Download Topic 6 Properties of clouds particles, e.g. optical, morphology, density

Topic 6 Properties of cloud
particles, e.g. optical parameters,
morphology, density
Bundke,
Gayet,
Mőhler,
Wendisch,
Stetzer,
Petters
Introduction:
(Motivation / Importance of knowing the parameters)
– Microstructure of ice crystals as function of T, S
– Atmospheric optics (some nice pictures)
– Terminal velocity
– Morphology and radiation
– Calculation of scattering
– Influences on interpretation of remote sensing
http://www.snowcrystals.com/
Introduction Shape Temperature
Variations of ice crystal habit with temperature and Supersaturation
e.g. Kobayashi (1961) Magoo and Lee (1966), Rottner and Vali (1974)
http://www.snowcrystals.com/
Heymsfiled 2000
Morphology and density
Morphology (coefficient of drag) and density (gravitational
force) govern the sedimentation velocity (final velocity) and
thus the collecting/coagulation efficiency for microphysical
modeling (growth of hydrometeors by collision,
coalescence, riming,.. )
» Aerodynamic shape factor / eff. Diameter
» Particle mass / eff. Volume
Microstructure influences on
» radiative effects
» cloud lifetime
» precipitation
Westbrook, 2008; Heymsfield, 1999
Cirrus particle terminal velocity I
Mean Doppler velocity (cm
1/s; top) and equivalent radar
reflectivity (in dBZe)
Heymsfield 2000
Cirrus particle terminal velocity II
Heymsfield ,2000
Cirrus particle terminal velocity III
Westbrook, 2008
Introduction atmospheric optics
http://www.snowcrystals.com/
Radiative interactions
• Understanding of the radiation budget of earth
and atmosphere system, and hence its climate,
must begin with an understanding of the
scattering and the absorption properties of cloud
particles, a large number of cloud particles are
non-spherical ice crystals. Basic scattering,
absorption and polarizing data for these particles
are needed.
(Liou 2000)
Morphology Optically
and Radiation
thin Cirrus,
SOLAR
Thin CIRRUS
solar
= 21°
Ice absopion bands
s
Sphere
F 
s
2. Auswirkungen auf Strahlungsenergiebudget – Kristallform
Wendisch et al. [2005]
Morphology
and
thick
Optically
Thick Radiation
CIRRUS
 = 78° solar
Cirrus,
Ice absopion bands
SOLAR
s
Sphere
F 
s
2. Auswirkungen auf Strahlungsenergiebudget – Kristallform
Wendisch et al. [2005]
MorphologyOptically
and Thin
Radiaion
thin Cirrus,
THERMAL IR
CIRRUS
Cloudless
thermal
F
Sphere
2. Auswirkungen auf Strahlungsenergiebudget – Kristallform
Wendisch et al. [2007]
Interpretation of reflectance
R = 22 µm,  = 5.2
measuremets
R = 24 µm,  = 3.3
eff
eff
3. Fernerkundung: Probleme – Zirren (Kristallform)
Eichler et al. [2009]
Calculation of electromagnetic
scattering and absorption
• Last Century:
“soft sphere”/ “fluffy sphere” approach
equal mass sphere (blend of ice and air) with
dielectric properties derived using Maxwell
Garnet (1904) or Bruggemann (1935) mixing rule
• Now:
– FDTD finite-difference time domain method
(Taflove and Hagness 2000)
– CDA = coupled dipole approximation
also known as DDA=discrete dipole approximation
(Purcell and Penypacker, 1973; Dungey and Bohren,
1990;….; Petty and Huang, 2009)
Mie Theory
scattered wave
Incident wave
wave inside particle
Ansatz:
wave is divided into three parts:
a) incident light,
Diss. Hermann Vortisch
b) wave inside the scattering particle
c) scattered wave (far field)
•All three parts have to fulfill the wave equation
•The particle surface marks a discontinuity of the optical parameters (complex
refractive index)
•To be found: a solution of the coupled differential (Maxwell) equations for each part
•Boundary condition: Tangential component of the fields inside and outside the
particle match continuously at the particle surface
Mie
linear scale
Phase functions
(polar plot)
Log scale
CDA Model
Examples of dipole representations of
soft spheres ranging in density from
100% (solid
ice) to 2%. Each contains
approximately 5000 dipoles.
