1. Motivation: Cloud-Aerosol interactions2. Background: Lidar Multiple Scattering and

Depolarization3. Depol-lidar for Water Cld. remote sensing

Inversion method for Nc, LWC, Reff at Cloud base

Simulation Results using LES clouds

4. Examples with Real dataApplication to Cabauw lidar obs.Comparison with aerosol number densities

5. Summary

1 Royal Netherlands Meteorological Institute (KNMI). PO Box 201, 3730 AE De Bilt, The Netherlands. donovan@knmi.nl2 Netherlands Organisation for Applied Scientific Research (TNO), Utrecht, The Netherlands.3 Technical University of Delft (TUD), Delft, The Netherlands.

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

Motivation

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

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

2

cz

time

Lidar Multiple Scattering (MS)

Scattering by cloud droplets of At uv-near IR is mainly forward

Photons can scatterMultiple times and remain within lidar Field-Of-View

Enhanced return w.r.t single scattering theory

1st order

2nd order 3rd order

total

4th order

Lidar FOV cone2

0( ) ( ) exp[ ( ) ]2

z

lidP z C z z z dz

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 (Reff ) profile• The Extinction profileLiquid 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 ?

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.

Look-up table based inversion procedure

Para.

Perp.

Depol

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..

Inversion approach tested and developed using LES based simulations.

Black and Green: (simulated) observationsRed and Blue: Retrieval Fits.

Simulation Example I

Retrieved Instrument Depol calibrationfactors

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

Simulation Example II

Red “Truth”Black Inversion resultsGrey Estimated uncertainty range

Extinction at 100m from cld. base

Effective radius 100m from cld. base

Slope of LWCat cld. base

Slope of LWCat cld. base

Adiabatic limit

Radar reflectivityPredicted by lidar results(Light-BlueDrizzle Contribution removed)

Application to Real Data at Cabauw

Real Example I (UV LEOSPHERE lidar At Cabauw)

In non-drizzle conditions: Good comparison with 35 GHz Ze !

Effective radius

LWC slope

Number concentration

ParaZe

Lidar predicted valuesbinned to coarser radar vert. grid

Real Example II (UV LEOSPHERE lidar At Cabauw : Drizzle present)

Effective radius

LWC slope

Number concentration

ParaZe

Drizzle

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.

Each Point1/2 hr sample. Different symbols Different months

Tower Measurements

Lida

r Inv

ersi

on re

sults

Different Empirical RelationshipsBased on aircraft obs.(see Pringle at al. 2009)

Retrieval Problems ? Hard to say as results are still physically plausible(see Pinsky et al 2012)

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 .

Lida

r ret

rieve

d LW

C sl

ope

Adiabatic LWC slope

One-to-one line

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…Most earlier theoretical work assumed homogeneous clouds (i.e. constant LWC and Reff). But now with better Rad-transfer codes and much faster computers more realistic cloud models can be treated.

• 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 !

• 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)

Most Clouds Examined appear to have a drizzle component

A Few examples drawn from the MC generated LUTs

A simple water cloud model is used: Linearly increasing LWC profile and constant number density 1/3( )eff bR z z

Para

Depolratio

Perp

Lidar Wavelength 355nm

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.

Synergy with Satellite Cloud Observations

LWC

Altit

ude

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 measurementReff 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

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

WaterWater

Ice

Carswell and Pal 1980: Field Obs. Roy et al. 2008: Lab results

ECSIM MC results

2D Camera Images

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.

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)

Circlin

ECSIM vs other MC results

Comparison with CALIPSO obsInt Beta –vs-Int Depol

Range of CALIPSO ObservationsPoints are ECSIM results for

CALIPSO configuration

Hu et al.

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.