CALIPSO Ocean Products: Progress Advisors: Mike Behrenfeld and Chuck McClain Ball Aerospace: Carl Weimer CNES: Jacques Pelon NASA LaRC: Yongxiang Hu, Sharon.

Slides:



Advertisements
Similar presentations
Calipso (LIDAR in space) Data during DODO Flight B237 over Ocean off Mauritanian Coast 22 nd August 2006.
Advertisements

Robin Hogan, Richard Allan, Nicky Chalmers, Thorwald Stein, Julien Delanoë University of Reading How accurate are the radiative properties of ice clouds.
ESTO Advanced Component Technology 11/17/03 Laser Sounder for Remotely Measuring Atmospheric CO 2 Concentrations GSFC CO 2 Science and Sounder.
Exploiting multiple scattering in CALIPSO measurements to retrieve liquid cloud properties Nicola Pounder, Robin Hogan, Lee Hawkness-Smith, Andrew Barrett.
Satellite Ocean Color Overview Dave Siegel – UC Santa Barbara With help from Chuck McClain, Mike Behrenfeld, Bryan Franz, Jim Yoder, David Antoine, Gene.
Calibration Scenarios for PICASSO-CENA J. A. REAGAN, X. WANG, H. FANG University of Arizona, ECE Dept., Bldg. 104, Tucson, AZ MARY T. OSBORN SAIC,
Earth System Science Teachers of the Deaf Workshop, August 2004 S.O.A.R. High Earth Observing Satellites.
1 An initial CALIPSO cloud climatology ISCCP Anniversary, July 2008, New York Dave Winker NASA LaRC.
EarthCARE: The next step forward in global measurements of clouds, aerosols, precipitation & radiation Robin Hogan ECMWF & University of Reading With input.
Near Surface Soil Moisture Estimating using Satellite Data Researcher: Dleen Al- Shrafany Supervisors : Dr.Dawei Han Dr.Miguel Rico-Ramirez.
A 21 F A 21 F Parameterization of Aerosol and Cirrus Cloud Effects on Reflected Sunlight Spectra Measured From Space: Application of the.
Profiling Clouds with Satellite Imager Data and Potential Applications William L. Smith Jr. 1, Douglas A. Spangenberg 2, Cecilia Fleeger 2, Patrick Minnis.
1 Satellite Remote Sensing of Particulate Matter Air Quality ARSET Applied Remote Sensing Education and Training A project of NASA Applied Sciences Pawan.
Direct Radiative Effect of aerosols over clouds and clear skies determined using CALIPSO and the A-Train Robert Wood with Duli Chand, Tad Anderson, Bob.
1 CALIPSO Status and Plans Dave Winker Winds Working Group, June 2009, Wintergreen, VA.
Remote Sensing Allie Marquardt Collow Met Analysis – December 3, 2012.
Metr 415/715 Monday May Today’s Agenda 1.Basics of LIDAR - Ground based LIDAR (pointing up) - Air borne LIDAR (pointing down) - Space borne LIDAR.
The IOCCG Atmospheric Correction Working Group Status Report The Eighth IOCCG Committee Meeting Department of Animal Biology and Genetics University.
1 Satellite Remote Sensing of Particulate Matter Air Quality ARSET Applied Remote Sensing Education and Training A project of NASA Applied Sciences Pawan.
EARLINET and Satellites: Partners for Aerosol Observations Matthias Wiegner Universität München Meteorologisches Institut (Satellites: spaceborne passive.
Applications and Limitations of Satellite Data Professor Ming-Dah Chou January 3, 2005 Department of Atmospheric Sciences National Taiwan University.
