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Title: The Physics of Atmospheric Gas Measurements The 2009 Noble Lectures University of Toronto


1
The Physics of Atmospheric Gas Measurements The
2009 Noble Lectures University of Toronto
Kelly Chance Harvard-Smithsonian Center for
Astrophysics
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C.D. Rodgers, Characterization and error analysis
of profiles retrieved from remote Sounding
measurements, J. Geophys. Res., 95, 5587-5595,
1990.
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Rodgers, 1990.
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UV/visible satellite spectra Introduction and
motivation
  • Target tropospheric gases are O3, NO2, HCHO,
    CHO-CHO, SO2, BrO, IO, H2O
  • Our aims are
  • To retrieve tropospheric gases from GOME,
    SCIAMACHY, OMI, and future UV/visible satellite
    instruments
  • To perform geophysical process studies with the
    results
  • To (eventually) monitor pollution globally and
    continuously from space
  • Successful retrieval involves detailed
    development of algorithm physics coupled with
    chemistry and transport modeling and
    multiple-scattering radiative transfer
    calculations.
  • Cloud products (cloud fraction, cloud-top height,
    and cloud optical thickness) are important. They
    are produced with GOMECAT, FRESCO, .

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The Rules
  • Dont define your algorithm in advance
  • - Test all steps for utility and applicability
  • - Let the physics guide you
  • No black boxes
  • - You must have and understand all source code,
    and be able to modify it as necessary
  • - You must test all assumptions
  • Fitters must go to bedrock (Occams taser) If
    you didnt do it yourself, it isnt done (and you
    have to do it down to bedrock and also understand
    and publish all the reasons why you did it that
    way)
  • Reference data as used must be peer-reviewed,
    published, and publicly-available (P3)
  • - no unexplained shifts in cross sections, for
    example

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Fitting Trace Gases
  • HCHO is the most challenging gas to fit for slant
    columns in satellite spectra (roughly, HCHO gt IO
    gt CHO-CHO gt SO2 gt OClO gt O3 (depending) gt BrOgt
    NO2)
  • Requires precise (dynamic) wavelength
    calibration, Ring effect correction,
    undersampling correction, and proper choices of
    reference spectra (HITRAN! BUT)
  • Best satellite fitting results come from direct
    fitting of radiances, I or I/E (except for
    tropospheric O3 and SO2)

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Slant Column Algorithm Overview
  • Direct fitting of radiances by nonlinear
    least-squares fitting
  • - Simple Ring effect formulation (no induced
  • Fraunhofer structure or induced
    wavelength
  • mismatch
  • - No distortion of measured data (no high-pass
  • filtering) no distortion of reference spectra.
  • Correction for
  • - Wavelength calibration (Doppler effect)
  • - Ring effect (Placzek-Teller coefficients)
  • - Spectral undersampling
  • Division by air-mass factor (AMF) using LIDORT
    radiative transfer model and GEOS-Chem 3-D
    tropospheric chemistry and transport model
  • - Tropospheric residuals may require further
  • adjustment (e.g., for NO2)

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Fitting approach Nonlinear least-squares fitting
of radiances with lots of optimization
Radiance R is fitted directly (BOAS fitting) as
N.B. ??!
Further manipulation for Beers law fitting gives
But Its not a linear fitting problem! (Note
alternate linear-nonlinear-linear fitting)
DOAS fitting adds high-pass filtering (H) to
give
Direct BOAS fitting gives a factor of 2-3
improvement!
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Good cross sections (like these) are critical.
They must be P3! FTS vacuum measurements are
much preferable to grating and/or air
measurements Cross sections from field
instruments should be avoided if at all possible.
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Ozone Cross Sections Need Work!
Hartley Huggins bands (245-355 nm)
Huggins bands (318-340 nm)
Chappuis bands (400-800 nm)
  • Wavelength-dependent O3 absorption dependence
    of Rayleigh scattering provide discrimination of
    O3 at different altitudes from backscattered
    measurements.
  • Temperature-dependent ozone absorption in the
    Huggins bands provides additional tropospheric
    ozone information.

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Fitting Example - HCHO
1.0(3) ? 1016 cm-2
 
3.0(4) ? 1016 cm-2
8.4(7) ? 1016 cm-2
Fitting of HCHO in GOME orbit 70927023 for low,
medium, and high HCHO amounts. Uncertainties from
spectrum fitting and cross sections included.
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Fitting Example - IO
SCIAMACHY spectral fitting for one orbit on
October 5, 2005
Detection of IO electronic transition
A2?3/2 X2?3/2 over wavelength window
between 426 -440 nm The (4,0) and (3,0) band,
centered At 427 nm and 435.7 nm
Grey line corresponds to fitted IO atmospheric OD
plus the final fitting residual Black line is
the fitted atmospheric OD
Retrieved amounts and 1sigma fitting uncertainties
are in SCD units of mol cm-2 Fitting rms is
given as fractions of full-scale radiance
Reference spectra include NO2, O3, O2-O2, H2O,
Ring, H2O Ring
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GEOS-CHEM global 3D tropospheric chemistry and
transport model
The GEOSChem model is a global 3-D model of
atmospheric composition driven by assimilated
meteorological observations from the Goddard
Earth Observing System (GEOS) of the NASA Global
Modeling and Assimilation Office (GMAO).
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  • LIDORT multiple-
  • scattering radiative transfer code (Spurr et
    al.)
  • Discrete ordinate radiative transfer code
  • Full analytical perturbation analysis of
    intensity field
  • Yields Jacobians (weighting functions) in one
    pass (no finite-differencing)
  • Pseudo-spherical and quasi-spherical versions
    available
  • Surface BRDF
  • Vector (polarization) version in progress
  • RT Solutions, Inc.

