Assessing State-of-the-Art Capabilities for Probing the Atmospheric Boundary Layer: The XPIA Field Campaign

Lundquist, Julie K.; Wilczak, James M.; Ashton, Ryan; Bianco, L.; Brewer, W. Alan; Choukulkar, Aditya; Clifton, Andrew; Debnath, Mithu; Delgado, Rubén; Friedrich, Katja; Gunter, Scott; Hamidi, Armita; Iungo, Giacomo Valerio; Kaushik, Aleya; Kosović, Branko; Langan, Patrick A.; Lass, Adam; Lavin, Evan; Lee, Joseph C. Y.; McCaffrey, Katherine; Newsom, Rob; Noone, David; Oncley, Steven; Quelet, P. T.; Sandberg, Scott P.; Schroeder, John L.; Shaw, W. J.; Sparling, L. C.; Martin, Clara St.; St., Alexandra; Strobach, Edward; Tay, Ken; Vanderwende, Brian Joseph; Weickmann, A. M.; Wolfe, D. E.; Worsnop, Rochelle P.

doi:10.1175/bams-d-15-00151.1

Cited by 85 publications

(91 citation statements)

References 86 publications

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“…Of course, additional observation of wind-farm wake behavior could inform development of both techniques. In particular, comprehensive four-dimensional representations of flow throughout large arrays (e.g., from lidars [Banta et al, 2015;Lundquist et al, 2016] and radars [Hirth et al, 2015]) would enable validation of simulated wake evolution and structure. The myriad uses for wind-farm parameterizations present many challenges, but also many opportunities for evaluation and improvement.…”

Section: Discussionmentioning

confidence: 99%

Simulating effects of a wind‐turbine array using LES and RANS

Vanderwende

Kosović

Lundquist

et al. 2016

J Adv Model Earth Syst

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Growth in wind power production has motivated investigation of wind‐farm impacts on in situ flow fields and downstream interactions with agriculture and other wind farms. These impacts can be simulated with both large‐eddy simulations (LES) and mesoscale wind‐farm parameterizations (WFP). The Weather Research and Forecasting (WRF) model offers both approaches. We used the validated generalized actuator disk (GAD) parameterization in WRF‐LES to assess WFP performance. A 12‐turbine array was simulated using the GAD model and the WFP in WRF. We examined the performance of each scheme in both convective and stable conditions. The GAD model and WFP produced qualitatively similar wind speed deficits and turbulent kinetic energy (TKE) production across the array in both stability regimes, though the magnitudes of velocity deficits and TKE production levels were underestimated and overestimated, respectively. While wake growth slowed in the latter half of the WFP array as expected, wakes did not approach steady state by the end of the array as simulated by the GAD model. A sensitivity test involving the deactivation of explicit TKE production by the WFP resulted in turbulence levels within the array well that were below those produced by the GAD in both stable and unstable conditions. Finally, the WFP overestimated downwind power production deficits in stable conditions because of the lack of wake stabilization in the latter half of the array.

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Section: Discussionmentioning

confidence: 99%

Simulating effects of a wind‐turbine array using LES and RANS

Vanderwende

Kosović

Lundquist

et al. 2016

J Adv Model Earth Syst

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“…The uncertainty of the LOS measurement by the lidar (when compared to the sonic anemometer) allows evaluating the various measurement techniques in terms of additional uncertainty added to this baseline value. The offset in the LOS velocity of the UTD lidar was found to be due to improper calibration of the pulse-length-dependent frequency offset (Lundquist et al, 2016a). This was characterized using independent measurements and was found to be constant throughout the XPIA campaign.…”

Section: Determining Baseline Uncertaintymentioning

confidence: 96%

“…The instrumentation included six scanning Doppler lidars (four capable of coordinated scanning) and five vertically profiling lidars. Lundquist et al (2016a) give a detailed description of the XPIA field study along with an overview of the instrumentation deployment. Herein, for sake of brevity, only the details of the scanning lidar deployment used for testing the various single and multi-Doppler measurements are described.…”

