Evaluation of a Procedure to Correct Spatial Averaging in Turbulence Statistics from a Doppler Lidar by Comparing Time Series with an Ultrasonic Anemometer

Brugger, Peter; Träumner, K.; Jung, Christina

doi:10.1175/jtech-d-15-0136.1

Cited by 13 publications

(9 citation statements)

References 23 publications

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“…where σ 2 w is the contribution from atmospheric turbulence at the scales the lidar can measure (Brugger et al, 2016), σ 2 e is due to the instrumental noise, and σ 2 d is related to the variation in the aerosol terminal fall velocity within the sampled volume, which can safely be ignored since the particle fall speed is typically very low (< 1 cm s −1 ). The contribution of instrumental noise σ 2 e can be written as a function of the signal-to-noise ratio (SNR) (Pearson et al, 2009):…”

Section: Turbulence Dissipation Rate From Wind Profiling Lidarmentioning

confidence: 99%

Spatial and temporal variability of turbulence dissipation rate in complex terrain

et al. 2019

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Abstract. To improve parameterizations of the turbulence dissipation rate (ϵ) in numerical weather prediction models, the temporal and spatial variability of ϵ must be assessed. In this study, we explore influences on the variability of ϵ at various scales in the Columbia River Gorge during the WFIP2 field experiment between 2015 and 2017. We calculate ϵ from five sonic anemometers all deployed in a ∼4 km2 area as well as from two scanning Doppler lidars and four profiling Doppler lidars, whose locations span a ∼300 km wide region. We retrieve ϵ from the sonic anemometers using the second-order structure function method, from the scanning lidars with the azimuth structure function approach, and from the profiling lidars with a novel technique using the variance of the line-of-sight velocity. The turbulence dissipation rate shows large spatial variability, even at the microscale, especially during nighttime stable conditions. Orographic features have a strong impact on the variability of ϵ, with the correlation between ϵ at different stations being highly influenced by terrain. ϵ shows larger values in sites located downwind of complex orographic structures or in wind farm wakes. A clear diurnal cycle in ϵ is found, with daytime convective conditions determining values over an order of magnitude higher than nighttime stable conditions. ϵ also shows a distinct seasonal cycle, with differences greater than an order of magnitude between average ϵ values in summer and winter.

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Section: Turbulence Dissipation Rate From Wind Profiling Lidarmentioning

confidence: 99%

Spatial and temporal variability of turbulence dissipation rate in complex terrain

et al. 2019

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“…σ 2 w is the desired net contribution from atmospheric turbulence at the scales that can be measured by the lidar (Brugger et al, 2016), from which the estimation of can be made. The additional contributions to the variance are due to the instrumental noise (σ 2 e ) and the variation in the aerosol terminal fall speeds within the measurement volume from different sample intervals (σ 2 d ), which however can safely be neglected since the particle fall speed is typically < 1 cm s −1 .…”

Section: Dissipation From Doppler Lidarmentioning

confidence: 99%

Estimation of turbulence dissipation rate and its variability from sonic anemometer and wind Doppler lidar during the XPIA field campaign

2018

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Abstract. Despite turbulence being a fundamental transport process in the boundary layer, the capability of current numerical models to represent it is undermined by the limits of the adopted assumptions, notably that of local equilibrium. Here we leverage the potential of extensive observations in determining the variability in turbulence dissipation rate ( ). These observations can provide insights towards the understanding of the scales at which the major assumption of local equilibrium between generation and dissipation of turbulence is invalid. Typically, observations of require time-and labor-intensive measurements from sonic and/or hot-wire anemometers. We explore the capability of wind Doppler lidars to provide measurements of . We refine and extend an existing method to accommodate different atmospheric stability conditions. To validate our approach, we estimate from four wind Doppler lidars during the 3-month XPIA campaign at the Boulder Atmospheric Observatory (Colorado), and we assess the uncertainty of the proposed method by data intercomparison with sonic anemometer measurements of . Our analysis of this extensive dataset provides understanding of the climatology of turbulence dissipation over the course of the campaign. Further, the variability in with atmospheric stability, height, and wind speed is also assessed. Finally, we present how increases as nocturnal turbulence is generated during low-level jet events.

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“…At the lowest given range gate and for the ultrasonic measurement, maximum energy is shifted towards higher frequencies (0.01 to 0.1 Hz) and smaller time periods (10 s to 2 min), respectively. Besides, as discussed by Frehlich et al (1998) or Brugger et al (2015 for example, it is obvious that in the inertial subrange, the energy of the lidar spectra 25 decreases faster than the theoretical −2/3-slope, i.e. for frequencies higher than about 0. to register fluctuations on these scales, the sampling frequency is restricted due to the spatial averaging of the lidar pulses.…”

Section: Characteristics Of Vertical Velocity Datamentioning

confidence: 75%

Observed spatial variability of boundary-layer turbulence over flat, heterogeneous terrain

Maurer¹,

Kalthoff²,

Wieser³

et al. 2015

Preprint

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In spring 2013, extensive measurements with multiple Doppler lidar systems were performed. The instruments were arranged in a triangle with edge lengths of about 3 km in a moderately flat, agriculturally used terrain. For six mostly cloud-free convective days, vertical velocity variance profiles were compared for the three locations. On the aver-5 age over all considered cases, differences between variances at different sites were about three times higher than between those derived from measurements by different lidars at the same site. For all investigated averaging periods between 10 min and 4 h, the differences were not significant on the average when considering the statistical error. However, statistically significant spatial differences were found in several individual 10 cases. These could not be explained by the existing surface heterogeneity.In some cases, nearby energy balance stations provided surface fluxes that were not suitable for scaling the variance profiles. Weighted-averaged values proved to be more applicable, but even then, the scaled profiles showed a large scatter for each location. Therefore, it must be assumed that the intensity of turbulence is not always 15 well-determined by the local heat supply at the Earth's surface. Instead, a certain dependency of turbulence characteristics on mean wind speed and direction was found: thermals were detected that travelled from one site to the other with the mean wind when the travel time was shorter than the large-eddy turnover time. At the same time, no thermals passed for more than two hours at a third site that was located perpendic-20 ular to the mean wind direction in relation to the first two sites. Subsidence prevailing in the surroundings of thermals advected with the mean wind can thus partly explain significant spatial variance differences existing for several hours.

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Evaluation of a Procedure to Correct Spatial Averaging in Turbulence Statistics from a Doppler Lidar by Comparing Time Series with an Ultrasonic Anemometer

Cited by 13 publications

References 23 publications

Spatial and temporal variability of turbulence dissipation rate in complex terrain

Spatial and temporal variability of turbulence dissipation rate in complex terrain

Estimation of turbulence dissipation rate and its variability from sonic anemometer and wind Doppler lidar during the XPIA field campaign

Observed spatial variability of boundary-layer turbulence over flat, heterogeneous terrain

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