Retrieving global aerosol emission sources from satellite observations

PhD student: Cheng CHEN

--> download Cheng CHEN's CV

The project
Our understanding of the role atmospheric aerosol plays in earth-atmosphere system is limited by the uncertainties in our knowledge of the global distribution, composition and sources of atmospheric aerosol. While the satellite observations from space provide very valuable and extensive information about aerosol, this information is not fully appropriate for answering the important questions about effect of aerosol on climate and environment. These questions are usually addressed by simulations using chemical transport and climate models, because these models intend to consider full complexity of physical and chemical processes in the atmosphere and allow modeling of detailed distribution of aerosol in global scale and for any chosen time period. However, the accuracy of global aerosol models is limited by uncertainties in aerosol emission source characteristics, knowledge of atmospheric processes, and the meteorological field data used. As a result, even the most recent models are mainly expected to capture only the principal global features of aerosol transport; among different models, quantitative estimates of average regional aerosol properties often disagree by amounts exceeding the uncertainty of remote sensing aerosol observations. Therefore, there are diverse and continuing efforts to harmonize and improve global aerosol modeling by refining the meteorology, atmospheric process representations, emissions, and other modeling components used. One of the most promising approaches for reducing this uncertainty is improving the aerosol emission fields (that is input for the models) by inverse modeling retrieval, i.e. fitting satellite observations and models and retrieving or correcting aerosol emission. For example, Dubovik et al., (2008) have developed an algorithm for inverting global MODIS data and implemented the approach to retrieve global distribution of fine mode aerosol emissions. However, due to many factors  (limitation of satellite observations to daytime, clear-sky condition, etc.) retrieval of global aerosol emission sources from satellite observations remains very challenging and underdeveloped task.

The objective of the thesis is to improve this retrieval approach by means of both:
(i) Using and advancing the up-to-data elaborated transport model, such as, GEOS–Chem and Adjoint GEOS-Chem (Bey et al. 2001, Henze et al. 2007) that has already largely adapted for inverse modeling purpose and
(ii) Applying it to more detailed (compare to MODIS data) satellite observations from GRASP (Dubovik et al. 2011, 2014) products of PARASOL polarimeter possibly combined with data of other satellite (such as CALIPSO and MODIS) for retrieving.

Keywords: Aerosol emission sources, GEOS-Chem, Adjoint, GRASP, Inverse Modeling


Dubovik O., Lapyonok T., Kaufman, Y. J., Chin M., Ginoux P., Kahn R. A. and Sinyuk A., Retrievaing global aerosol sources from satellites using inverse modeling, Atmos. Chem. Phys., 8, 209-250, 2008

Dubovik O., Lapyonok T., Litvinov, P., Herman M., Fuertes D., Ducos F., Lopatin A., Chaikovsky A., Torres B., Derimian Y., Huang X., Aspetsberger M. and Federspiel C., GRASP: a versatile algorithm for characterizing the atmosphere, SPIE, 2014

Dubovik O., Herman M., Holdak A., Lapyonok T., Tanré D., Deuzé J. L., Ducos F., Sinyuk A. and Lopatin A., Statistically optimized inversion algorithm for enhanced retrieval of aerosol properties from spectral multi-angle polarimetric satellite observations, Atmos. Meas. Tech., 4, 975-1018, 2011

Henze D. K., Hakami A. and Seinfeid J. H., Development of the adjoint of GEOS-Chem, Atmos. Chem. Phys., 7, 2413-2433, 2007

Bey I., Jacob D. J., Yantosca R. M., Logan J. A., Field B. D., Fiore A. M., Li Q., Liu Y., Mickley L. J. and Schultz M. G., Global modeling of tropospheric chemistry with assimilated meteorology: Model description and evaluation, J. Geophys. Res., 106, 23073-23095, 2001

Director: Oleg Dubovik (LOA)

Laboratory: LOA

Financing: Bourse du gouvernement