Viewed from Space Shuttle

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Retrieval of Aerosol Properties from SAGE III
Limb Scattering Measurements
Viewed from Space Shuttle
Robert Loughman, Dept. of Atmo. and Plan.
Sciences, Hampton University Didier Rault,
Climate Science Branch, NASA Langley Research
Center
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Outline

• Why measure stratospheric aerosol?
• Discuss SAGE III limb scattering (LS)
  stratospheric aerosol extinction profile
  retrieval
• Describe aerosol size distribution (ASD)
  retrieval technique
• Show comparisons to coincident SAGE II solar
  occultation (SO) aerosol data
• Conclusions and future work

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Why studystratospheric aerosol?

• Direct effect on global climate (radiative
  forcing)
• Indirect effects (on heterogenous chemical
  reactions, cloud formation)
• Improved understanding of stratospheric aerosol
  processes is needed (e.g., currently observed
  level ltlt previously postulated natural
  background level)
• Ignoring stratospheric aerosol introduces at
  least 5-10 error on LS ozone profile retrievals.
  OMPS has a 3 precision requirement.
• Several solar occultation instruments have
  retired (SAGE, POAM), so future LS instruments
  (OMPS) may be asked to play a stratospheric
  aerosol monitoring role.

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Future Applications of LS for Ozone Retrieval
The Ozone Mapper Profiler Suite (OMPS) includes a
Limb Profiler (LP) instrument to provide
stratospheric ozone profile information.
The tri-agency Integrated Program Office (NASA,
NOAA, Department of Defense) has committed to LS
to provide ozone profile retrievals, beginning in
2010. The main purpose of the current research
with SAGE III LS data is to test our knowledge of
instrument design and retrieval algorithms for
this relatively unproven technique.
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LS Aerosol Signature

• Where possible, infer Effective Lambertian
  Albedo at altitudes 35-45 km
• Then solve for Aerosol Extinction Coefficient at
  altitudes between cloud

top and 35 km,
at 6 ? 520, 600, 675, 750, 870, 1020 nm
From Rault and Taha (2005)
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Aerosol ExtinctionRetrieval Algorithm

• Ozone profile effective surface reflectivity
  are static
• Compare Id / In to Im / In to solve for aerosol
  extinction at each tangent height
• Id measured radiance data
• Im calculated model radiance with latest
  aerosol extinction profile in the atmosphere
  (updated at each iteration)
• In calculated radiance, with no aerosol in the
  atmosphere
• Initially use an assumed aerosol size
  distribution (ASD), shape (spherical), and index
  of refraction (1.448, 0) to solve for the
  extinction profile ?a (z) at each wavelength
  independently
• Then update both extinction and ASD by iteration

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LS Aerosol Sensitivity
Ratio of data to model (No aerosol)
The range of heights with significant LS aerosol
sensitivity is limited
Sensitivity functions for aerosol retrieval
Dlog(I) / Dlog(?a)
especially at the shorter wavelengths
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ASD Characterization
We seek a single parameter to characterize the
ASD, based on its moments Mk. Several authors
(Hansen and Travis, 1974 Lenoble and Brogniez,
1984 Bauman et al., 2003) suggest Ra as a
promising candidate. (Shettle, 2006) For
todays small stratospheric aerosol particles, Rv
may be even better
(area-weighted effective radius)
(volume-weighted effective radius)
For a single-mode, log-normal ASD
General effective radius
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What can we learn about ASD?
ASD retrieval seeks the (r0, ?) pair for which
the aerosol extinction cross-section Ca(?) best
matches the retrieved aerosol extinction
coefficient ?a(?). Problem Many plausible (r0,
?) pairs work equally well!
From Rault and Loughman (2007)
r0 (?m)
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1 ASD parameter retrieved
r0 (?m)
From Rault and Loughman (2007)
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Validation ofRetrieved Extinction
From Rault and Loughman (2007)
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Validation of Ozone
From Rault and Taha (2005)
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Conclusions

