title calipso* and the a-train: spaceborne lidar for global aerosol/cloud/climate assessment *cloud...
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CALIPSO Adds the 3rd Dimension to MODIS ObservationsTRANSCRIPT
titletitle
CALIPSO* and the A-Train: Spaceborne lidar for global
aerosol/cloud/climate assessment
*Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observations
Qiang Fu Department of Atmospheric Sciences
University of Washington
titletitle
Outline1. overview of capabilities
2. technical challenges
3. validation
4. science applications
CALIPSO Adds the 3rd Dimension to MODIS Observations
Aug 17Aug 18Aug 19
Aug 20Aug 21Aug 22Aug 23
Aug 25Aug 28
5 km
Major Saharan Dust Transport Major Saharan Dust Transport Event: Aug 17-28Event: Aug 17-28
(courtesy of Dave Winker, P.I.)
Part 1Part 1
Capabilities
aerosol profiles,cloud tops
thick cloudsdrizzlepolarization,
multi-angleCERES: TOA fluxesMODIS: cloud re, AMSR: LWP O2 A-band
The atrain
Cloud ice/water mass
CloudSatMLSAMSR
Cloud microphysics MODISCloudSatPARASOL
Precipitation CloudSatAMSR
Aerosol optics CALIPSOMODISPARASOLOMI
Cloud optics CALIPSO, MODIS, andPARASOL
Chemistry TES, MLS, OMI
Radiative Fluxes CERES
composition,chemistry, dynamics
705 km, sun-synchronous orbitThree co-aligned instruments:• CALIOP: polarization lidar• IIR: Imaging IR radiometer• WFC: Wide-Field Camera
CALIPSO: a NASA-CNES collaboration
Launch: 28 April 2006
Laser Nd: YAG, 2x110 mJ Wavelength 532 nm, 1064 nm Repetition rate 20.16 Hz Receiver telescope 1.0 m diameter Polarization 532 ⎯ ⎯ and Fooprin/FOV 100 m / 130 rad Verical resoluion 30 - 60 m Horizonal resoluion 333 m Lin. dynamic range 22 bis
Wavelength 645 nm Spectral bandwidth 50 nm IFOV / Swath 125 m / 61 km
Wavelength 8.65, 10.6,12.05 mm Specral resoluion 0.6 -1.0 mm IFOV / Swah 1 km / 64 km NETD @ 210K 0.3 K Calibraion ±1 K
CALIOP
Imaging Infrared Radiometer (IIR)
Wide-Field Camera (WFC)
Payload SpecificationsPayload Specifications
Wide Field Camera
Imaging Infrared
RadiometerLidar Transmitter
CALIPSO Science ObjectivesCALIPSO Science Objectives
Improve understanding aerosol/cloud forcings and feedbacks by providing:
– aerosol profiles over all surfaces, day and night
– cloud profiles of thin clouds and multi-layer cloud structures
– layer identification:• cloud water phase• cirrus particle size• aerosol type
– test, refine, and complement other A-Train instruments
Aerosol Subtypes
Cloud-Aerosol Mask
Cloud Phase
CALIPSO Data Products
Level 1: 532 nm total atten. backscatter
(courtesy of Dave Winker, P.I.)
Part 2Part 2
Technical Challenges
CALIOP and GLAS TrendsCALIOP and GLAS Trends(courtesy of Dave Winker, NASA
Langley)
Lidar CalibrationLidar Calibration
Etalon
532 ||
PolarizaionBeam Splier
F|| + F⊥
1064
532 ⊥
Inerference Filer
LaserBackscaer
fromClouds/Aerosols
Deecors andElecronics
Depolarizer
(Calibrae)
Transmier
Calibration: - 532║ – normalization of molecular return
night, clean upper stratosphere - 532┴ – relative to 532║ using on-board cal H/W - 1064 – relative to 532║ using cirrus backscatter
Analog detection– 532 nm: PMT’s– 1064 nm: APD
22-bit dynamic range
Active boresight adjustment
Calibration Error: Cause and Calibration Error: Cause and EffectEffect
Level 1 Attenuated Backscatter Coefficients 532 nm Calibration Coefficients
2 August 20062 August 2006
(courtesy of Mark Vaughan, NASA Langley)
Proposal: A Revised Calibration Proposal: A Revised Calibration ProcedureProcedure
Time4.4
4.6
4.8
5.0
5.2
5.4
5.6
5.8
6.0
6.2
6.4
6.6NIGHTNIGHT NIGHTNIGHTDAYDAY
Interpolation between end-points of
successive night segments
Polynomial approximation
(courtesy of Mark Vaughan, NASA Langley)
CALIPSO obs. of strat. aerosols CALIPSO obs. of strat. aerosols assessmentassessment
Interpolation between end-points of
successive night segments
Polynomial approximation
Thomason, Pitts, and Winker (2007)
Altitude ErrorAltitude Error
Speed of light (in retrieval algorithm):– Old value: c = 3.00E8 m/sec– New value: c = 2.99792458E8 m/sec
(courtesy of Bill Hunt, NASA Langley)
?