B. Gentry/GSFCSLWG 06/29/05 Scaling Ground-Based Molecular Direct Detection Doppler Lidar Measurements to Space Using Wind Profile Measurements from GLOW.
Problems and Future Directions in Remote Sensing of the Ocean and Troposphere Dahai Jeong AMP.
Summer Institute in Earth Sciences 2009 Comparison of GEOS-5 Model to MPLNET Aerosol Data Bryon J. Baumstarck Departments of Physics, Computer Science,
Introduction Invisible clouds in this study mean super-thin clouds which cannot be detected by MODIS but are classified as clouds by CALIPSO. These sub-visual.
1 GOES-R AWG Product Validation Tool Development Aviation Application Team – Volcanic Ash Mike Pavolonis (STAR)
1 GOES-R AWG Product Validation Tool Development Aviation Application Team – Volcanic Ash Mike Pavolonis (STAR)
Dr. North Larsen, Lockheed Martin IS&S Dr. Knut Stamnes, Stevens Institute Technology Use of Shadows to Retrieve Water Vapor in Hazy Atmospheres Dr. North.
Estimation of Cloud and Precipitation From Warm Clouds in Support of the ABI: A Pre-launch Study with A-Train Zhanqing Li, R. Chen, R. Kuligowski, R. Ferraro,
1 CALIPSO: Validation activities and requirements Dave Winker NASA LaRC GALION, WMO Geneva, September 2010.
Status of CFLOS study using CALIPSO data G. D. Emmitt, D. Winker and S. Greco WG SBLW Destin, FL January 27-30, 2009.
Ocean subsurface studies from space-based lidar measurements Xiaomei Lu, 1 Yongxiang Hu, 2 1 Science Systems and Applications, Inc. (SSAI), Hampton, Virginia.
WATER VAPOR RETRIEVAL OVER CLOUD COVER AREA ON LAND Dabin Ji, Jiancheng Shi, Shenglei Zhang Institute for Remote Sensing Applications Chinese Academy of.
2006 OCRT Meeting, Providence Assessment of River Margin Air-Sea CO 2 Fluxes Steven E. Lohrenz, Wei-Jun Cai, Xiaogang Chen, Merritt Tuel, and Feizhou Chen.
The Second TEMPO Science Team Meeting Physical Basis of the Near-UV Aerosol Algorithm Omar Torres NASA Goddard Space Flight Center Atmospheric Chemistry.
Characterization of Aerosols using Airborne Lidar, MODIS, and GOCART Data during the TRACE-P (2001) Mission Rich Ferrare 1, Ed Browell 1, Syed Ismail 1,
Group proposal Aerosol, Cloud, and Climate ( EAS 8802) April 24 th, 2006 Does Asian dust play a role as CCN? Gill-Ran Jeong, Lance Giles, Matthew Widlansky.
AT737 Aerosols.
Use of Solar Reflectance Hyperspectral Data for Cloud Base Retrieval Andrew Heidinger, NOAA/NESDIS/ORA Washington D.C, USA Outline " Physical basis for.
Radiative transfer in the thermal infrared and the surface source term
Bryan A. Baum, Richard Frey, Robert Holz Space Science and Engineering Center University of Wisconsin-Madison Paul Menzel NOAA Many other colleagues MODIS.
Jetstream 31 (J31) in INTEX-B/MILAGRO. Campaign Context: In March 2006, INTEX-B/MILAGRO studied pollution from Mexico City and regional biomass burning,
The Orbiting Carbon Observatory (OCO) Mission: Retrieval Characterisation and Error Analysis H. Bösch 1, B. Connor 2, B. Sen 1, G. C. Toon 1 1 Jet Propulsion.
Satellites Storm “Since the early 1960s, virtually all areas of the atmospheric sciences have been revolutionized by the development and application of.