Model snow particle structures.
(a) Needle aggregate NA,
(b) dendrite aggregate DA1,
(c) dendrite aggregate DA2,
(d) dendrite aggregate DA3.
(Petty et al, 2009)
CDA
CPU TIME
Examples of the dependence of the CPU time requirement
on N for different soft sphere ice fractions, using DDSCAT
(dashed curves) and SCDScat (solid curves). These calculations
are for 35.6 GHz and d 5 50 mm on a 1.8-GHz Intel Core Duo
workstation.
Petty et al 2009
Soft-ice approx.
Comparison of computed (top) backscattering cross sections, for soft
sphere ice particles with rliq = 1.0 mm. Methods used include CDA with
d=64 µm (large dots), Bruggeman (dotted line),Maxwell Garnett with ice
as inclusion (solid line) and with air as inclusion (long dashed), and the
exponential rule with n = 0.85 (short dashed).
Petty et al 2009
Remote Sensing Retrievals
• As example:
Parameters/ Assumptions for global analysis of Cloud
droplet effective radius (CDR) using depolarization (e.g.
POLDER instrument)
• (Cirrus Cloud optical and microphysical properties
determined from AIRS infrared spectra
(Yue and Liou 2008)
Influence of non-spherical particles on the
interpretation of remote sensing devices
• Lidar
– (Liou and Takano 1994)
– (Hu et al. Depolarization signature -> phase discrimination)
• Radar (Austin 2009, Heymsfield 2005)
– Only ice spheres were assumed for retrieval (e.g. CloudSat)
• Satellite retrievals of cloud properties: examples..
• POLDER (Parol 2004, Bréon 1998)
• CloudSat (Stephens 2002,2008, Grecu and Olson 2008)
• TRMM Tropical Rainfall Measuring Mission (Kummerow 2000)
• Advanced Microwave Scanning Microwave Radiometer
(Kawanishi 2003)
• GPM (Global precipitation Measurement) Sat. to be launched 2013
-> GMI Microwave Imager
• …
POLDER
130
170
160
120
150
Bréon,GRL, 1998
POLDER (non spheric paticles)
Ratio (AOT of large non-spherical particles)/(AOT of large particles)
http://www.icare.univ-lille1.fr/parasol/?rubrique=overview_product
Sattelite Retrival (POLDER)
Cloud droplet radius derived from POLDER measurements during the spring of
1997. The units are microns. White areas correspond to regions where no
successful estimate was possible. This image is based upon a compilation of
19,500 estimates.b
Breon, Science 2002
TOPIC 6
Measurements: State of the Art
Overlaps partly with Topic 16 new
sensors
Optical Parameters
(individual and ensembles of particles)
• Extinction Coefficient
(e.g. CEP Cloud extinction probe (ICCP 2008, A. Korolev)
• Scattering Phase Function
(e.g. polar Nephelometer Data (Gayet))
• Scattering Coefficient
• Absorption Coefficient ?
• Asymmetry parameter of the volume scattering function
(e.g.CIN Cloud integrating Nephelometer
g-meter)
• Sigle Scattering Albedo
(Calculations..Yang et al 2010)
• Complex Refractive index as mixture of ice and air -> see density,
Mixing Rules
Optical Parameters (Cloud)
what we also need…
•
•
•
•
Size distribution
Ice/ water partitioning
Crystal morphology/ composition
Effective size of ice particles
(def. McFarquhar and Heymsfield 1998, Wyser 1998, Fu 1996)
• Cloud solar albedo e.g. as function of IWP
Optical parameter: CEP
Cloud Extinction Probe
Principle Beer-Bouguer law (transmission method)
(A. Korolev , Abstracts ICCP 2008)
Optical Properties: CEP watercloud
Water Cloud
Rosemount Icing
Detector signal.