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Determination of air mass factors (AMFs), for
converting measured slant column abundances in
vertical column abundances, for absorption by
atmospheric gases. In the optically thin case,
the air mass factor calculation is separable into
a radiative transfer part (scattering weights)
and a normalized atmospheric loading (shape
factor).
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AMF example HCHO over Tennessee
S?(?) w(?)
AMF0.71
w(?)
AMFG2.08
GEOS-CHEM S?(?)
An AMF calculation is done for every GOME scene.
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Ozone hole
Biomass burning over Indonesia
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GLOBAL INVENTORY OF NITROGEN OXIDE EMISSIONS
CONSTRAINED BY SPACE-BASED OBSERVATIONS OF NO2
COLUMNS
Thanx to Randall Martin, Lyatt Jaeglé, Daniel
Jacob and many others!
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NO2 Los Angeles
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USE RETRIEVED NO2 COLUMNS TO MAP NOx EMISSIONS
Satellite
Tropospheric NO2 column ? ENOx
BOUNDARY LAYER
NO2
NO
NO/NO2 ? ? W/ Altitude
lifetime hours
HNO3
Emission
NITROGEN OXIDES (NOx)
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STRATEGY OPTIMIZE INVENTORIES USING A PRIORI
BOTTOM-UP AND GOME TOP-DOWN INFORMATION
Top-down emissions
A priori emissions
A posteriori emissions
Top-down errors
A priori errors
GOME
GEOS-CHEM
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HOW DO WE EVALUATE AND IMPROVE BOTTOM-UP NOx
INVENTORIES?
Surface NOX
  • Global NOx Emissions (Tg N yr-1)
  • Fossil Fuel (20-33)
  • Biomass Burning (3-13)
  • Soils (4-21)
  • Here in Tg N yr-1 (based on)
  • Fossil Fuel 24 (GEIA)
  • Biomass Burning 6 (Logan/Duncan)
  • Soils 5 (Yienger and Levy)
  • Sep 1996 Aug 1997

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OPTIMIZED NOX EMISSIONS
37.7 Tg N yr-1
36.4 Tg N yr-1
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GLOBAL TOP-DOWN ISOPRENE EMISSION INVENTORIES
CONSTRUCTED FROM GOME MEASUREMENTS OF
FORMALDEHYDE COLUMNS
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RELATING HCHO COLUMNS TO VOC EMISSION
hn (340 nm), OH
oxn.
VOCi
HCHO
yield yi
k 0.5 h-1
Emission Ei
smearing, displacement
In absence of horizontal wind, mass balance for
HCHO column WHCHO
Local linear relationship between HCHO and E
  • but wind smears this local relationship between
    WHCHO and Ei
  • For VOCs with lifetime gtgt 1 day, all structure
    in WHCHO is lost
  • For isoprene (lifetime 1h), smearing lt 100 km
    regional structure in WHCHO is preserved

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OZARKS ISOPRENE VOLCANO AS SEEN BY GOME
Temperature dependence of isoprene emission (GEIA)
GOME HCHO columns over the Ozarks, July 1996
daily orbits and relationship to temperature
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SEASONALITY OF GOME HCHO COLUMNS
(9/96-8/97) Largely reflects seasonality of
isoprene emissions general consistency with GEIA
but also some notable differences
GOME GEOS-CHEM (GEIA)
GOME GEOS-CHEM (GEIA)
JUL
MAR
AUG
APR
SEP
MAY
JUN
OCT
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Reactive NMVOC emissions from East and South
Asia. Upper panels bottom-up inventories of
Streets et al. 2003a (anthropogenic, biomass
burning) and Guenther et al. 2006 (biogenic).
Bottom panels emissions inferred from the GOME
HCHO observations in this study. Color
scales left - anthropogenic and biomass burning
right - biogenic and total sources. (Courtesy
T.-M. Fu)
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Monthly mean afternoon (1300 to 1700 local
time) surface ozone concen-trations simulated by
GEOS-Chem using bottom-up inventories for NMVOCs
in (a) March, (b) June, (c) September, and (d)
December, 2001. (Courtesy T.-M. Fu)
Difference in modeled monthly mean afternoon
(1300 to 1700 local time) surface ozone
concentrations using GOME-inferred reactive NMVOC
emission versus the bottom-up inventories for (a)
March, (b) June, (c) September, and (d) December,
2001. (Courtesy T.-M. Fu)
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BrO and near-surface O3 depletion at northern
high latitudes in spring
  • BrO is a strong source of O3
  • destruction in the stratosphere.
  • BrO is measured globally by GOME, SCIAMACHY, OMI.
  • Enhanced tropospheric BrO has been observed over
    the Arctic and Antarctic ice pack in the polar
    spring.
  • Quantifying tropospheric BrO from nadir satellite
    measurements is difficult due to the effect of
    Rayleigh scattering on air mass factors.

