurements as inputs to radiative transfer models, were used, in order to estimate biases associated with (i) hygroscopicity, (ii) variability of single-scattering albedo profiles, and (iii) plane-parallel closure of the modelled atmosphere. A chemical weather model with satellite-derived biomass-burning emissions was applied to interpret the transport and transformation pathways. The provided MODTRAN radiative transfer model (RTM) simulations for the smoke event (14:00 9 July–11:30 11 July) resulted in a mean aerosol direct radiative forcing at the levels of −78.9 and −47.0 W m−2 at the surface and at the top of the atmosphere, respectively, for the mean value of aerosol optical depth equal to 0.64 at 550 nm. This corresponded to the average clear-sky direct radiative forcing of −43.3 W m−2, estimated by radiometer and model simulations at the surface. Ultimately, uncertainty associated with the plane-parallel atmosphere approximation altered results by about 2 W m−2. Furthermore, model-derived aerosol direct radiative forcing efficiency reached on average −126 W m-2/τ550 and −71 W m-2/τ550 at the surface and at the top of the atmosphere, respectively. The heating rate, estimated at up to 1.8 K day−1 inside the biomass-burning plume, implied vertical mixing with turbulent kinetic energy of 0.3 m2 s−2.

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Anisotropy of turbulence near the top of the stratocumulus‐topped boundary layer (STBL) is studied using large‐eddy simulation (LES) and measurements from the POST and DYCOMS‐II field campaigns. Focusing on turbulence ∼100 m below the cloud top, we see remarkable similarity between daytime and nocturnal flight data covering different inversion strengths and free‐tropospheric conditions. With λ denoting wavelength and znormalt cloud‐top height, we find that turbulence at λ/znormalt≃0.01 is weakly dominated by horizontal fluctuations, while turbulence at λ/znormalt>1 becomes strongly dominated by horizontal fluctuations. Between are scales at which vertical fluctuations dominate. Typical‐resolution LES of the STBL (based on POST flight 13 and DYCOMS‐II flight 1) captures observed characteristics of below‐cloud‐top turbulence reasonably well. However, using a fixed vertical grid spacing of 5 m, decreasing the horizontal grid spacing and increasing the subgrid‐scale mixing length leads to increased dominance of vertical fluctuations, increased entrainment velocity, and decreased liquid water path. Our analysis supports the notion that entrainment parameterizations (e.g., in climate models) could potentially be improved by accounting more accurately for anisotropic deformation of turbulence in the cloud‐top region. While LES has the potential to facilitate improved understanding of anisotropic cloud‐top turbulence, sensitivity to grid spacing, grid‐box aspect ratio, and subgrid‐scale model needs to be addressed.

A new analytical wake model for wind farm design is presented. The new model, TurbOPark, is based on the Park model but features non-linear wake expansion and a Gaussian deficit profile. The modelled wake expansion depends on the intensity of the local turbulence, which is a combination of ambient atmospheric turbulence and wake-generated turbulence. Moving downstream from the rotor, the intensity of the wake-generated turbulence decreases, and the wake expansion slows down. This leads to more persistent wakes in wind farms and clusters of wind farms with large distances between turbines. In the present study, we focus on the London Array wind farm and show examples of how results from Park and TurbOPark compare to SCADA data. We look at the array efficiency of the wind farm, the production of individual turbines, and the impact of neighbouring wind farms. We find that predictions from TurbOPark generally are closer to observations than predictions from the Park model.

We present a systematic framework for the validation and uncertainty quantification of wind farm wake models. The methodology is based on a new definition of the freestream wind speed. We apply the framework on data from 19 offshore wind farms. Our results show that the new wake model TurbOPark is overall unbiased and that the wake model error at each specific site does not depend on the mean turbine spacing. The Park model underestimates the wake loss unless a slower wake expansion than typically used is assumed. In either case, the Park model tends to underestimate the wake loss more for increasing distances between the turbines. We estimate the wake model uncertainty as less than 10% of the wake loss for the considered models.