(A. Korolev,2008)
Optical Parameter: CEP ice cloud
• CEP in Ice cloud
(A. Korolev, 2008)
Optical Parameter: CEP Mixed phase
cloud
Mixed phase cloud
In mixed phase cloud regions,
the extinction coefficient measured by the
Extinction Probe is larger than that
calculated from the OAPs
mixed phase zones
(A. Korolev, 2008)
CEP advantages
• large sample area (~60cm2)
• measurements are practically not
contaminated by shattered ice particles
• The Cloud Extinction Probe can be used to
identify and characterize shattering and
plashing efficiency of different cloud particle
size spectrometers
• threshold sensitivity is estimated at 0.2 1/km.
(A. Korolev, 2008)
Optical Parameter: Phase function
– PN: Polar Nephelometer (Gayet)
– SID/SID 2 ( “phase function”) in forward direction ,
• Ice / Water separation
– PHIPS: Particle Habit Imaging and Polar Scattering
Probe (Schnaiter, Möhler)
Gayet, 2009
Nephelometer I
22° Halo peak
Polar Neph.
FSSP+Mie
Conc : 1.9 cm-3
C100 : 7.3 l-1 IWC
: 14 mg m-3 Ext :
0.49 km-1 Deff :
86 mm
g
:
0.795
a
Irregular
< 1%
16%
b
CPI
15%
Rosette
5%
7%
SidePlane
Dendrite
Plate
57%
cirrus sampled near -27°C
Column
c
c
Needle
d
Fig. 1
Gayet, 2009
Nephelometer II
No 22° Halo peak
Polar Neph.
FSSP+Mie
Conc : 1.4 cm-3
C100 : 0.3 l-1 IWC
: 2 mg m-3 Ext :
0.28 km-1 Deff :
21 mm g
:
0.788
5%
b
a
Irregular
5%
CPI
Rosette
SidePlane
48%
Dendrite
42%
Plate
Column
cirrus properties near -59°C
c c
Needle
d
Fig. 2
Gayet, 2009
cCPI = 50 l-1
IWC
= 0.25 g/m3 Deff =
190 mm
Z = 20
dBZ
R = 1.7
mm/h
Diameter (mm)
Ext = 10 km-1
g = 0.786
Scattering coefficient (mm-1 sr-1)
Concentration (mm-1 l-1)
Nephelometer III arctic mixed phase
clouds
Scattering angle (°)
mid-latitude cirrus
phase functions (in the
visible) are smooth and
featureless
0
500 mm
From Gayet et al., ACP, 2009
Figure 4
Interpretation of CALIPSO observations (level 2.01) from in situ
measurements : CIRCLE-2 Experiment (16 May 2007) (From
Mioche et al., 2010, JGR, in press).
● Frontal cirrus over Ocean (West of France)
● Cloud top : -59°C, Visible optical depth ~0.5
CALIOP Extinction coeff.
