1. Lidar+Radar ice cloud remote sensing within CLOUDNET. D.Donovan, G-J Zadelhof (KNMI) and the CLOUDNET team With outside contributions from… Z. Wang (NASA/GSFC) D. Whiteman (NASA/GSFC)

2. Cnet October Delft Background/Rational Approaches used in CloudNET How we have used data within Cloudnet Testing using Raman lidar data (the future) Summary Introduction

3. Cnet October Delft Active (lidar/radar) cloud remote sensing Lidar Radar Returned Power Time or Range Lidar Radar Difference in returns is a function of particle size !!   350-1100nm  3-100mm

4. Cnet October Delft Basic Considerations The lidar extinction must first be extracted from the lidar signal (or, equivalently, the observed lidar backscatter must be corrected for attenuation). Observed signal Calibration Constant Backscatter Extinction Ze used to link backscatter and extinction and facilitate extinction correction/determination process. The retrieved extinction (corrected backscatter) can then be used to estimate an effective particle size and IWC.

5. Cnet October Delft No Rayleigh No Raman Must use Klett (Fernald + Rayleigh) Must estimate extinction at z m (cloud top) Very difficult to do directly if one only has IR lidar info If have Radar then use smoothness constraint on derived lidar/radar particle size, or extinction, or No*. But solutions converge if optical depth is above 1 or so !!

6. Cnet October Delft Forward inversion is more direct but it is unstable !! Radar Lidar 10 % error ForwardBackward 10 % error In CLOUDNET most lidar data is IR  No usable Rayleigh signal  Radar quite helps

7. Cnet October Delft Effective Particle Size for Ice Crystals Ice particles are large compared to lid (Optical scattering regime) Ice particles are small compared to rad (Rayleigh scattering regime) Exact treatment of scattering difficult (impossible?) However: Confirmed using DDA and RT calculations

8. Cnet October Delft Two main approaches within CNET (for IR lidars) Lidar Signal Effective Radius IWC Extinction Lidar+Radar Signals  /Z e =F(R' eff ) Retrieve R' eff,  Habit/size dist form info. R eff IWC Radar Reflectivity MS correction KNMI Approach Chooses BV Such that Variation of Reff Is minimized

9. Cnet October Delft CETP approach uses concept of Normalized number density No* associated with scaled size distribution Based on In-situ Aircraft Observations CETP Approach Chooses BV Such that Variation of No* Is minimized

10. Cnet October Delft How we have used results within CNET ? Current approaches Limited by fact that Must have BOTH Good Radar and Lidar Data even to get Extinction

11. Cnet October Delft So coverage is incomplete……  But we still have10 5 s of data points !!  Good for parameterization development !  Red  ARM SGP  BLUE  CNET

12. Cnet October Delft Can also use limited Lidar+Radar data as benchmark to assess accuracy of Radar only IWC estimates

13. Cnet October Delft The Future ? The `super’ IR celiometers used at CABAUW and Chilbolton really are not optimal. Ideal is a high power 24/7 Raman system !! But that is expensive (but will be coming to CABAUW!) Settle for 24/7 visible or UV system of good sensitivity for ice clouds.

14. Cnet October Delft Elastic vs Inelastic scattering

15. Cnet October Delft

16. A Test Case Using GSFC Raman lidar data and ARM MMCR. Eo  S-S’ E1  Force R=1 where no cloud. E2  Minimize derivative of ext E3  Minimize derivative of R’eff E4  Minimize derivative of No* Test various approaches w.r.t Raman results

17. Cnet October Delft MS effects: Consistency between approaches  Can be accounted for Signature of MS

18. Cnet October Delft Comparison of Techniques

19. Cnet October Delft Comparison of Techniques (In terms of OT)

20. Cnet October Delft OPTIMAL APPROACH (FOR ELASTIC RAYLEIGH LIDARS) Combine Methods 1+3(4) ! Should work well in thicker Clouds also.

21. Cnet October Delft Conclusions Lidar Radar ice cloud remote sensing is becoming mature Limitations and strengths of technique becoming more understood. Increasing body of comparisons with In-Situ measurements Most useful in CNET for statistical parameterization of ice cloud effective radius parameterizations and to help estimate accuracy of Radar only IWC estimations. Ideal is to use Raman Lidar. If this is not an option then a good vis/uv lidar with a Radar is a good option.

22. Cnet October Delft Ext –vs- Ze

23. Cnet October Delft If we have Useful Rayleigh above the cloud. Then (effectively) can find S and C lid so that The scattering ratio R is 1.0 below and above cloud

24. Cnet October Delft If We have good Raman data then… Direct but noisy Less noisy but indirect

25. Cnet October Delft Implementation Cost = Eo + W1*E1 +W2* E2 +W3*E3 + W4*E4 Eo  S-S’ E1  Force R=1 where no cloud. E2  Minimize derivative of ext E3  Minimize derivative of R’eff E4  Minimize derivative of No*