• The LS aerosol extinction retrieval method is
  immature, but initial comparisons between
  coincident SAGE III LS and SAGE II SO
  measurements show bias lt 10 and precision of
  10-25.
• The LS aerosol extinction retrieval is
  significantly improved by an iterative retrieval
  that infers one aerosol size distribution
  parameter
• LS can make a useful contribution to global
  stratospheric aerosol monitoring

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Current Activities

• As delivered, the OMPS LP algorithms were sound,
  but incomplete, and did not fully reflect the
  knowledge gained from parallel research by NOAA
  and NASA research groups.
• Collaborations with NOAA and NASA scientists have
  brought the OMPS LP retrieval algorithms (of
  ozone and aerosol) up to date and nearer
  operational quality.
• Before The aerosol retrieval algorithm alone
  took 20 min per scan.
• Now The surface reflectance, pointing, aerosol
  and ozone retrievals combined take 3 min per
  scan.

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OMPS LP Collaborators

• Didier Rault, David Flittner (NASA Langley)
• Glen Jaross, Ghassan Taha, Adam Bourassa (SSAI)
• Larry Flynn, Jianguo Niu (NOAA NESDIS)
• John Hornstein, Eric Shettle (NRL/IPO)
• John Bergman, Jerry Lumpe (CPI)
• Terry Deshler (U. of Wyoming)
• Doug Degenstein (U. of Saskatchewan)
• John Burrows, Christian von Savigny (U. of
  Bremen)
• YOUR NAME HERE?

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Future Activities

• Compare SAGE III LS multi-view aerosol retrievals
  over Laramie, Wyoming with the Feb. 13, 2006
  balloon measurement of size-resolved aerosol
  concentration (ground-truth comparison).
• Compare OSIRIS and SAGE III LS aerosol retrievals
  during the August 2004 INTEX campaign
• High density of SAGE III and OSIRIS LS
  coincidences
• Variety of observing conditions
• Apply lessons learned during the NPP OMPS flight
  to future NPOESS launches through the NOAA Data
  Exploitation program.

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Acknowledgements

• David Flittner, for lots of help with the
  radiative transfer modeling (especially the
  aerosol kernel calculation)
• The SAGE III instrument team, for patiently and
  carefully implementing the LS measurement mode,
  in its numerous permutations
• The SAGE II, SAGE III, and University of Wyoming
  aerosol measurement teams, for maintaining and
  sharing their high-quality data sets
• The NASA/NOAA/IPO team, for supporting LS
  research
• Eric Shettle, for helpful aerosol size
  distribution advice
• Adam Bourassa and the OSIRIS team, for shared
  data and helpful discussions

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THE END
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BACKUP SLIDES
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Volcanic Stratospheric Aerosol Cooling
Thanks to Makiko Sato
http//www.giss.nasa.gov/data/strataer/
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• Radiative forcing of long-lived greenhouse gases,
  relative to 1750 (From http//www.esrl.noaa.gov/gm
  d/aggi/, by Hoffman, 2007)

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Background(?) Stratospheric Aerosol

• In recent years, the observed stratospheric
  aerosol has dipped below previous estimates of
  the natural background stratospheric aerosol
  (Thomason, 2002)
• This clearly implies that our basic understanding
  of the processes controlling stratospheric
  aerosols is limited
• From the Integrated Global Atmospheric Chemistry
  Observations System Theme Report (2004)
• Given that the sulfate budget of the upper
  troposphere and lower stratosphere is not well
  understood, a coherent long-term record of the
  stratospheric aerosol will be essential. With
  SAGE-III the immediate future is well covered.
• but SAGE II R.I.P. SAGE III R.I.P.
  POAM III R.I.P.
• The future of (American) solar occultation
  measurements is very hazy and LS instruments
  may be asked to play a stratospheric aerosol
  monitoring role, ready or not.