MODIS cloud field
Closure Experiment CALIOP vs. PN
11
0.8
12
0.8
Altitude (km)
10
8
σ (km-1)
6
0
45.5
1
46
2
3
46.5
47
Latitude (°N)
σCALIOP (km-1)
σext / Caliop [km-1]
0.6
0.4
0.6
0.2
0.4
0
16 Mai
y = 2.27x ± 0.58
R² = 0.65
0.2
0
00
0.2 0.2 0.4
0.6
0.4
σNP
0.80.6
1
0.8
(km-1)
σext / PN [km-1]
Figure 3
1
Current state of the art
Particle Size and Shape Determination
– Single Particle Light Scattering
•
•
•
•
•
•
Background Mie Theory
FSSP Forward Scattering Spectrometer Probe
CDP Cloud Droplet Probe (mostly identical to FSSP)
CAS Cloud and Aerosol Spectrometer
SID/SID2
Small Ice Detector
PMS 2D-C, 2D-P
• PDPA Phase Doppler Particle Analyzer or (PDI) Phase
Doppler Interferometer
Principle of Single particle scattering
devices
CAS optics
• Interpretation
using Mie Theory
SID-2
hybrid photo-diode (HPD)
PHIPS – Particle Habit Imaging and Polar
Scattering Probe
8
•
6
•
Stereo imaging for reconstruction of 3D
particle shape and orientation
Simultaneous measurement of the polar
scattering function in 1° - 170° angular range
log(Intensity)
Features
7
Mie
PHIPS
100 µm
5
4
3
2
Reconstructed Particle
1
0
Image 1
Image 2
20
40
60
80
100
120
140
160
180
Angle (°)
100 µm
Optics
Electronics
Schnaiter, Möhler 2010, priv. comm
Current state of the art
Particle Size and Shape Determination
– Particle imaging
•
•
•
•
•
VIPS Video Ice Particle Sampler
Cloud Scope Imaging microscope
CPI
Cloud Particle Imager
CIP
Cloud Imaging Probe
NIXE-CAPS
(combined device)
(New Ice eXpEriment- Cloud and Aerosol Particle Spectrometer)
• Holographic methods
– digital-holographic particle imaging system
(Raupach, Borrmann Vössing, 2006, 2009) (groud based)
– HOLODEC (Fugal, Shaw, 2004)
– HOLIMO (Amsler, Stezer, 2009)
Principles Particle imaging
Reflection
Refraction
Diffraction
Principles Particle imaging
•
Principle optical diagram utilized in the
airborne particle imaging probes
EUFAR Book Chapter 6
Baumgardner/Brenguier
EUFAR Book Chapter 6
Baumgardner/Brenguier
Principle Diffraction
Image depends only on the
dimensionless variable
Zd 
Z
R2
Diffraction image equals if
Z1 R12
Z1  Z 2 
 2
Z 2 R2
Strength and limitations
Particle imaging technique
Positive
•
•
Wide range of measured particle sizes D>2µm
Particle sizing is independent to the particle shape and refractive index
Negative
• Depend on the particle orientation in the sample volume of the probe and
it is sensitive to the viewing angle
• The image size depends on the distance from the object plane. The error
in particle sizing may reach a factor of 1.8. The size retrieval algorithms
have to be applied
• The depth-of-field and the sample area is a function of particle size
• The sizing of particles smaller than 4 pixels in size is subject to large errors
related to image digitization. Small images also have larger uncertainty in
the depth-of-field definition, which may result in large errors in
concentration
VIPS
Video Ice Particle Sampler
M. Krämer, p. c., 2010
NIXE-CAPS
Current state of the art
Particle Size and Shape Determination
• Density (sampling needed)
– Tomographic method to obtain particle density
(Kersten/Midaner) (ground based, lab only)
– Sampling and gas-pycnometry
(e.g. Hänel and Thudium, 1973; Tamari, 2003)
– DMA and Mass spectrometer (Zelenyuk, 2005)
• Density (Volume) in situ
– Holographic method -> 3D-Volume?
How to estimate the Mass ???
Density
• X-Ray Microtomography
(Miedaner Kersten)
Murshed , Kersten GRL 2008
Air filled voids inside a natural graupel grain sampled
at the Jungfraujoch research station.
Long frame edge is just one mm, spatial voxel
resolution is 1.4 μm. The total volume of the
sample is 0.75 mm3. The porosity of the grain is 5%,
while the inner surface yield as much as 6 mm².
Midaner ,2007
Density
• Gas Pycnometer
(Hänel,Thudium 1973)
• Principle ; Pressure const vs. volume
constant
• V can be measure with 0.02% accuracy
• Volume definition
Problems:
–
–
–
–
Handling of the probe
Separate mass and volume measurement
Mass measured by balance
No isolated inclusions are accounted
• Holographic method
• 3D Volume
• Time and personal intensive
• Mass ?
Tamari (2004)
Density
• Holographic method (Volume)
– (Borrmann, Raupach, Vössing 2006)
• 10µm – O( mm ) Particle Size
• +- 25 µm acc. Particle distance
– HOLODEC(Fugal and Shaw, 2009),
– HOLIMO (Amsler, Stezer, 2009)
In line holography
Raupach,2006
In line holography
• Interpreting cross-view images
• Volume estimation possible
• Operator intensive business!
