Depolarization lidar for water cloud remote sensing 1.Background: MS and Depolaization 2.Short...
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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
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
Slide 2
Lidar Multiple scattering 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
Slide 3
For a polarization sensitive lidar MS also gives rise to: A
Cross-polarized signal even for spherical targets. Depends on:
Wavelength Size Dist.(R eff profile) Extinction profile Filed Of
View Distance from Lidar Multiple Scattering induced depolarization
In order to calculate MS enhanced signal and depol accurately
Monte-Carlo approaches must be used.
Slide 4
What is a MC simulation ? (simple example with no variance
reduction techniques) Launch Photon packet Determine path length
until next interaction using PRNG and Beers law Determine
scattering angle using PRNG and scatterers 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
Slide 5
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.
Slide 6
Validation: 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
Slide 7
From Space: ECSIM MC vs CALIPSO Observations
Slide 8
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 Hus
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 to water cloud remote sensing.
Slide 9
Some Examples: A simple water cloud model is used: Adiabatic
Linear LWC profile and constant number density
Slide 10
D_LWC/dz = 0.5 gm-3D_LWC/dz = 1.0 gm-3 Look-up-tables were made
for several cloud-bases, different size-dist widths and receiver
fovs. Para Profiles normalize so that the peak is 1.0
Slide 11
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. Same extinction profile but
different Reff profiles
Slide 12
Trial using one of the `blind-test LES scenes WITH DRIZZLE
!
Slide 13
Drizzle in lower part of cloud does not present a problem
Slide 14
Since effectively only information from the lowest 100 meters
of the clouds is used. Departures from good behavior particularly
near cloud top are problematic.
Slide 15
A case using real data A real case: Cabauw: Leosphere ALS-450
355nm, 2.3 mrad fov
Slide 16
Slide 17
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.
Slide 18
Summary Lidar Depolarization measurements are an underutilized
source of information on water clouds. Fundamental Idea is not
newSassen, Carswell, Pal, Bissonette, Roy, etc have done a lot of
work stretching back to the 80s and likely earlier. But now with
better Rad-transfer codes and much faster computers a re-visit is
in order.
Slide 19
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.