Section: Experiments Setupmentioning

confidence: 99%

Evaluation of single and multiple Doppler lidar techniques to measure complex flow during the XPIA field campaign

et al. 2017

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Abstract. Accurate three-dimensional information of wind flow fields can be an important tool in not only visualizing complex flow but also understanding the underlying physical processes and improving flow modeling. However, a thorough analysis of the measurement uncertainties is required to properly interpret results. The XPIA (eXperimental Planetary boundary layer Instrumentation Assessment) field campaign conducted at the Boulder Atmospheric Observatory (BAO) in Erie, CO, from 2 March to 31 May 2015 brought together a large suite of in situ and remote sensing measurement platforms to evaluate complex flow measurement strategies.In this paper, measurement uncertainties for different single and multi-Doppler strategies using simple scan geometries (conical, vertical plane and staring) are investigated. The tradeoffs (such as time-space resolution vs. spatial coverage) among the different measurement techniques are evaluated using co-located measurements made near the BAO tower. Sensitivity of the single-/multi-Doppler measurement uncertainties to averaging period are investigated using the sonic anemometers installed on the BAO tower as the standard reference. Finally, the radiometer measurements are used to partition the measurement periods as a function of atmospheric stability to determine their effect on measurement uncertainty.It was found that with an increase in spatial coverage and measurement complexity, the uncertainty in the wind measurement also increased. For multi-Doppler techniques, the increase in uncertainty for temporally uncoordinated measurements is possibly due to requiring additional assumptions of stationarity along with horizontal homogeneity and less representative line-of-sight velocity statistics. It was also found that wind speed measurement uncertainty was lower during stable conditions compared to unstable conditions.

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“…Here, we investigate the variability of the urban boundary layer in New York City using vertical profiles from three local airports, and profiles derived from a microwave radiometer within the urban centre to investigate the seasonal variabilities in the thermal and moisture boundary-layer structure. Microwave radiometers have the advantage of being robust instruments (Xu et al 2015), with a recent, extensive study judging them to be very good at estimating temperature profiles, particularly in the lower boundary layer (Lundquist et al 2017). While microwaveradiometer observations have been used to determine the mixed-layer height (Cimini et al 2013), the vertical extent of the urban heat island (Khaikine et al 2006), and for estimating stability conditions for wind-energy applications (Friedrich et al 2012), our main objectives here are to analyze the spatial variability in the thermal structure of the boundary layer over the highly dense area of New York City, and to quantify the diurnal and seasonal variabilities of the urban thermal and moisture profiles.…”

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confidence: 99%

Thermal Structure of a Coastal–Urban Boundary Layer

Melecio-Vázquez

Ramamurthy

Arend

et al. 2018

Boundary-Layer Meteorol

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We use various temperature profilers located in and around New York City to observe the structure and evolution of the thermal boundary layer. The primary focus is to highlight the spatial variability of potential-temperature profiles due to heterogeneous surface forcing in an urban environment during different flow conditions. Overall, the observations during the summer period reveal the presence of thermal internal boundary layers due to the interaction between the marine atmospheric boundary layer and the convective urban environment. The summer daytime potential-temperature profiles within the city indicate a superadiabatic layer is present near the surface beneath a mildly stable layer. Large spatial variability in the near-surface (0-300 m) potential temperature is detected, with the thermal profile in the lower atmosphere uniquely determined by the underlying surface forcing and the distance from the coast. The summer and winter average night-time potential-temperature profiles show that the atmosphere is still convective near the surface. The seasonal averages of mixing ratio show large variability in the vertical direction.Keywords Coastal boundary layer · Microwave radiometer · Moisture boundary layer · Thermal boundary layer · Urban boundary layer

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Assessing State-of-the-Art Capabilities for Probing the Atmospheric Boundary Layer: The XPIA Field Campaign

Abstract: 36

Cited by 85 publications

References 86 publications

Simulating effects of a wind‐turbine array using LES and RANS

Simulating effects of a wind‐turbine array using LES and RANS

Evaluation of single and multiple Doppler lidar techniques to measure complex flow during the XPIA field campaign

Thermal Structure of a Coastal–Urban Boundary Layer

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