title 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

title Outline 1. overview of capabilities 2. technical challenges 3. validation 4. science applications

CALIPSO Adds the 3rd Dimension to MODIS Observations

Aug 17 Aug 18 Aug 19 Aug 20 Aug 21 Aug 22 Aug 23 Aug 25 Aug 28 5 km Major Saharan Dust Transport Event: Aug (courtesy of Dave Winker, P.I.)

Part 1 Capabilities

aerosol profiles, cloud tops thick clouds drizzle polarization, multi-angle CERES: TOA fluxes MODIS: cloud r e,  AMSR: LWP O 2 A-band The atrain Cloud ice/water mass CloudSat MLS AMSR Cloud microphysicsMODIS CloudSat PARASOL PrecipitationCloudSat AMSR Aerosol opticsCALIPSO MODIS PARASOL OMI Cloud opticsCALIPSO, MODIS, and PARASOL ChemistryTES, MLS, OMI Radiative FluxesCERES composition, chemistry, dynamics

705 km, sun-synchronous orbit Three co-aligned instruments: CALIOP: polarization lidar IIR: Imaging IR radiometer WFC: Wide-Field Camera CALIPSO: a NASA-CNES collaboration Launch: 28 April 2006

CALIOP Imaging Infrared Radiometer (IIR) Wide-Field Camera (WFC) Payload Specifications Wide Field Camera Imaging Infrared Radiometer Lidar Transmitter

CALIPSO 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 2 Technical Challenges

CALIOP and GLAS Trends (courtesy of Dave Winker, NASA Langley)

Lidar Calibration Calibration: ║ – normalization of molecular return night, clean upper stratosphere ┴ – relative to 532 ║ using on-board cal H/W – 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 Effect Level 1 Attenuated Backscatter Coefficients532 nm Calibration Coefficients 2 August 2006 (courtesy of Mark Vaughan, NASA Langley)

Proposal: A Revised Calibration Procedure NIGHTNIGHTDAY Interpolation between end-points of successive night segments Polynomial approximation (courtesy of Mark Vaughan, NASA Langley)

CALIPSO obs. of strat. aerosols assessment Interpolation between end-points of successive night segments Polynomial approximation Thomason, Pitts, and Winker (2007)

Altitude Error Speed of light (in retrieval algorithm): –Old value: c = 3.00E8 m/sec –New value: c = E8 m/sec (courtesy of Bill Hunt, NASA Langley) ?

Part 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 dateoffset 13JulTBD 26JulTBD 28JulTBD 30Jul611 m 31Jul566 m 02Aug1251 m 03Aug1317 m 08Aug61 m 10Aug170 m 11Aug498 m 12Aug36 m 13Aug1716 m 14AugTBD CPL CRS 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 apples both are above the atmosphere --> see the entire column both have dual wavelength and depolarization Differences: repetition rate: vertical resolution: platform speed: detection: footprint at surface: Resulting caveats: imperfect collocation --> instruments see different scenes advection of atmosphere --> true coincidence is instantaneous CPL -vs- CALIPSO: the similarities and differences CPL 5 kHz 30 m ~200 m/s photon counting 2 m dia. CALIPSO Hz 60 m ~7500 m/s analog 88 m dia. (courtesy of Matt McGill, NASA GSFC)

11Aug06: 1064 nm Calibrated Attenuated Backscatter Altitude (km) latitude km -1 sr Altitude (km) km -1 sr -1 Coincident at 08:00:00 UTC ( , ) (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 Altitude (km) attenuated backscatter (km -1 sr -1 ) 532 nm Altitude (km) attenuated backscatter (km -1 sr -1 ) blue = CPL black = CALIPSO blue = CPL black = 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 4 Science Applications

Combining 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 DustSmoke Aerosol Type Discrimination

Using Water Clouds as a Lidar Target (1. Hu et al, 2006, Optics Letters; 2. Hu et al, 2006, 23 rd ILRC; ) Gustav Mie 1. Lidar ratio, Sc, and single scattering property can be accurately computed from Mie theory 2. Lidar multiple scattering can be well characterized through depolarization measurements Similar to molecules, water clouds are well-understood objects  : depolarization ratio (courtesy of Y. Hu, NASA Langley)

Using Water Clouds as a Lidar Target (courtesy of Y. Hu, NASA Langley)

Verifying the simple relation between multiple scattering and depolarization Using Water Clouds as a Lidar Target (courtesy of Y. Hu, NASA Langley)

Optical Depth of Aerosol above cloud Aerosol Layer Cloud Using Water Clouds as a Lidar Target (courtesy of Y. Hu, NASA Langley)

In-situ Measurement Opportunity: 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/m2 all-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) (courtesy of Michael Schulz)

My 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) 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 intrinsically difficult 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).

Method Method

SHADOZ data (temperature, O 3, H 2 O) SHADOZ data (temperature, O 3, H 2 O) 12 stations within -20S—20N from 1998 to 2005): 2244 profiles Thompson et al. (2003)

Temperature & O 3 profiles: Raw data

Temperature & O 3 & H 2 O profiles above ~10 mb UKMO radiosonde HALOE radiosonde Weight function: W=1- (lnP base -lnP)/(lnP base -lnP top ) Var transition (P) =(1-W)*climate(P)+W*Var radiosonde (P) ~ 3km Climate: UKMO/HALOE P top P base P top ~ 3km TO3O3

Radiative heating rate profile (total mean) T ρ θ Q rad T ρ θ ~17km

Identify 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. Fu et al. (2007)

Validation with CALIPSO Lidar Cloud Obs. Fu et al. (2007)

Validation with CALIPSO Lidar Cloud Obs. Fu et al. (2007)

Dust Storm from CALIPSO over China