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Depolarization lidar for water cloud remote sensing. Background: MS and Depolaization Short overview of the MC model used in this work Depol -lidar for Water Cld remote sensing: Model cases Example with Real data Summary. Lidar Multiple scattering. Lidar FOV cone. 1 st order.

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depolarization lidar for water cloud remote sensing

Depolarization lidar for water cloud remote sensing

Background: MS and Depolaization

Short overview of the MC model used in this work

Depol-lidar for Water Cld remote sensing: Model cases

Example with Real data


lidar multiple scattering
Lidar Multiple scattering

Lidar FOV cone

1st order

4th order


2nd order

3rd order

Photons can scatter

Multiple times and remain within lidar Field-Of-View

Enhanced return w.r.t single scattering theory

Scattering by cloud droplets of

At uv-near IR is mainly forward


Multiple Scattering induced depolarization

  • For a polarization sensitive lidar MS also gives rise to:
  • A Cross-polarized signal even for spherical targets.
  • Depends on:
    • Wavelength
    • Size Dist.(Reff profile)
    • Extinction profile
    • Filed Of View
    • Distance from Lidar

In order to calculate MS enhanced signal and depol accurately Monte-Carlo approaches must be used.

what is a mc simulation simple example with no variance reduction techniques
What is a MC simulation ? (simple example with no variance reduction techniques)

Launch Photon packet

Determine path length until next interaction using PRNG and Beer’s law

Determine scattering angle using PRNG and scatterer’s phase function

Loop until packet is absorbed, hits receiver or migrates too far from the receiver fov

Loop in packet until desired SNR is reached

ecsim lidar monte carlo model
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-basedsimulations.
validation against other mc models and observations
Validation: Against other MC models and Observations

ECSIM vs other MC results

Validation (vs other models): Cases presented in Roy and Roy, Appl. Opts. (2km from a C1 cumulus cloud OD=5)



ECSIM MC results

Carswell and Pal 1980: Field Obs.

Roy et al. 2008: Lab results


Connection towater cloud remote sensing….

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.


Some Examples:

A simple water cloud model is used:

Adiabatic  Linear LWC profile and constant number density


D_LWC/dz = 0.5 gm-3

D_LWC/dz = 1.0 gm-3

Para Profiles normalize so that the peak is 1.0

Look-up-tables were made for several cloud-bases, different size-dist widths and receiver fovs.


Same extinction profile but different Reff profiles

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

However at larger effective radii values then there is no size sensitivity.


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


A case using real data

A real case:

Cabauw: Leosphere ALS-450

355nm, 2.3 mradfov


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.

  • 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.

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.