Effects of Path Averaging in a Sonic Anemometer on the Estimation of Turbulence-Kinetic-Energy Dissipation Rates

Sonic anemometer observation was performed on 29 - 30 September 2014 in Ledeng, Bandung to see diurnal variations of Turbulence Kinetic Energy (TKE) that occurred in this area. The measured sonic anemometer was the wind velocity components u, v, and w. From the observation result, it can be seen that the diurnal variation that happened was quite significant. The maximum TKE occurs during the daytime when atmospheric conditions tend to be unstable. TKE values were small at night when atmospheric conditions are more stable than during the daytime.

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Diffusional and accretional growth of water drops in a rising adiabatic parcel: effects of the turbulent collision kernel

Atmospheric Chemistry and Physics ◽

10.5194/acp-9-2335-2009 ◽

2009 ◽

Vol 9 (7) ◽

pp. 2335-2353 ◽

Cited By ~ 33

Author(s):

W. W. Grabowski ◽

L.-P. Wang

Keyword(s):

Energy Dissipation ◽

Kinetic Energy ◽

Turbulent Kinetic Energy ◽

Collision Kernel ◽

Small Scale ◽

Large Set ◽

Initiation Time ◽

Cloud Droplets ◽

Dissipation Rates ◽

Speedup Factor

Abstract. A large set of rising adiabatic parcel simulations is executed to investigate the combined diffusional and accretional growth of cloud droplets in maritime and continental conditions, and to assess the impact of enhanced droplet collisions due to small-scale cloud turbulence. The microphysical model applies the droplet number density function to represent spectral evolution of cloud and rain/drizzle drops, and various numbers of bins in the numerical implementation, ranging from 40 to 320. Simulations are performed applying two traditional gravitational collection kernels and two kernels representing collisions of cloud droplets in the turbulent environment, with turbulent kinetic energy dissipation rates of 100 and 400 cm2 s−3. The overall result is that the rain initiation time significantly depends on the number of bins used, with earlier initiation of rain when the number of bins is low. This is explained as a combination of the increase of the width of activated droplet spectrum and enhanced numerical spreading of the spectrum during diffusional and collisional growth when the number of model bins is low. Simulations applying around 300 bins seem to produce rain at times which no longer depend on the number of bins, but the activation spectra are unrealistically narrow. These results call for an improved representation of droplet activation in numerical models of the type used in this study. Despite the numerical effects that impact the rain initiation time in different simulations, the turbulent speedup factor, the ratio of the rain initiation time for the turbulent collection kernel and the corresponding time for the gravitational kernel, is approximately independent of aerosol characteristics, parcel vertical velocity, and the number of bins used in the numerical model. The turbulent speedup factor is in the range 0.75–0.85 and 0.60–0.75 for the turbulent kinetic energy dissipation rates of 100 and 400 cm2 s−3, respectively.

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Upper and middle tropospheric kinetic energy dissipation rates from measurements of — review of theories, in-situ investigations, and experimental studies using the Buckland Park atmospheric radar in Australia

Journal of Atmospheric and Solar-Terrestrial Physics ◽

10.1016/s1364-6826(97)00020-5 ◽

1997 ◽

Vol 59 (14) ◽

pp. 1779-1803 ◽

Cited By ~ 50

Author(s):

W.K. Hocking ◽

P.K.L. Mu

Keyword(s):

Energy Dissipation ◽

Kinetic Energy ◽

Experimental Studies ◽

Dissipation Rates

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Diffusional and accretional growth of water drops in a rising adiabatic parcel: effects of the turbulent collision kernel

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-8-14717-2008 ◽

2008 ◽

Vol 8 (4) ◽

pp. 14717-14763 ◽

Cited By ~ 4

Author(s):

W. W. Grabowski ◽

L.-P. Wang

Keyword(s):

