title calipso* and the a-train: spaceborne lidar for global aerosol/cloud/climate assessment *cloud...

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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