Depolarization lidar for water
cloud remote sensing
1. Background: MS and
Depolaization
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
Lidar Multiple scattering
Lidar FOV cone
1st order
z
P ( z )  Clid z 2  ( z ) exp[ 2   ( z )dz ]
0
4th order
total
2nd order
3rd order
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
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)
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
• 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: Against other MC models and
Observations
ECSIM vs other
MC results
Circ
Validation (vs other models): Cases presented
in Roy and Roy, Appl. Opts. (2km from a C1
cumulus cloud OD=5)
lin
ECSIM MC results
Carswell and Pal
1980: Field Obs.
Roy et al. 2008: Lab results
From Space: ECSIM MC vs CALIPSO
Observations
Figure 1: Left: Histogram built using CALIPSO observations taken from Hu et
al. 2009. Middle ECSIM calculations. Right: Overlap of the first two panels.
Connection to water 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:
1/3
Adiabatic  Linear LWC profile and constant number density Reff  ( z  zb )
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.
Trial using one of the `blind-test’ LES scenes
WITH DRIZZLE !
Drizzle in lower part of cloud does
not present a problem
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 mrad fov
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.
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.
•
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.