Raupach,2009
In line holography: HOLODEC
Known Problems
– Sampling artifacts (shattering, breakup by aircraft/
sensor influence (Korolev, Field)
– Sampling in large convective clouds is not possible
– Scattering instruments: Interference of non
spherical particles in size distribution
measurement (in mixed phase clouds)
– Particle imager problems
e.g. (2D vs. 3D, depth of focus,..)
– Satellite retrievals (non spherical particles issues)
optical properties
What we need / Future
• The single scattering properties of at least the predominant
particle habits must be determined, these optical
properties should serve as the basis for parameterizations
of the radiative properties of clouds / contrails/ contrail
cirrus for application to climate models and the retrieval of
cloud / contrail / contrail cirrus properties from satellite
observations
• Laboratory measurements of optical properties of ice
crystals are limited in terms of spectral coverage (scattering
phase function)
• Lack of (simultaneous) measurements of complete sets of
single scattering properties (phase function, extinction
cross section, single scattering albedo
optical properties
What we need / Future
• Solution? Theoretical determination (3D
modeling, Monte Carlo, CDA, etc.. ) of these
properties for a wide variety of ice crystal habits
and statistical based database for the different
cloud types (here many flight hours have to be
spent)
Known from medicine
• Most climate model parameterizations
refer to
natural cirrus - what about contrails / contrail
cirrus (reduction of uncertainty)
What we need / future
In situ density measurements
– Is there a way inverting “special mixing rules” using phase function
measurements (hypothetical)
– 3D Particle Imager / HOLODEC
– Piezoelectric measurement of the momentum (Andy’s idea)
– quantitative Computer tomography (QCT) ?
Used in medicine
– Dual-Energy X-ray Absorptiometry, DXA/DEXA ?
– Sonographic techniques ?
• ICE particle sampler (Martina Krämer has set up one!!) for lab
analysis
•
• Please add your suggestions
END
Let’s have a good discussion!
Appendix
• References
• Additional-slides
References
•Amsler, P., et al. (2009), Ice crystal habits from cloud chamber studies obtained by inline holographic microscopy related to depolarization measurements, Appl Optics,
48(30), 5811-5822.
•Austin, R. T., et al. (2009), Retrieval of ice cloud microphysical parameters using the
CloudSat millimeter-wave radar and temperature, J Geophys Res-Atmos, 114, -.
•Battaglia, A., et al. (2010), Multiple-scattering in radar systems: A review, Journal of
Quantitative Spectroscopy & Radiative Transfer, 111(6), 917-947.
•Breon, F.-M., et al. (2002a), Aerosol Effect on Cloud Droplet Size Monitored from
Satellite, Science, 295(5556), 834-838.
•Breon, F. M. (1998), Comment on Rayleigh-scattering calculations for the terrestrial
atmosphere, Appl Optics, 37(3), 428-429.
•Breon, F. M., and P. Goloub (1998), Cloud droplet effective radius from spaceborne
polarization measurements, Geophysical Research Letters, 25(11), 1879-1882.
•Breon, F. M., et al. (2002b), Scientific results from the POLarization and Directionality
of the Earth's Reflectances (POLDER), Earth's Atmosphere, Ocean and Surface Studies,
30(11), 2383-2386.
•Deschamps, P. Y., et al. (1994), The Polder Mission - Instrument Characteristics and
Scientific Objectives, Ieee Transactions on Geoscience and Remote Sensing, 32(3), 598615.
References
•Eichler, H., et al. (2009), Influence of ice crystal shape on retrieval of cirrus optical
thickness and effective radius: A case study, J Geophys Res-Atmos, 114, -.
•Fugal, J. P., et al. (2004), Airborne Digital Holographic System for Cloud Particle
Measurements, Appl. Opt., 43(32), 5987-5995.
