1. Depolarization lidar for water cloud remote sensing 1.Background MS and MC 2.Short overview of the MC model used in this work 3.Depol-lidar for Water Cld remote sensing: Model cases 4.Example with Real data 5.Summary

2. MS gives rise to: Increase in lidar signal compared to SS Cross-polarized signal even for spherical targets. Depends on: Wavelength Size Dist. Extinction profile FOV Distance from Lidar Lidar+clouds  Multiple Scattering : MS

3. ECSIM lidar MC model Fast MC lidar model developed originally for EarthCARE simulations. Capable of simulations at large range of wavelengths and viewing geometries including ground-based. Recently used in a component of COST Blind- Test algorithm intercomparision activities.

4. Val: Against other MC models and Observations Validation (vs other models): Cases presented in Roy and Roy, Appl. Opts. (2km from a C1 cumulus cloud OD=5) Circ lin Carswell and Pal 1980: Field Obs. Roy et al. 2008: Lab results ECSIM MC results ECSIM vs other MC results

5. From Space: MC vs CALIPSO OBS

6. Not too long ago, motivated by the observations of highly depolarizing volcanic ash I was looking for a way to verify the depol. calibration of a lidar system I operate. Motivated by Hu’s results for Calipso, I wondered if strato-cu could be a good target So I setup a script to run my MC code on several hundred cases using a simple water cloud model (Fixed LWC slope and Constant N) The results were initially disappointing…..the resulting depol and backscatter relationships depended too much on the LWC slope and N ! …… Hmmm….. maybe I should look at this in some more detail from the other side. Connection for water cloud remote sensing….

7. D_LWC/dz = 0.5 gm-3D_LWC/dz = 1.0 gm-3 MC look-up-tables made for several cloud bases and different size-dist widths and receiver fovs. Para Profiles normalize so that the peak is 1.0

9. Depol and `Shape’ largely a function of extinction profile but exploitable differences exist, especialy at small particle sizes (depends somewhat of fov).

10. Trial using one of the `blind-test’ LES scenes WITH DRIZZLE !

11. Drizzle in lower part of cloud does not present a problem

12. Since effectively only information from the lowest 100 meters of the clouds is used. Departures from “good behavior” particularly near cloud top are problematic.

13. A case using real data A real case: Cabauw: Leosphere ALS-450 355nm, 2.3 mrad fov

15. Comparison with uwave radiometer observations and sensitivity to size-dist width assumptions, fov and depol calibration uncertainties Ran out of time… ….but preliminary findings are encouraging.

16. Summary Lidar Depolarization measurements are an underutilized source of information on water clouds. Fundamental Idea is not new…Sassen, Carswell, Pal, Bissonette, Roy, etc… have done a lot of work stretching back to the 80’s and likely earlier. But now with better Rad-transfer codes and much faster computers a re-visit is in order.

17. The general problem (i.e. the inversion of backscatter+depol measurements to get lwc profile and Reff under general circumstances ) is complex and likely requires multiple fov measurements. However… Constraining the problem to adiabatic(-like) clouds simplifies things and enables one to construct a simple and fast inversion procedure. Still early days but the idea looks worth pursuing. There is A LOT of existing lidar observations it could be applied to. Results are insensitive to presence of drizzle drops ! Lots of opportunities for synergy with radars, uwave radiometers and other instruments. Will require some thinking on how to integrate within an Ipt-like scheme.