Part 3Part 3
Validation
Ground-based lidar stations(courtesy of Anne Garnier,
Laplace Institute)
Ground-based lidar stations(courtesy of Anne Garnier,
Laplace Institute)
The CC-VEX Field Campaign
date offset13Jul TBD26Jul TBD28Jul TBD30Jul 611 m31Jul 566 m02Aug 1251 m03Aug 1317 m08Aug 61 m10Aug 170 m11Aug 498 m12Aug 36 m13Aug 1716 m14Aug TBD
CPLCRS VIS
MAS
• Dedicated to CALIPSO-CloudSat validation.• July 26 - Aug 14, based in Warner-Robbins, GA.• Total of 13 underflights, with varying scenes.• Payload: Cloud Physics Lidar (CPL),
Cloud Radar System (CRS), MODIS Airborne Simulator (MAS), Visible camera (VIS).
(courtesy of Matt McGill, NASA GSFC)
Similarities:both are backscatter lidar --> use apples to validate applesboth are above the atmosphere --> see the entire columnboth have dual wavelength and depolarization
Differences:
repetition rate:vertical resolution:platform speed:detection:footprint at surface:
Resulting caveats:imperfect collocation --> instruments see different scenesadvection of atmosphere --> true coincidence is instantaneous
CPL -vs- CALIPSO: the similarities and differences
CPL5 kHz30 m
~200 m/sphoton counting
2 m dia.
CALIPSO20.25 Hz
60 m~7500 m/s
analog88 m dia.
(courtesy of Matt McGill, NASA GSFC)
11Aug06: 1064 nm Calibrated Attenuated Backscatter
5
10
15
0
Alti
tude
(km
)
latitude37 38 3936
37 38 3936 km-1 sr-1
5
10
15
0
Alti
tude
(km
)
10-3
10-1
10-2
10-3
10-1
10-2
km-1 sr-1
Coincident at 08:00:00 UTC(37.2423, -87.8275)
(courtesy of Matt McGill, NASA GSFC)
CPL is 25 second average (5 km). CALIPSO data is 5 km average.
11Aug06: Calibrated Attenuated Backscatter Comparison
1064 nm
Alti
tude
(km
)
5
0
15
10
20
10-3 10-110-210-5 10-4
attenuated backscatter (km-1 sr-1)
532 nm
Alti
tude
(km
)
5
0
15
10
20
10-3 10-110-210-5 10-4
attenuated backscatter (km-1 sr-1)
blue = CPLblack = CALIPSO
blue = CPLblack = CALIPSO
(courtesy of Matt McGill, NASA GSFC)
Airborne High Spectral Resolution Lidar (HSRL)
(courtesy of Chris Hostetler, NASA Langley)
Airborne High Spectral Resolution Lidar (HSRL) (courtesy of Chris Hostetler,
NASA Langley)
Airborne High Spectral Resolution Lidar (HSRL)
(courtesy of Chris Hostetler, NASA Langley)
Part 4Part 4
Science Applications
Combining CALIPSO and CloudsatCombining CALIPSO and Cloudsat
Japan
Clouds link the radiation budget and the hydrologic cycle
CALIPSO(532 nm)
CloudSat
(courtesy of Dave Winker, P.I.)
CloudSat (July-Aug)
Zonally averaged distribution of cloudiness
CALIPSO and CloudSat together provide the first reliable view of the full vertical structure of clouds over the globe (especially at night)
Combining CALIPSO and CloudSat
CALIPSO (July)
(courtesy of Dave Winker, P.I.)
532 nm
1064 nm
Depolarization ratio
Dust Smoke
Aerosol Type Discrimination
2 2single
1( ) (( )
)1
single scatteringmultiple single scat
I Iter Iin Ig
f dd
⊥
⊥
−+
=−
=+ +
= P
P
Using Water Clouds as a Lidar Using Water Clouds as a Lidar Target Target
19.1 0.2cS = ±
(1. Hu et al, 2006, Optics Letters; 2. Hu et al, 2006, 23rd ILRC; )
Gustav Mie1. Lidar ratio, Sc, and single scattering property can beaccurately computed from Mie theory
2. Lidar multiple scattering can be well characterized through depolarization measurements
Similar to molecules, water clouds are well-understood objects
d: depolarization ratio
(courtesy of Y. Hu, NASA Langley)
2
singl
_single
e
19
can be measured
2
water cloud
cloud topwater cloud
Sc water cloud lidar ratio
f
Tf
Sc
b
b
= ≈
=
22_
single 2
aerosolcloud top
water cloud
T ef
Sc
b−
=
aerosol
icecloud
Using Water Clouds as a Lidar Using Water Clouds as a Lidar Target Target
(courtesy of Y. Hu, NASA Langley)
Verifying the simple relation between multiple scattering and depolarization
Using Water Clouds as a Lidar Using Water Clouds as a Lidar Target Target
(courtesy of Y. Hu, NASA Langley)
Optical Depthof Aerosol
above cloud
Aerosol Layer
Cloud
Using Water Clouds as a Lidar Using Water Clouds as a Lidar Target Target
(courtesy of Y. Hu, NASA Langley)
In-situ Measurement Opportunity:In-situ Measurement Opportunity:Above-cloud single scatter albedo (SSA)Above-cloud single scatter albedo (SSA)
Chemical Transport Model estimates (AEROCOM): - cloudy-sky DCF (direct climate forcing) virtually eliminates clear-sky DCF
clear-sky: -0.7 W/m2all-sky: -0.2 W/m2
- effect is entirely due to absorbing aerosol above low clouds - effect varies wildly among the different models (see figure) - there is essentially no empirical constraint!
