Atmospheric Chemistry and Physics Discussions
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10.5194/acpd-7-3629-2007
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http://www.atmos-chem-phys-discuss.net/7/3629/2007/acpd-7-3629-2007.pdf
3629
3718
2007-03-12
Retrieving global sources of aerosols from MODIS observations by inverting GOCART model
O. Dubovik
dubovik@loa.univ-lille1.fr
T. Lapyonok
Y. J. Kaufman
M. Chin
P. Ginoux
A. Sinyuk
Laboratoire de Optique Atmosphérique, Université de Lille 1/CNRS, Villeneuve d 'Ascq, France
Major part of this study was done while worked at: Laboratory for Terrestrial Physics, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Laboratory for Terrestrial Physics, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Science Systems and Applications, Inc., Lanham, MD, USA
Laboratory for Atmospheres, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Geophysical Fluid Dynamics Laboratory, NOAA, Princeton, NJ, USA
Knowledge of the global distribution of tropospheric aerosols is important
for studying the effects of aerosols on global climate. Chemical transport
models rely on archived meteorological fields, accounting for aerosol
sources, transport and removal processes can simulate the global
distribution of atmospheric aerosols. However, the accuracy of global
aerosol modeling is limited. Uncertainty in location and strength of aerosol
emission sources is a major factor in limiting modeling accuracy. This paper
describes an effort to develop an algorithm for retrieving global sources of
aerosol from satellite observations by inverting the GOCART aerosol
transport model.
<br><br>
To optimize inversion algorithm performance, the inversion was formulated as
a generalized multi-term least-squares-type fitting. This concept uses the
principles of statistical optimization and unites diverse retrieval
techniques into a single flexible inversion procedure. It is particularly
useful for choosing and refining a priori constraints in the retrieval algorithm. For
example, it is demonstrated that a priori limitations on the partial derivatives of
retrieved characteristics, which are widely used in atmospheric remote
sensing, can also be useful in inverse modeling for constraining time and
space variability of the retrieved global aerosol emissions. The
similarities and differences with the standard "Kalman filter" inverse modeling approach
and the "Phillips-Tikhonov-Twomey" constrained inversion widely used in remote sensing are discussed.
In order to retain the originally high space and time resolution of the
global model in the inversion of a long record of observations, the
algorithm was expressed using adjoint operators in a form convenient for
practical development of the inversion from codes implementing forward model
simulations.
<br><br>
The inversion algorithm was implemented using the GOCART aerosol transport
model. The numerical tests we conducted showed successful retrievals of
global aerosol emissions with a 2°×2.5° resolution by
inverting the GOCART output. For achieving satisfactory retrieval from
satellite sensors such as MODIS, the emissions were assumed constant within
the 24 h diurnal cycle and aerosol differences in chemical composition
were neglected. Such additional assumptions were needed to constrain the
inversion due to limitations of satellite temporal coverage and sensitivity
to aerosol parameters. As a result, the algorithm was defined for the
retrieval of emission sources of fine and coarse mode aerosols from the
MODIS fine and coarse mode aerosol optical thickness data respectively.
Numerical tests showed that such assumptions are justifiable, taking into
account the accuracy of the model and observations and that it provides
valuable retrievals of the location and the strength of the aerosol
emissions. The algorithm was applied to MODIS observations during two weeks
in August 2000. The global placement of fine mode aerosol sources retrieved
from inverting MODIS observations was coherent with available independent
knowledge. This was particularly encouraging since the inverse method did
not use any a priori information about the sources and it was initialized under a
"zero aerosol emission" assumption. The retrieval reproduced the
instantaneous global MODIS observations with a standard deviation in fitting
of aerosol optical thickness of ~0.04. The optical thickness during
high aerosol loading events was reproduced with a standard deviation of
~48%. Applications of the algorithm for the retrieval of coarse
mode aerosol emissions were less successful, mainly due to the currently
existing lack of MODIS data over high reflectance desert dust sources.
<br><br>
Possibilities for enhancing the global satellite data inversion by using
diverse a priori constraints on the retrieval are demonstrated. The potential and
limitations of applying our approach for the retrieval of global aerosol
sources from aerosol remote sensing are discussed.
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