Cloud property retrieval from hyperspectral IR measurements Jun Li, Peng Zhang, Chian-Yi Liu, Xuebao Wu and CIMSS colleagues Cooperative Institute for.
Direct aerosol radiative effects based on combined A-Train observations Jens Redemann, Y. Shinozuka, J. Livingston, M. Vaughan, P. Russell, M.Kacenelenbogen,
Ball Aerospace & Technologies Corporation -
1 CALIPSO VALIDATION and DATA QUALITY IMPROVEMENT EECLAT T0, J. Pelon.
1 Atmospheric Radiation – Lecture 13 PHY Lecture 13 Remote sensing using emitted IR radiation.
Cloudnet meeting Oct Martial Haeffelin SIRTA Cloud and Radiation Observatory M. Haeffelin, A. Armstrong, L. Barthès, O. Bock, C. Boitel, D.
Laser Telescope Laser wavelength532 nm Laser energy per pulse50-80 uJ Laser pulse repetition freq.>100 Hz Detector Photoncounting Scattered Light Ocean.
1 Recent advances in CALIPSO cloud retrievals: Progress over the last 7 years Looking ahead to the next 30 ISCCP at 30: City College of New York, 23 April.
TS 15 The Great Salt Lake System ASLO 2005 Aquatic Sciences Meeting Climatology and Variability of Satellite-derived Temperature of the Great Salt Lake.
Global Characterization of X CO2 as Observed by the OCO (Orbiting Carbon Observatory) Instrument H. Boesch 1, B. Connor 2, B. Sen 1,3, G. C. Toon 1, C.
Precipitation Effects on Turbulence and Salinity Dilution in the Near Surface Ocean Christopher J. Zappa Lamont-Doherty Earth Observatory, Columbia University,
Ocean Sciences The oceans cover 3/4 of the Earth’s surface. They provide the thermal memory for the global climate system, and are a major reservoir of.
The study of cloud and aerosol properties during CalNex using newly developed spectral methods Patrick J. McBride, Samuel LeBlanc, K. Sebastian Schmidt,
What Are the Implications of Optical Closure Using Measurements from the Two Column Aerosol Project? J.D. Fast 1, L.K. Berg 1, E. Kassianov 1, D. Chand.
Motivation: Help satellite studies of aerosol-cloud interactions Aerosol remote sensing near clouds is challenging Excluding areas near-cloud risks biases.
John E. Yorks, M. McGill, S. Rodier, M. Vaughan, Y. Hu, D. Hlavka African Dust and Smoke Influences on Radiative Effects in the Tropical Atlantic Using.
Aerosol properties in a cloudy world (from MODIS and CALIOP) Alexander Marshak (GSFC) Bob Cahalan (GSFC), Tamas Varnai (UMBC), Guoyong Wen, Weidong Yang.
Visible vicarious calibration using RTM
Vertically resolved CALIPSO-CloudSat aerosol extinction coefficient in the marine boundary layer and its co-variability with MODIS cloud retrievals David.
Extinction measurements
NPOESS Airborne Sounder Testbed (NAST)
Polarization Effects on Column CO2 Retrievals from Non-Nadir Satellite Measurements in the Short-Wave Infrared Vijay Natraj1, Hartmut Bösch2, Robert J.D.
Polarization Effects on Column CO2 Retrievals from Non-Nadir Satellite Measurements in the Short-Wave Infrared Vijay Natraj1, Hartmut Bösch2, Robert J.D.
Mark Schoeberl NASA/GSFC
Mike Pavolonis (NOAA/NESDIS/STAR)
Snowfall changes and climate sensitivity
Presentation transcript:

CALIPSO Ocean Products: Progress Advisors: Mike Behrenfeld and Chuck McClain Ball Aerospace: Carl Weimer CNES: Jacques Pelon NASA LaRC: Yongxiang Hu, Sharon Rodier, Chip Trepte, Bill Hunt NASA NRC Postdoc Program: Pengwang Zhai and Damien Josset Stevens Institute of Tech: Knut Stamnes ODU: Richard Zimmerman and Victoria Hill SAIC: Jim Koziana Bigelow: William Balch HSRL and RSP instruments: Chris Hostetler and Brian Cairns Supported by NASA HQ ocean biogeochemistry program and radiation science program Paula and Hal: Thanks!

Outline CALIPSO lidar measurements: introduction Sub-surface particulate backscatter from cross-polarization profiling: progress Highlight: a self-calibration method is developed for CALIPSO ocean subsurface lidar backscatter product (good for future trend analysis) Air-sea gas exchange velocity from lidar measurements of ocean surface mean square slope Other studies that supports ocean color program 1.aerosol optical depth estimates without microphysics assumption 2.identification of smoke aerosols 3.modeling and sensitivity studies with polarimeter measurements

CALIPSO and A-Train CALIPSO: Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation CALIPSO is 75 seconds behind Aqua, with MODIS, CERES, AMSR, …, onboard

CALIPSO Payload Three Near Nadir Viewing Instruments CALIOP Cloud-Aerosol Lidar with Orthogonal Polarization 2 wavelength polarization sensitive lidar: 1064 nm, 532 nm (parallel and perpendicular) Wide Field Camera (WFC) High-resolution image (125m resolution) Vertical profiles of atmosphere Lidar Imaging Infrared Radiometer (IIR) High-resolution image (swath product) IIR WFC CALIOP Laser Transmitter CALIPSO Payload CALIOP Receiver Telescope 1 meter

Altitude Region 0.3 degree

Ocean Study Using Lidar in Space 532nm Cross-Polarization: Particulate Backscatter 1064nm: Air-sea gas transfer velocity; wind speed 532nm Co-polarization: aerosol correction

CALIPSO cross polarization channel ocean subsurface integrated backscatter (Lc) Lc = (total lidar backscatter in water) / ( atmospheric two way transmittance) total lidar backscatter in water = sum [lidar backscatter] (unit: 1/sr) Atmospheric two way transmittance = exp (- 2* atmospheric optical depth)

Progress in lidar subsurface backscatter Study Self-calibration (solving daytime calibration issue): the new ocean subsurface CALIPSO backscatter is self-calibrated Lc = subsurface particulate backscatter / atmospheric twoway transmittance = theoretical Cox-Munk Reflectance (from CloudSat or AMSR-E) * [calib * uncalib subsurface signal] / [calib * uncalib ocean surface signal] Solving issues related to instrumentation new crosstalk correction (co-polarization vs cross polarization); cross-pol low-gain change from Feb 2007; … Monte Carlo simulation of multiple scattering and its impact on the signal: significant contribution from multiple scatter; more going studies; needs help on characterizing particulate scattering phase matrix

CALIPSO sub-surface backscatter: 2007 and 2008

CALIPSO sub-surface backscatter: Night vs Day No big problem with daytime: Self calibration worked!

CALIPSO sub-surface backscatter: MarAprMay vs SepOctNov

CALIPSO sub-surface backscatter: DecJanFeb vs JunJulAug

7m/s vs 11 m/s thresholds: impact of bubbles V<7 m/s V<11 m/s

7m/s vs 11 m/s thresholds: impact of bubbles V<7 m/s V<11 m/s

Link between B bp and lidar cross-polarization particulate backscatter  Lc  Lc= (1+m)B bp /(2  beam )*[1-exp(-2  beam  H/(1+m))] * f(particle shape) *exp(-2  beam H 0 /(1+m))  beam : beam attenuation m: multiple scatter contribution Integrated lidar subsurface backscatter [Sum(  Lc)] is proportional to (1+m)B bp /  beam When (1+m)*(beam attenuation ) of a size bin is >>1, 1-exp(-2  beam  H/(1+m))]=1 Backscatter of individual vertical bin,  Lc, of the profile is proportional to B bp when  H is small and 1-exp(-2  beam  H/(1+m))= 2  beam  H/(1+m) Effective depth and backscatter profile product (under development): extra information help separate B bp and  beam

Validating Aerosol Correction using Ocean Surface Co-polarization Component (HSRL: Sept. 04, 2007; from Chris Hostetler, John Hair, and others of the NASA LaRC HSRL group. Thanks!)

