Evaluation of idealized large-eddy simulations performed with the Weather Research and Forecasting model using turbulence measurements from a 250 m meteorological mast

Peña, Alfredo; Kosović, Branko; Mirocha, Jeffrey D.

doi:10.5194/wes-6-645-2021

Cited by 16 publications

(13 citation statements)

References 32 publications

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“…Earlier virtual lidar studies have generally considered complex lidar behavior and have been built on a range of different LES models. The coordinated use of multiple lidar devices to simultaneously probe spanning vectors of the wind in a volume was studied by Stawiarski et al (2013) using the parallelized large-eddy simulation model (PALM) (Raasch and Schröter, 2001;Maronga et al, 2015). The dependence of profiling lidar on horizontal homogeneity complicates its use in complex terrain; Klaas et al (2015) investigated observation deviations due to terrain and choice of instrument location.…”

Section: Introductionmentioning

confidence: 99%

“…It can be configured for ideal simulations or coupled with mesoscale nesting to simulate case studies of real sites (Mazzaro et al, 2017;Haupt et al, 2019) and offers a range of sub-filter-scale turbulence models for use in LES (Mirocha et al, 2010;Kirkil et al, 2012). In validations, WRF-LES has also compared well to observations of boundary layers in varying stabilities (Peña et al, 2021). Though the virtual lidar tool is targeted at WRF-LES, with minor adjustments to accommodate for different output formats, it could be easily adapted for use with other LES models.…”

Section: Introductionmentioning

confidence: 99%

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Behavior and mechanisms of Doppler wind lidar error in varying stability regimes

Robey

Lundquist

2022

Atmos. Meas. Tech.

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Abstract. Wind lidars are widespread and important tools in atmospheric observations. An intrinsic part of lidar measurement error is due to atmospheric variability in the remote-sensing scan volume. This study describes and quantifies the distribution of measurement error due to turbulence in varying atmospheric stability. While the lidar error model is general, we demonstrate the approach using large ensembles of virtual WindCube V2 lidar performing a profiling Doppler-beam-swinging scan in quasi-stationary large-eddy simulations (LESs) of convective and stable boundary layers. Error trends vary with the stability regime, time averaging of results, and observation height. A systematic analysis of the observation error explains dominant mechanisms and supports the findings of the empirical results. Treating the error under a random variable framework allows for informed predictions about the effect of different configurations or conditions on lidar performance. Convective conditions are most prone to large errors (up to 1.5 m s−1 in 1 Hz wind speed in strong convection), driven by the large vertical velocity variances in convective conditions and the high elevation angle of the scanning beams (62∘). Range-gate weighting induces a negative bias into the horizontal wind speeds near the surface shear layer (−0.2 m s−1 in the stable test case). Errors in the horizontal wind speed and direction computed from the wind components are sensitive to the background wind speed but have negligible dependence on the relative orientation of the instrument. Especially during low winds and in the presence of large errors in the horizontal velocity estimates, the reported wind speed is subject to a systematic positive bias (up to 0.4 m s−1 in 1 Hz measurements in strong convection). Vector time-averaged measurements can improve the behavior of the error distributions (reducing the 10 min wind speed error standard deviation to <0.3 m s−1 and the bias to <0.1 m s−1 in strong convection) with a predictable effectiveness related to the number of decorrelated samples in the time window. Hybrid schemes weighting the 10 min scalar- and vector-averaged lidar measurements are shown to be effective at reducing the wind speed biases compared to cup measurements in most of the simulated conditions, with time averages longer than 10 min recommended for best use in some unstable conditions. The approach in decomposing the error mechanisms with the help of the LES flow field could be extended to more complex measurement scenarios and scans.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Behavior and mechanisms of Doppler wind lidar error in varying stability regimes

Robey

Lundquist

2022

Atmos. Meas. Tech.

