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

2. New method of estimating turbulence 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  at a given level & time – noisy for low w. 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:  v 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; k 1 = 2  /30U and k 2 = 2  /U NEED TO KNOW THE HORIZONTAL WIND

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,  v –Can use this to estimate turbulent kinetic energy dissipation rate –Important for vertical mixing, warm rain initiation in cumulus etc. Spectral width  v contaminated by variations in particle fall speed

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

5. Part of TKE spectrum can be interpreted in terms of the variance of the mean Doppler velocity: –k 1 is min horizontal wavenumber sampled in 30 s (use model winds) –k 2 is max horizontal wavenumber due to beamwidth of radar In the inertial sub-range (Kolmogorov) Hence by integration TKE dissipation rate  k2k2 k1k1

6. Calculation of  1. Use model winds to find the value of k 1. - 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 Z  Cirrus Stratocu Rain

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 Previous range for cirrus found from aircraft  0.02 – to trigger Coalescence in Cu? Khain and Pinsky, 1997