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CloudNET: retrieving turbulence parameters from cloud radar.

CloudNET: retrieving turbulence parameters from cloud radar. Anthony Illingworth, Robin Hogan , Ewan O’Connor, U of Reading, UK Dominique Bouniol , CETP, France. New method of estimating turbulence. New Method : Vertically pointing narrow beamwidth radar.

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CloudNET: retrieving turbulence parameters from cloud radar.

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  1. CloudNET: retrieving turbulence parameters from cloud radar. Anthony Illingworth, Robin Hogan , Ewan O’Connor, U of Reading, UK Dominique Bouniol, CETP, France

  2. New method of estimating turbulence New Method: Vertically pointing narrow beamwidth radar. Look at 1 second values of mean Doppler v for 30 secs. And calculate the standard deviation over 30 secs:sv Beamwidth very narrow, horizontal wind U m/s. So in 1 second U m of clouds advects past. And in 30 seconds 30U m of cloud advects past. e.g. U=10m/s sample scales 10 to 300m. i.e sample turbulent spectrum between; k1 = 2/30U and k2 = 2  /U NEED TO KNOW THE HORIZONTAL WIND Previous methods used : • Doppler spectral width (for ground based radar)  but also contributions from shear and terminal velocity • Spectral analysis of w (from airborne and ground observations)  only gives e at a given level & time – noisy for low w.

  3. Turbulence measurements • Changes in 1-s mean Doppler velocity dominated by changes in vertical wind, not terminal fall-speed • We calculate new parameter: 30-s standard deviation of 1-s mean Doppler velocity, sv • Can use this to estimate turbulent kinetic energy dissipation rate • Important for vertical mixing, warm rain initiation in cumulus etc. Spectral width sv contaminated by variations in particle fall speed

  4. Measurements of “sigma-v-bar” • 26 Jan 2004 Stable layer: sv~3 mm/s Frontal shear layer: sv~3 cm/s Unstable evaporating layer sv~30 cm/s

  5. Part of TKE spectrum can be interpreted in terms of the variance of the mean Doppler velocity: k1 is min horizontal wavenumber sampled in 30 s (use model winds) k2 is max horizontal wavenumber due to beamwidth of radar In the inertial sub-range (Kolmogorov) Hence by integration TKE dissipation rate  k2 k1

  6. Calculation of  1. Use model winds to find the value of k1. - this may fail in the tropics – unpredictable winds. ideally have a co-located wind profiler. 2. Remove any linear trends in the one second value of v: this could be due to gravity waves. 3. Check that changes in v not due to gradients in Z –leading to changes in terminal velocity, by computing Z/Z(av). Reject data if this is too high.

  7. Dissipation rate in different clouds Cirrus Rain Stratocu • Z • 

  8. 1 –year of CloudNet data • PDF of dissipation rate for different types of cloud • Note that aircraft measurements have lower limit of detectability of ~10–6 due to aircraft vibrations 0.02 – to trigger Coalescence in Cu? Khain and Pinsky, 1997 Previous range for cirrus found from aircraft

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