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OMI SO2 Volcanoes
Kilauea activity, source of the VOG event in
Honolulu on 9 November 2004
  • Air quality forecasting

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The End!
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Why the Smithsonian?
Langley, S.P., and C.G. Abbot, Annals of the
Astrophysical Observatory of the Smithsonian
Institution, Volume 1 (1900). Langleys recently
invented bolometer was used to make measurements
from the infrared through the near ultraviolet in
order to determine the mean value of the solar
constant and its variation. Langley and Abbot
also developed substantial new experimental
techniques (such as an early chart recorder) and
various analysis techniques (e.g., the Langley
plot), including photographic techniques for
high and low pass filtering to produce line
spectra from bolographs (spectra), illustrated,
foreshadowing the high pass filtering used today
by researchers employing the DOAS technique for
analyzing atmospheric spectra.
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GOME irradiance, radiance, and albedo for
high-albedo (fully cloudy) ground pixel
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Ozone profiles and tropospheric ozone from GOME,
SCIAMACHY, OMI, GOME-2, OMPS, .
Tropospheric ozone publications Liu, X., K.
Chance, C.E. Sioris, T.P. Kurosu, R.J.D. Spurr,
R.V. Martin, M. Fu, J.A. Logan, D.J. Jacob, P.I.
Palmer, M.J. Newchurch, I. Megretskaia, and R.
Chatfield, First directly-retrieved global
distribution of tropospheric column ozone from
GOME Comparison with the GEOS-CHEM model, J.
Geophys. Res., in press, 2005. Liu, X., K.
Chance, C.E. Sioris, R.J.D. Spurr, T.P. Kurosu,
R.V. Martin, and M.J. Newchurch, Ozone profile
and tropospheric ozone retrievals from Global
Ozone Monitoring Experiment Algorithm
description and validation, J. Geophys. Res. 110,
D20307, doi10.1029/2005JD006240,
2005. Rodriguez, J.V., C.J. Seftor, C.G.
Wellemeyer, and K. Chance, An overview of the
nadir sensor and algorithms for the NPOESS Ozone
Mapping and Profiler Suite (OMPS), Proc.
S.P.I.E., Optical Remote Sensing of the
Atmosphere and Clouds III, 4891, 65-75,
2003. Chance, K.V. J.P. Burrows, D. Perner, and
W. Schneider, Satellite measurements of
atmospheric ozone profiles, including
tropospheric ozone, from UV/visible measurements
in the nadir geometry A potential method to
retrieve tropospheric ozone, J. Quant. Spectrosc.
Radiat. Transfer 57, 467-476, 1997. Chance,
K.V., J.P. Burrows, and W. Schneider, Retrieval
and molecule sensitivity studies for the Global
Ozone Monitoring Experiment and the SCanning
Imaging Absorption spectroMeter for Atmospheric
CHartographY, Proc. S.P.I.E., Remote Sensing of
Atmospheric Chemistry, 1491, 151-165, 1991.
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Needs spectral data!
Examples of Averaging Kernels. Left averaging
kernels for an OMI and TES synthetic ozone
profile retrieval for an ozone estimate at 30.5
degrees latitude. The middle and right panels
show the averaging kernels for this same scene
but assuming a TES and OMI sounding of this scene
respectively.
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Top The boundary layer DOFS for the previous set
of ozone profiles as would be measured by OMI
(purple line), TES (orange line) and OMI plus TES
(black line). The DOFS are a metric for the
vertical resolution or sensitivity of ozone
sounding to the true ozone. Particularly striking
is the non-linear increase in boundary layer
sensitivity near 34 degrees latitude. This
results from the more linearly independent
averaging kernels of TES and OMI for this scene.
Bottom The total DOFS for the region between the
surface and 100 hPa (16 km).
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SCIAMACHY tropospheric NO2 is at higher spatial
resolution, showing greater detail for emission
sources
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NO2 Global Picture
Tropospheric Column NO2 (Sector Method) July 2005
Total Column NO2 (geometric AMF) July 2005
Cloud screening cloud fraction 20
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NO2
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INTERANNUAL VARIABILITY OF GOME HCHO COLUMNS
Augusts 1995-2001 correlation with temperature
anomaly explains some but not all of the HCHO
column variability
1995
1999
1996
2000
1997
2001
1998
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