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Limb Scattering (LS) Schematic
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Aerosol properties to retrieve

• Stratospheric aerosol extinction -- f(?, z)
• Stratospheric aerosol optical properties
  (complex index of refraction) f(?, z)
• Stratospheric aerosol shape and size
  distribution f(z)
• This is a chronically underdetermined problem
  (since all can vary with altitude)

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OMPSInstrument Design

• Total Ozone Mapper
• UV Backscatter, grating spectrometer, 2-D CCD
• TOMS, SBUV(/2), GOME(-2), OMI, SCIAMACHY
• 110 deg. cross track, 300 to 380 nm spectral
• Limb Profiler (LP)
• UV/Visible Limb Scatter, prism, 2-D CCD array
• SOLSE/LORE, OSIRIS, SAGE III, SCIAMACHY
• Three 100-KM vertical slits, 290 to 1000 nm
  spectral
• Nadir Profiler (NP)
• UV Backscatter, grating spectrometer, 2-D CCD
• SBUV(/2), GOME(-2), SCIAMACHY, OMI
• Nadir view, 250 km cross track, 270 to 310 nm
  spectral
• The calibration concept uses working and
  reference solar diffusers.

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Ozone Environmental Data Records (EDRs)
Properties and Performance
Table 1. Total Column Ozone EDR Performance.
Measurement Parameter Specification
Horizontal Cell Size 50 KM _at_nadir
Range 50 DU to 650 DU
Accuracy 15 DU or better
Precision 3 DU 0.5
Long-term Stability 1 over 7 years
Table 2. Ozone Profile EDR Performance.
Measurement Parameter Specification
Vertical Cell Size 3 KM
Vertical Coverage Tropopause to 60 KM
Horizontal Cell Size 250 KM Range
0.1 to 15 ppmv Accuracy
Below 15 KM Greater of 20 or 0.1 ppmv
Above 15 KM Greater of 10 or 0.1 ppmv
Precision Below 15 KM Greater of 10 or 0.1
ppmv 15 to 50 KM Greater of 3 or
0.05 ppmv 50 to 60 KM Greater of
10 or 0.1 ppmv Long-term Stability 2
over 7 years
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Procedure to retrieve size distribution
For each tangent altitude

• Define cost function
• ? ColorRatiodata-ColorRatioMie(Rmean,s)
• Find point of minimum ? ?? 0
• Look at all points with ? between ?0 and 2 ?0
• Find point at minimal distance
• Find points in direction
• of local minimum with
• minimum ?
• Evaluation of effective
• parameter ?

Effective parameter ?
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Assumed ASDs

• pink true ASD
• How do the ?a and ASD retrievals react to
  various assumed ASDs? (Stable, efficient
  retrieval?)

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For ozone retrievals, aerosols are a problem
From Loughman et al. (2005)
Even for background stratospheric aerosol
loading, the LS ozone profile retrieval error can
? 20 if aerosols are neglected in the ozone
retrieval algorithm (depending on scattering
angle).
Meeting the OMPS 3 ozone profile precision
threshold requires a high-quality LS aerosol
profile retrieval.
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Relevant aerosol properties

• Stratospheric aerosol number density na(z)
• Stratospheric aerosol size distribution (ASD)
  f(z)
• Stratospheric aerosol complex index of
  refraction f(?, z)
• Stratospheric aerosol shape f(z)
• For LS measurements, this is always likely to be
  an underdetermined retrieval problem, since all
  properties can vary with altitude. These
  properties combine to determine the aerosol
  scattering coefficient ?a(?), aerosol scattering
  cross-section ?a(?) and aerosol phase function
  Pa(?, ?) that appear in the radiative transfer
  equation

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even when the loading is small
Even for a low (bkgd) aerosol loading, a
sensitivity study for the LS ozone retrieval
algorithm shows that aerosol contamination is the
second largest term in the ozone retrieval error
budget
?
From Loughman et al. (2005)
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Characteristics of the perturbed ASDs LN
(log-normal, from dAlmedia, 1991) r0 0.0695
µm, s 1.86 MG (modified gamma, from WRCP,
1986) r0 1 µm, a 1, ? 1, b 18 Deshler
(bi-modal log-normal) No1 0.999045, r01
0.0564 µm, s1 1.521, No2 9.55d-4, r02 0.273
µm, s2 1.218 J (Junge, more suitable for
troposphere) rm 0.1 µm, ? 3