Prospects for empirical constraint: - satellite lidar (GLAS and now CALIPSO) will yield AOD above cloud - this information will be close to meaningless without knowing SSA - only in-situ methods can supply data on SSA
ISCCP low level cloud cover
Schulz et al. 2006, Atmos Chem Phys Disc
Aerosol forcing in cloudy skies (AEROCOM)Aerosol forcing in cloudy skies (AEROCOM)(courtesy of Michael Schulz)
My Research Interests with CALIPSOMy Research Interests with CALIPSO
CALIPSO’s capability to detect tropical thin cirrus and its vertical profile - identify the top of the TTL (Fu et al. 2007) - quantify cloud radiative forcing in the TTL - understand processes controlling TTL vertical transport and dehydration - Constrain cloud microphysics parameterizations used in both GCMs and CRMs
CALIPSO’s capability to detect aerosol vertical profiles in both clear & cloudy sky: - aerosol direct radiative forcing in cloudy sky Dust aerosol (with JP Huang at LZU) Biomass burning aerosols (with Brian Magi at GFDL/Princeton) black carbon aerosols (with Terry Nakajima at CCSR?)
Validations - thin cirrus (ARM TWP sites) - aerosol (LZU site with JP Huang)
Tropical Tropopause Layer (TTL)Tropical Tropopause Layer (TTL)
The base of TTL (~15 km):The level of zero net radiative heating rate
It is more difficult to define the top of the TTL. A useful conceptual definition is that it is the height at which the upward convective mass flux becomes small in comparison to the B-D mass flux.
Unfortunately, it is intrinsicallydifficult to diagnose the high altitude tail of the convective detrainment profile from observations (Folkins, 2006).
Fueglistaler et al. (2007)
•TTL is a transition region whose properties are intermediate between the troposphere and stratosphere, rather than a material surface (Highwood and Hoskins, 1998; Folkins et al, 1999).
MethodMethod
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radiationQz
Tw =∂∂θ
θ)/(T
TQw radiation ∂∂
=θ
θrwM =
SHADOZ data SHADOZ data (temperature, O(temperature, O33, H, H22O)O)
12 stations within -20S—20N from 1998 to 2005): 2244 profiles
http://croc.gsfc.nasa.gov/shadoz
Thompson et al. (2003)
Temperature & OTemperature & O3 3 profiles: Raw dataprofiles: Raw data
Temperature & OTemperature & O3 3 & H& H22O profiles above ~10 O profiles above ~10 mbmb
UKMO
radiosonde
HALOE
radiosonde
Weight function: W=1- (lnPbase-lnP)/(lnPbase-lnPtop)Vartransition(P) =(1-W)*climate(P)+W*Varradiosonde(P)
~3km
Climate: UKMO/HALOE
Ptop
Pbase Pbase
Ptop
~3km
T O3
Radiative heating rate profile (total Radiative heating rate profile (total mean)mean)
T ρ
θQrad
T ρ
θ Qrad
~17km
Identify the top of TTLIdentify the top of TTL
Top of TTL
Mean mass flux
We define the top of the TTL as the level at which the vertical mass flux is less than 110% of the mean mass flux between 19 and 24 km.
Validation with CALIPSO Lidar Cloud Obs.Validation with CALIPSO Lidar Cloud Obs.
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Fu et al. (2007)
Validation with CALIPSO Lidar Cloud Obs.Validation with CALIPSO Lidar Cloud Obs.
Fu et al. (2007)
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Validation with CALIPSO Lidar Cloud Obs.Validation with CALIPSO Lidar Cloud Obs.
Fu et al. (2007)
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Dust Storm from CALIPSO over ChinaDust Storm from CALIPSO over China
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Dust Storm from CALIPSO over ChinaDust Storm from CALIPSO over China
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Dust Storm from CALIPSO over ChinaDust Storm from CALIPSO over China
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