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“…β is nearly 0 • within a large portion of the PBL and increases (in magnitude) with height at the vertical level where both velocity gradients and eddy fluxes are near zero. Within the range 40-200 m, β becomes slightly positive because of the combination of the impact of the subgrid-scale model on the eddy fluxes and the excessive vertical wind shear within the surface layer that the subgrid-scale model produces [29,30]. Over land (Figure 8b), α increases with height, is always positive, and is slightly higher than the observed values at the Hamburg tower.…”

Section: Wrf-lesmentioning

confidence: 87%

Departure from Flux-Gradient Relation in the Planetary Boundary Layer

2021

Self Cite

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It is well known that when eddies are small, the eddy fluxes can be directly related to the mean vertical gradients, the so-called flux-gradient relation, but such a relation becomes weaker the larger the coherent structures. Here, we show that this relation does not hold at heights relevant for wind energy applications. The flux–gradient relation assumes that the angle (β) between the vector of vertical flux of horizontal momentum and the vector of the mean vertical gradient of horizontal velocity is zero, i.e., these vectors are aligned. Our observations do not support this assumption, either onshore or offshore. Here, we present analyses of a misalignment between these vectors from a Doppler wind lidar observations and large-eddy simulations. We also use a real-time mesoscale model output for inter-comparison with the lidar-observed vertical profiles of wind speed, wind direction, momentum fluxes, and the angle between the horizontal velocity vector and the momentum flux vector up to 500 m, both offshore and onshore. The observations show this within the height range 100–500 m, β=−18∘ offshore and β=−12∘ onshore, on average. However, the large-eddy simulations show β≈0∘ both offshore and onshore. We show that observed and mesoscale-simulated vertical profiles of mean wind speed and momentum fluxes agree well; however, the mesoscale results significantly deviate from the wind-turning observations.

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“…The wind turbine is located 400 m (

5 D

) downstream the inlet, as shown in Figure 1. According to the observation of Peña et al, 54,55 we set ABL height

δ

= 350, 500, and 750 m for the stable, neutral, and convective ABLs, respectively. A constant streamwise pressure gradient is used to drive the flow within the boundary layer in the precursor domain.…”

Section: Les Methodologymentioning

confidence: 99%

A physical wind‐turbine wake growth model under different stratified atmospheric conditions

Liu

2022

Wind Energy

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The wind‐turbine wake growth is crucial for wake assessment. At present, it can only be determined empirically, which is the primary source of prediction errors in the analytical wake model, and a physically‐based method is urgently needed. Recently, the plume model is proposed for wake width prediction in the neutral boundary layer based on Taylor's diffusion theory. However, this model is not applicable for high‐roughness neutral and strongly convective conditions, which is mainly related to the fact that the specified far wake point in the plume model is too close to the virtual wake origin. In this condition, the wake width prediction has evident convex function characteristics, which is inconsistent with the actual linear expansion of wake width. To this end, we propose a coupled model of the plume model and the traditional wake model (CPT model) to calculate the wake growth rate iteratively, thereby obtaining the wake width and velocity deficits in the far‐wake region. The average wake width prediction error decreases from 11.75% to 3.1% in these conditions. Since the wind‐turbine‐induced‐turbulence contribution is dominant in the wake recovery in the very stable boundary layer, both models have low but engineering acceptable prediction accuracy. Except for the above conditions, both the plume and CPT models can predict the wake width well, and their average wake width prediction errors are 2.5% and 1.9%, respectively. This implies that the proposed CPT model has higher prediction accuracy and a broader application range.

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Evaluation of idealized large-eddy simulations performed with the Weather Research and Forecasting model using turbulence measurements from a 250 m meteorological mast

Cited by 16 publications

References 32 publications

Behavior and mechanisms of Doppler wind lidar error in varying stability regimes

Behavior and mechanisms of Doppler wind lidar error in varying stability regimes

Departure from Flux-Gradient Relation in the Planetary Boundary Layer

A physical wind‐turbine wake growth model under different stratified atmospheric conditions

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