Energy Dissipation ◽

Kinetic Energy ◽

Turbulent Kinetic Energy ◽

Collision Kernel ◽

Small Scale ◽

Large Set ◽

Initiation Time ◽

Cloud Droplets ◽

Dissipation Rates ◽

Speedup Factor

Abstract. A large set of rising adiabatic parcel simulations is executed to investigate the combined diffusional and accretional growth of cloud droplets in maritime and continental conditions, and to assess the impact of enhanced droplet collisions due to small-scale cloud turbulence. The microphysical model applies the droplet number density function to represent spectral evolution of cloud and rain/drizzle drops, and various numbers of bins in the numerical implementation, ranging from 40 to 320. Simulations are performed applying two traditional gravitational collection kernels and two kernels representing collisions of cloud droplets in the turbulent environment, with turbulent kinetic energy dissipation rates of 100 and 400 cm2 s−3. The overall result is that the rain initiation time significantly depends on the number of bins used, with earlier initiation of rain when the number of bins is low. This is explained as a combination of the increase of the width of activated droplet spectrum and enhanced numerical spreading of the spectrum during diffusional and collisional growth when the number of model bins is low. Simulations applying around 300 bins seem to produce rain at times which no longer depend on the number of bins, but the activation spectra are unrealistically narrow. These results call for an improved representation of droplet activation in numerical models of the type used in this study. Despite the numerical effects that impact the rain initiation time in different simulations, the turbulent speedup factor, the ratio of the rain initiation time for the turbulent collection kernel and the corresponding time for the gravitational kernel, is approximately independent of aerosol characteristics, parcel vertical velocity, and the number of bins used in the numerical model. The turbulent speedup factor is in the range 0.75–0.85 and 0.60–0.75 for the turbulent kinetic energy dissipation rates of 100 and 400 cm2 s−3, respectively.

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Evaluation of turbulence measurement techniques from a single Doppler lidar

Atmospheric Measurement Techniques ◽

10.5194/amt-10-3021-2017 ◽

2017 ◽

Vol 10 (8) ◽

pp. 3021-3039 ◽

Cited By ~ 26

Author(s):

Timothy A. Bonin ◽

Aditya Choukulkar ◽

W. Alan Brewer ◽

Scott P. Sandberg ◽

Ann M. Weickmann ◽

...

Keyword(s):

Boundary Layer ◽

Kinetic Energy ◽

Planetary Boundary Layer ◽

Turbulence Intensity ◽

Measurement Techniques ◽

Sonic Anemometer ◽

Shear Velocity ◽

Turbulence Kinetic Energy ◽

Doppler Lidar ◽

Turbulence Measurement

Abstract. Measurements of turbulence are essential to understand and quantify the transport and dispersal of heat, moisture, momentum, and trace gases within the planetary boundary layer (PBL). Through the years, various techniques to measure turbulence using Doppler lidar observations have been proposed. However, the accuracy of these measurements has rarely been validated against trusted in situ instrumentation. Herein, data from the eXperimental Planetary boundary layer Instrumentation Assessment (XPIA) are used to verify Doppler lidar turbulence profiles through comparison with sonic anemometer measurements. For 17 days at the end of the experiment, a single scanning Doppler lidar continuously cycled through different turbulence measurement strategies: velocity–azimuth display (VAD), six-beam scans, and range–height indicators (RHIs) with a vertical stare.Measurements of turbulence kinetic energy (TKE), turbulence intensity, and stress velocity from these techniques are compared with sonic anemometer measurements at six heights on a 300 m tower. The six-beam technique is found to generally measure turbulence kinetic energy and turbulence intensity the most accurately at all heights (r2 ≈  0.78), showing little bias in its observations (slope of ≈ 0. 95). Turbulence measurements from the velocity–azimuth display method tended to be biased low near the surface, as large eddies were not captured by the scan. None of the methods evaluated were able to consistently accurately measure the shear velocity (r2 =  0.15–0.17). Each of the scanning strategies assessed had its own strengths and limitations that need to be considered when selecting the method used in future experiments.

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