1. Top-down estimate of aerosol emissions from MODIS and OMI
Jun Wang, Xiaoguang Xu, Yi Wang
Daven Henze, Zhen Qu
Yuxuang Wang
Atmospheric Composition and Carbon
3rd International A-Train Symposium
Pasadena, California, 2017

2. Radiative forcing of each aerosol component
IPCC AR-5
NOx

3. Importance of aerosol emissions
CTM model simulations can only be as good as the emissions.
Data assimilation:
Initial condition error from emissions still persist.
Its effect on model simulation decays quickly with time.
For both climate studies and air quality forecast, there is a need to have a holistic interpretation from emissions to observations, and vice versa.
1/22/2017
emissions
CTM
Forecast&
Validation

4. Conceptual Diagram GEOS-chem & △ Jacobian /  (, m, …) SAT
Forward △
Jacobian /  (, m, …)
Emissions
Assuming aerosol composition (relative mass ratio for each species) has no bias in the CTM,
GEOS-chem
Adjoint

5. SO2 emissions for Apr. 2008 over China
Inversion with MODIS and evaluation with OMI SO2
Prior SO2 emission for 2005/2006
Posterior SO2 emission
OMI SO2 loading
GC-modeled SO2
OMI SO2 loading moles cm-2

6. NOx emissions for Apr. 2008 over China
Inversion with MODIS and evaluation with OMI NOx
Prior NO2 emission for 2005/2006
Posterior NO2 emission
OMI NO2 loading
GC-modeled NO2
OMI NO2 loading moles cm-2

7. Validation over the ground station in dust source region
Since there is not direct measurement of dust emission, so we evalute the simualted results with those simulated with dust emission…
In situ data measured at Zhangye (39.08 N, E) is provided by Qiang Ji, Si-Chee Tsay, and others in a DOE project in 2008.

8. Further evaluation with AERONET (dots) and MISR AOD
over the whole study region
Prior AOD at MISR space
Posterior AOD at MISR space
MISR AOD

9. Put all together, posterior emission gives GC simulation better agreement with MISR and AERONET AOD, OMI SO2 and NO2, and in situ ground data of PM10, and sulfate-nitrate-ammonium mass.

10. However, once emitted, SO2 contributes to both atmospheric SO2 and aerosol loadings. Using OMI SO2 to constrain SO2 emissions
Simulated SO OMI SO Simulation – OMI SO2 emission
prior
(bottom-up)
posterior
(top-down)
April 2008.
B.18: A new approach for monthly updates of anthropogenic sulfur dioxide emissions from space

11. Large urban emissions are reduced.
Top-down (OMI) vs. bottom-up
Two different sensors (one for gas and one for aerosols) telling nearly the same results about SO2 emissions!
Top-Down vs. Top-down
MODIS vs. OMI
Top-down (MODIS) vs. bottom-up
Large urban emissions are reduced.

12. Let’s embrace GEO Era Sentinel-4 TEMPO MSG GOES-R GEMS Himawari
Courtesy Jhoon Kim,
Andreas Richter
Policy-relevant science and environmental services enabled by common observations
Improved emissions, at common confidence levels, over industrialized Northern Hemisphere
Improved air quality forecasts and assimilation systems
Improved assessment, e.g., observations to support the United Nations Convention on Long Range Transboundary Air Pollution

13. Multi-angle, multi-spectral, and polarization measurements
Sensitivity of polarized reflectance to emission is 2-3 factor larger than that of intensity
MAIA
to be launched in 2020
AMT, 2013

14. Summary
With the pending availability of geostationary measurements of tropospheric composition, it may soon be possible to rapidly constrain emission of aerosols and their precursors at fine spatiotemporal scales through combined use of bottom-up and top-down emission estimate techniques.
TEMPO+ GOES-R, etc.
NPP, JPSS, EPIC, etc
MAIA, PACE, etc
TROPOMI, OCO-2, etc
O2 A, O2 B + polarization
AEROENT + polarization

15. Thank you ! References. http://arroma.uiowa.edu
Wang, Y., J. Wang, X. Xu, D. K. Henze, Y. Wang, Z. Qu, A new approach for monthly updates of anthropogenic sulfur dioxide emissions from space: implications for air quality forecasts, Geophys. Res. Lett., 43, 9931–9938, 2016
Meland, B. S., Xu, X., Henze, D. K., and J. Wang , Assessing remote polarimetric measurements sensitivities to aerosol emissions using the GEOS-Chem adjoint model, Atmos. Meas. Tech. , 6, , 2013.
Xu, X.,  J. Wang , D. Henze, W. Qu, and M. Kopacz, Constraints on aerosol sources using GEOS-Chem adjoint and MODIS radiances, and evaluation with Multi-sensor (OMI, MISR) data, J. Geophys. Res. Atmos., 118, , doi: /jgrd.50515, 2013.