•Gayet, J. F., et al. (2009), Microphysical and optical properties of Arctic mixed-phase
clouds. The 9 April 2007 case study., Atmos Chem Phys, 9(17), 6581-6595.
•Grecu, M., and W. S. Olson (2008), Precipitating snow retrievals from combined
airborne cloud radar and millimeter-wave radiometer observations, J Appl Meteorol
Clim, 47(6), 1634-1650.
•Hänel, G., and J. Thudium (1977), Mean bulk densities of samples of dry atmospheric
aerosol particles: A summary of measured data, Pure and Applied Geophysics, 115(4),
799-803.
•Heymsfield, A. J., and J. Iaquinta (2000), Cirrus crystal terminal velocities, J Atmos Sci,
57(7), 916-938.
•Heymsfield, A. J., et al. (2005), Improved radar ice water content retrieval algorithms
using coincident microphysical and radar measurements, J Appl Meteorol, 44(9), 13911412.
References
•Kawanishi, T., et al. (2003), The Advanced Microwave Scanning Radiometer for the Earth Observing
System (AMSR-E), NASDA's contribution to the EOS for global energy and water cycle studies, Ieee
Transactions on Geoscience and Remote Sensing, 41(2), 184-194.
•Kobayashi, T. (1961), The Growth of Snow Crystals at Low Supersaturations, Philosophical Magazine,
6(71), 1363-&.
•Korolev, A. (2008), NEW AIRBORNE CLOUD EXTINCTION PROBE, in International Conference on Cloud
and Precipitation (ICCP), edited, Cancun.
•Liou, K. N., and Y. Takano (1994), Light scattering by nonspherical particles: Remote sensing and climatic
implications, Atmos Res, 31(4), 271-298.
•Mishchenko, M. I., et al. (2000), Light scattering by nonspherical particles : theory, measurements and
applications, xxx, 690 p., [610] p. of plates pp., Academic, San Diego ; London.
•Murshed, M. M., et al. (2008), Natural gas hydrate investigations by synchrotron radiation X-ray cryotomographic microscopy (SRXCTM), Geophysical Research Letters, 35(23), -.
•Parol, F., et al. (2004), Review of capabilities of multi-angle and polarization cloud measurements from
POLDER, Climate Change Processes in the Stratosphere, Earth-Atmosphere-Ocean Systems, and
Oceanographic Processes from Satellite Data, 33(7), 1080-1088.
•Petty, G. W., and W. Huang (2010), Microwave Backscatter and Extinction by Soft Ice Spheres and
Complex Snow Aggregates, J Atmos Sci, 67(3), 769-787.
•Raupach, S. M. F., et al. (2006), Digital crossed-beam holography for in situ imaging of atmospheric ice
particles, Journal of Optics a-Pure and Applied Optics, 8(9), 796-806.
•Raupach, S. M. F. (2009a), Observation of Interference Patterns in Reconstructed Digital Holograms of
Atmospheric Ice Crystals, J Atmos Ocean Tech, 26(12), 2691-2693.
References
•Raupach, S. M. F. (2009b), Cascaded adaptive-mask algorithm for twin-image removal and its
application to digital holograms of ice crystals, Appl Optics, 48(2), 287-301.
•Rottner, D., and G. Vali (1974), Snow Crystal Habit at Small Excesses of Vapor Density over Ice
Saturation, J Atmos Sci, 31(2), 560-569.
•Stephens, G. L., et al. (2008), CloudSat mission: Performance and early science after the first year of
operation, J Geophys Res-Atmos, 113(D23), -.
•Tamari, S., and A. Aguilar-Chávez (2004), Optimum design of the variable-volume gas pycnometer for
determining the volume of solid particles, Measurement Science and Technology, 15(6), 1146.
•Vortisch, H. (2002), Beobachtung von Phasenübergängen in einzeln levitierten Schwefelsäuretröpfchen
mittels Raman-Spectroskopie und elastischer Lichtstreuung, book thesis, 260 pp, Freie Universität
Berlin, Berlin.
