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

Radiative forcing of each aerosol component IPCC AR-5 NOx

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

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

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

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

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, 100.3 E) is provided by Qiang Ji, Si-Chee Tsay, and others in a DOE project in 2008.

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

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.

However, once emitted, SO2 contributes to both atmospheric SO2 and aerosol loadings. Using OMI SO2 to constrain SO2 emissions Simulated SO2 OMI SO2 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

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.

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

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

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

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, 3441-3457, 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, 6396-6413, doi:10.1002/jgrd.50515, 2013.