1. Lee Smith Anthony Illingworth
Cloud liquid water path and drizzle from attenuation of CloudSat ocean return (s0)
Lee Smith
Anthony Illingworth

2. Surface echo attenuation method
Add isotachs for wind and thermometers for SST

3. Two - way attenuation at 94 GHz:
Oxygen: dB
Water:
Ice particles: negligble
Liquid water: ~ 1dB per 120 gm-2
Water vapour: ~ 1dB per gm-2
Factor of ~100 greater attenuation for liquid water  liquid water drops dominate attenuation in cloudy conditions
Use of CALIPSO lidar allows discrimination between clear sky and cloudy areas

4. Corrections to surface echo
(1) Temperature dependent vapour attenuation, using AMSR-E vapour path, model temperatures and Liebe attenuation model
(2) Correct for poor vertical sampling of surface echo
(3) Derive combined Wind/SST correction table for variability in surface echo derived from clear sky data
Surface echo well
characterized

5. Statistics of corrected clear sky so:
Standard deviation of s0 generally < 0.5 dB gm-2 even over 200 km

6. Case study: Measure attenuation
Identify clear sky using LIDAR and set reference s0 (blue line)
20 km

7. Case study: Liquid water path:
Multiply attenuation by temperature dependent coefficient LWP
Compare with MODIS, AMSR-E and Z-R relationship

8. Case study: Drizzle
Simulate adiabatic LWC from LWP and LIDAR cloud top
0.16
0.033
0.007
0.0012
Drizzle rate mm hr-1

9. Advantages:
LWP at 1km resolution coincident with cloud profiles during both local day and night
Possibility that lack of shadowing effects may give advantage over MODIS in horizontally small cumulus
Simple discrimination between drizzling and non-drizzling clouds by comparison of simulated Z from cloud water content and observed Z (dominated by drizzle)
Method can be applied to boundary layer clouds beneath ice cloud

10. Next steps: Currently refining quality flags
Statistics of LWP and drizzle occurrence
Model comparisons