•Wendisch, M., et al. (2005), Impact of cirrus crystal shape on solar spectral irradiance: A case study for
subtropical cirrus, J Geophys Res-Atmos, 110(D3), -.
•Wendisch, M., et al. (2007), Effects of ice crystal habit on thermal infrared radiative properties and
forcing of cirrus, J Geophys Res-Atmos, 112(D8), -.
•Westbrook, C. D. (2008), The fall speeds of sub-100 mu m ice crystals, Q J Roy Meteor Soc, 134(634),
1243-1251.
•Yue, Q., et al. (2007), Interpretation of AIRS data in thin cirrus atmospheres based on a fast radiative
transfer model, J Atmos Sci, 64(11), 3827-3842.
•Zelenyuk, A., et al. (2005), High Precision Density Measurements of Single Particles: The Density of
Metastable Phases, Aerosol Science and Technology, 39(10), 972-986.
The Afternoon or "A-Train" satellite constellation
The Afternoon or "A-Train" satellite constellation presently consists of five satellites
flying in formation around the globe (NASA's Aqua and Aura satellites and CNES'
PARASOL satellite). The CALIPSO and CloudSat satellite missions were inserted in
orbit behind Aqua in April 2006. Two additional satellites, OCO and Glory, will join
the constellation in the next few years.
Each satellite within the A-Train has unique measurement capabilities that greatly
complement each other.
A-Train: instrumentation
Satelite
Mission
Instruments
Aqua
+0 sec
Synergistic instrument package
studies global climate with an
emphasis on water in the
Earth/atmosphere system,
including its solid, liquid and
gaseous forms
AIRS/AMSU-A/HSB
AMSR-R
CERES
MODIS
CloudSat
+0.5 – 2 min
Cloud Profiling Radar (CPR) will
allow for most detailed study of
clouds to date and should better
characterize the role clouds play in
regulating the Earth's climate.
CPR
CALIPSO
+2 min 15 sec
Observations from spaceborne
lidar, combined with passive
imagery, will lead to improved
understanding of the role aerosols
and clouds play in regulating the
Earth's climate, in particular, how
the two interact with one another.
CALIOP
IIR
WFC
http://www-calipso.larc.nasa.gov/about/atrain.php
A-Train: instrumentation
Satelite
Mission
Instru
ments
PARASOL
+3 min 15 sec
Polarized light measurements will
allow better characterization of
clouds and aerosols in the Earth's
atmosphere,in particular,
distinguishing natural and
manmade aerosols.
POLDER
Aura
+ 15 min
Synergistic payload will study
atmospheric chemistry, focusing on
the horizontal and vertical
distribution of key atmospheric
pollutants and greenhouse gases
and how these distributions evolve
and change with time.
HIRDLS
MLS
OMI
TES
FUTURE:
•OCO
•GLORY
CO2 column
3 spectrometer
Aerosol prop.; BC; solar irradiance
APS, TIM, Cloud Camera
http://www-calipso.larc.nasa.gov/about/atrain.php
Current state of the art
Particle Size and Shape Determination
• Remote sensing (Liou et al. 2000)
– Bidirectional reflectance
Solar radiances reflected from clouds can be used to determine their composition and
structure (cloud optical depth),
– Linear polarization of reflected sunlight
interpretation of the polarization of scattered sunlight using scattering data of
nonspherical ice crystals
– Lidar backscattering depolarization
used to differentiate between ice and water clouds. Nonspherical particles will
depolarize incident polarized light in backscattering direction (spherical particles will do
not) (circular (Hu et al) / linear pol.)
– Information content of 1.38 µm and thermal infrared spectra
Water vapor exhibits a number of absorption bands in the solar spectrum. Bidirectional
reflectance at the top of the atmosphere in these bands will contain information of highlevel clouds. Specially at the 1.38µm band for cirrus clouds
– Solar albedo
Reflection (broad-band solar albedo) of solar radiation by clouds determines the amount
of solar energy absorbed within the atmosphere and by the surface. Information on ice
water path (IWP)
• LIDAR
• RADAR