Comparison with MODIS (January 2007) 532nm optical depth from CALIPSO/AMSR 1064nm optical depth from CALIPSO/AMSR 550nm optical depth from MODIS

Identifying Absorbing Aerosols Using Lidar Ratio (e.g. smoke: around 70) from Ocean Surface Backscatter Effective Lidar Ratio = Beam Attenuation / Backscatter = [1-exp(-2  )]/(2  )]

Application of lidar measurements: gas transfer velocity Ocean and the missing carbon sink From Woods Hole Reserch Center Website Atmospheric increase (3.2 PgC/yr) = Emissions from fossil fuels (6.3) + Net emissions from changes in land use (2.2) - Oceanic uptake (2.4) - Missing carbon sink (2.9) Combined with errors in partial pressure, the uncertainty of a factor of two in air-sea gas transfer velocity can lead to unacceptable error in global ocean flux of CO 2 (Wallace, 1995)

CO 2 Uptake = Air-sea Gas transfer velocity k x (660/Sc) n x solubility x  (Pco 2 ) Application of lidar measurements: gas transfer velocity Relation between carbon uptake and gas transfer velocitty

Air-sea gas transfer – wind speed relation : A source of uncertainty in Ocean Carbon Uptake R.A. Feely, C.L. Sabine, T. Takahashi, and R. Wanninkhof, 2001: Uptake and Storage of Carbon Dioxide in the Ocean: The Global CO2 Survey, Oceanography, 14/4, Uptake and Storage of Carbon Dioxide in the Ocean: The Global CO2 Survey

CALIPSO mean square slope improves gas transfer velocity ( Jahne et al 1984; Hara et al 1995; Bock et al 1999; Frew et al 2004, …) wave slope variance correlates with gas transfer velocity better than wind speed and wind stress Frew et al, 2004, JGR

Mean square slope is directly measured by CALIPSO: ocean surface backscatter Ocean Surface Backscatter  = C* [ sec 4  / exp(- 0.5 tan 2  / ]  C / At 532nm and 1064nm, Fresnel reflection is valid for all surface waves

8/17/2015 Carbon Uptake Comparison: F(Wind) (AMSR) vs F( ) (CALIPSO)

Combined Active/passive: Modeling and Sensitivity Studies for ACE 1.Fast and accurate coupled ocean-atmospheric model for polarized radiative transfer 2.Sensitivity studies: how can polarization help 3.Objectives: using polarization measurements to help improve lidar data analysis

Model Description A vector radiative transfer model has been developed for a coupled atmosphere ocean system. It is based on successive order of scattering method. It converges fast for optically thin or absorptive media. Various state of art techniques have been employed to enhance performance. A reference can be found at: Peng-Wang Zhai, Yongxiang Hu, Charles R. Trepte, and Patricia L. Lucker, "A vector radiative transfer model for coupled atmosphere and ocean systems based on successive order of scattering method," Opt. Express 17, (2009)

Sensitivity of Radiance and Degree of Polarization to Layer Depth  PP P  P  I1, P1: plankton layer at 10 m below surface I2, P2: 50 m below surface Unit of I1 and I2: Wm -2  m -1

Radiance and Degree of Polarization Sensitivity to Size  PP P  P1  Case 1: effective size 1  m Case 3: effective size 30  m Unit of I1 and I2: Wm -2  m -1

Summary and Discussion Highlight: a self-calibration method is developed for CALIPSO ocean subsurface lidar backscatter product (good for trend analysis) CALIPSO sub-surface integrated lidar backscatter (cross- polarization) is ready to release; depolarization ratio and vertical profiling of backscatter are still work in progress Preliminary study is done on the air-sea gas transfer velocity product ACE proof of concept: modeling and sensitivity studies of combined lidar/polarimeter for ocean color

HSRL lidar and RSP polarimeter flights over routes where in situ measurements are available Purpose: Learning from the success of ocean color vacarious calibration, we want to examine the potential of using several well defined targets (e.g. ocean surface) as the “moby” equivalent for lidar/radar and polarimeter How: A few aircraft measurement flights supported by CALIPSO over various targets, in situ measurements of optical properties, and polarized radiative transfer modeling to assess how well we can understand those targets Preliminary plan: flights this summer/fall near NASA Stennis and/or Langley (exact time and location to be decided: need your suggestions and collaborations on in situ measurements ) My Phone: