1. 1.Motivation: Cloud-Aerosol interactions 2.Background: Lidar Multiple Scattering and Depolarization 3.Depol-lidar for Water Cld. remote sensing Inversion method for N c, LWC, R eff at Cloud base Simulation Results using LES clouds 4.Application to CESAR data and ACCEPT results 5.Summary 1 Royal Netherlands Meteorological Institute (KNMI). PO Box 201, 3730 AE De Bilt, The Netherlands. donovan@knmi.nl Depolarization Lidar Observed Stratus Cloud based number density and adiabatic fraction during the ACCEPT campaign.

2. Aerosol Cloud Interactions Aerosols act as CCN For fixed amount of available water: more aerosol  more CCN  more smaller droplets  brighter clouds Number of knock-on effects which can damp or reinforce the impact of aerosols

3. LIght Detection And Ranging (LIDAR) Laser Telescope Spectral filter for rejection of unwanted background sky light Detector (PMT, APD etc..) Distance to target is found by measuring the time-resolved return signal after the launch of a “short” laser pulse time

4. Lidar Multiple Scattering (MS) Scattering by cloud droplets of At uv-near IR is mainly forward Photons can scatter Multiple times and remain within lidar Field-Of-View Enhanced return w.r.t single scattering theory 1 st order 2 nd order 3 rd order total 4 th order Lidar FOV cone

5. For a polarization sensitive lidar, MS gives rise to a Cross-polarized signal even for spherical targets. Depends on: Wavelength Field Of View Distance from Lidar and (more interestingly) The effective particle radius (R eff ) profile The Extinction profile  Liquid Water content and Number density Multiple Scattering induced depolarization Can one use depolarization lidar data to estimate cloud LWC and number density at cloud base ?

6. Look-up table based inversion procedure Para. Perp. Depo l Question: Can one use depolarization lidar data to estimate cloud LWC and number density at cloud base ? Answer: Yes (as revealed by the analysis of MC runs applied to a range of idealized clouds) Which led to the development of a..

7. Inversion approach tested and developed using LES based simulations. Black and Green: (simulated) observations Red and Blue: Retrieval Fits. Simulation Example I Retrieved Instrument Depol calibration factors Retrieved Cloud properties  can be used to predict No and other properties Procedure is “blind” to low levels of drizzle. Simulated Ze Simulated Para Horizontal OT of LES field

8. Simulation Example II Red  “Truth” Black  Inversion results Grey  Estimated uncertainty range Extinction at 100m from cld. base Effective radius 100m from cld. base Slope of LWC at cld. base Slope of LWC at cld. base Adiabatic limit Radar reflectivity Predicted by lidar results (Light-Blue  Drizzle Contribution removed)

9. Application to Real Data at Cabauw

10. Real Example I (UV LEOSPHERE lidar At Cabauw) In non-drizzle conditions: Good comparison with 35 GHz Ze ! Effective radius LWC slope Number concentration Para Ze Lidar predicted values binned to coarser radar vert. grid

11. Real Example II (UV LEOSPHERE lidar At Cabauw : Drizzle present) Effective radius LWC slope Number concentration Para Ze Drizzle

12. Sample Application 3 months Lidar vs Tower SMPS measurements Only cases connected that appear connected to the BL are selected (Geen). Cases above the BL (Red) are excluded since the Tower aerosol measurements are not expected to be representative of the CCN numbers.

13. Each Point  1/2 hr sample. Different symbols  Different months Tower Measurements Lidar Inversion results Different Empirical Relationships Based on aircraft obs. (see Pringle at al. 2009) Retrieval Problems ? Hard to say as results are still physically plausible (see Pinsky et al 2012)

14. Pinsky et al. (JGR doi:10.1029/2012JD017753, 2012) based on theoretical arguments predict that at the altitude of super-saturation max (which is usually within 10’s of meters from cloud base) that LWC/LWC_adiabatic= 0.44 regardless of CCN type +number and updraft velocity. The Lidar values are perhaps consistent with this prediction. Lidar retrieved LWC slope Adiabatic LWC slope One-to-one line

15. Oct-Mid Nov 2014 ACCEPT campaign at Cabauw UVlidar was operating Sensitive high resolution radar from TROPOS Polarization lidar from Tropos (verify uvlidar depol. calibration) UV DEPOL lidar inversion applied to Oct data

16. Typical Example: High N Sub-abiabatic LWC profile Mix of drizzling and non-drizzling clouds

17. Another Typical Example: High N Sub-abiabatic LWC profile Mix of drizzling and non-drizzling clouds

18. Focusing in on the adiabatic fraction

19. Summary So UV-DEPOL results ACCEPT results are consistent with earlier. – Better radar data for validation – Depol calibration more certain Work is underway to compare results with oter inversion routines (see next talk by S. Rusli) Why is Fa low ? – Mixing not captured by LES ? – Effects of drizzle ? – What are the implications ? Comparison with tower based measurements not yet done. Would like to spend more time on this but most time going to meet (Paid) ESA deadlines !

20. The general problem (i.e. the inversion of backscatter+depol measurements to get lwc profile and R eff 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 ! Preliminary results look very realistic – Agreement with Radar Ze in non-drizzle conditions – LWC mixing ratio at cloud base consistent with theoretical predictions – Nd vs Na measurements are consistent with earlier in-situ work and theoretical range Lots of opportunities for synergy with radars, uwave radiometers and other instruments, including Satellites (e.g. MSG) Vertical velocity measurements would be very useful ! (Radar Vd can likely be used sometimes but only in strict non-drizzle conditions. For Cabauw  < -35 DBz)

21. Most Clouds Examined appear to have a drizzle component

22. A Few examples drawn from the MC generated LUTs A simple water cloud model is used: Linearly increasing LWC profile and constant number density  Para Depol ratio Perp Lidar Wavelength 355nm

23. Role of ground-based Remote sensing Due to the nature of liquid water cloud formation information regarding cloud-base conditions is quite valuable Satellite cloud observations are very useful but are give very limited direct info on cloud-base conditions Ground-based remote sensing techniques are well-suited for investigating cloud-base conditions Depolarization lidars are an under-utilized source of info on cloud-base conditions.

24. Synergy with Satellite Cloud Observations LWC Altitude Surface based Lidar  (cloud- base Information) Microwave radiometer  LWP Radar  Constrains cloud-top and identifies presence of precip. Satellite VIS-NIR Radiance measurements Tau  Integrated measurement R eff  Weighted towards cloud-top. Depends on cloud structure and wavelength pair used. SEVERI: Obs. every 15 mins. ! Estimation of cloud-structure covering whole cloud Improved accuracy of CM-SAF products

25. CALIPSO-532 nm EarthCARE 355 nm Water-vs-Ice Discrimination (established for CALIPSO by Hu  ) Further: Perhaps some microphysical information can be extracted ? Spin-off: Application to Space-Borne lidars Ice Water Ice

26. Carswell and Pal 1980: Field Obs. Roy et al. 2008: Lab results ECSIM MC results 2D Camera Images

27. ECSIM lidar Monte-Carlo model MC lidar model developed originally for EarthCARE (Earth Clouds and Aerosol Explorer Mission) satellite based simulations. Uses various “variance reduction” tricks to speed calculations up enormously compared to direct simple MC (but is still computationally expensive). Capable of simulations at large range of wavelengths and viewing geometries, including ground-based simulations.

28. Validation: Example comparisons with other MC models and Observations Cases presented in Roy and Roy, Appl. Opts. (2km from a C1 cumulus cloud OD=5) Circ lin ECSIM vs other MC results Comparison with CALIPSO obs Int Beta –vs-Int Depol Range of CALIPSO Observations Points are ECSIM results for CALIPSO configuration Hu et al.

29. Connection to Water Cloud Remote Sensing Aim to predict cloud LWC and extinction/number density at cloud base Use ECSIM-MC code to create look-up tables of depolarized lidar returns Assume linear LWC profile and fixed No near cloud base. Normalize the lidar returns using the peak of the Para return signal so that the lidar does not need to be calibrated (Depol. ratio must be calibrated though) Errors in Normalization as well as depol. calibration and cross-talk factors accounted for by casting the problem in an Optimal Estimation Framework.

30. Lidar Monte-Carlo Radiative Transfer Calculations There is no analytical model that accurately predicts lidar MS+Polarization effects under general conditions (e.g. cloud properties vary with range). So… We use a Monte-Carlo (MC) lidar RT model that includes polarization. MC  Very many virtual Photons are propagated and scattered in a stochastic fashion (driven by random sequence). Kind of Ray- Tracing approach. Extinction coefficient and phase function fields define the propagation length and scattering angle distributions.

31. Aerosol-Cloud Interactions remain a source of large uncertainty (AR5) Motivation