1. Comparing various Lidar/Radar inversion strategies using Raman Lidar data (part II) D.Donovan, G-J Zadelhof (KNMI) Z. Wang (NASA/GSFC) D. Whiteman (NASA/GSFC)

2. Cnet October Delft Background/Rational Raman-vs-Elastic backscatter lidars Results 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 Rational KNMI lidar/radar routine developed for simple elastic lR backscatter lidar. No Rayleigh return MPL lidar data from ARM and SIRTA data has good Rayleigh signal. Should exploit it ! Good Raman lidar data can serve as semi- independent test of the strengths and weakness of different approaches. Will first concentrate on Visible extinction retrieval.

5. Cnet October Delft Elastic vs Inelastic scattering

6. Cnet October Delft

7. No Rayleigh, No Raman 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 with the Ze profile to estimate an effective particle size.

8. 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 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 !!

9. 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

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

11. 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 R’eff E3  Minimize derivative of ext E4  Minimize derivative of No*

12. Cnet October Delft A Test Case Using GSFC Raman lidar data and ARM MMCR.

13. Cnet October Delft Comparison Using Rayleigh return above cloud Using smooth R eff (  /Ze) constraint Signature of MS

14. Cnet October Delft Raman RatioRaman Direct Method 1:Use Rayleigh Method 2: Smooth  /Ze

15. Cnet October Delft Raman Direct Method 1:Use Rayleigh Method 2: Smooth  /Ze Raman Ratio

16. Cnet October Delft Raman Ratio-vs-Direct

17. Cnet October Delft

19. Combine Methods 1+2(4) ! Should work well in thicker Clouds also.

20. Cnet October Delft Combine Methods 1+4 (or 2) ! Should work well in thicker Clouds also. 4

21. Cnet October Delft Ext –vs- Ze

22. Cnet October Delft Conclusions Multiple scattering effects clearly seen. Appear well accounted for using Eloranta’s approach. Should use Rayleigh info if available ! Aim to create blended approach for non-Raman lidars to smoothly handle range of cases for non-Raman (i.e MPL) where Rayleigh signal from above cloud may or may not be available (almost there). Inferring optical depth using Ze alone very tricky on a case-by-case basis. Needs robust cloud masking (cld/nocloud/no info)

23. Cnet October Delft

24. Combine Methods 1+4 (or 2) ! Should work well in thicker Clouds also.

25. Cnet October Delft Raman Ratio-vs-Direct Raman RatioRaman Direct Raman RatioRaman Direct Method 1: Ray above Method 2: Smooth  /Ze (Raman direct)