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Remote Sens. 2012, 4(11), 3528-3543; doi:10.3390/rs4113528

Estimating Aerosol Emissions by Assimilating Remote Sensing Observations into a Global Transport Model

1,* , 2
1 University of Oxford, Parks rd, OX1 3PU Oxford, UK 2 Faculty of Applied Sociology, Environmental Studies, Kinki University, 3-4-1 Kowakae, 577-8502 Higashi Osaka, Osaka, Japan 3 University of Tokyo, 5-1-5 Kashiwanoha, 277-8658 Kashiwa, Chiba, Japan
* Author to whom correspondence should be addressed.
Received: 12 September 2012 / Revised: 10 November 2012 / Accepted: 10 November 2012 / Published: 19 November 2012
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We present a fixed-lag ensemble Kalman smoother for estimating emissions for a global aerosol transport model from remote sensing observations. We assimilate AERONET AOT and AE as well as MODIS Terra AOT over ocean to estimate the emissions for dust, sea salt and carbon aerosol and the precursor gas SO2. For January 2009, globally dust emission decreases by 26% (to 3,244 Tg/yr), sea salt emission increases by 190% (to 9073 Tg/yr), while carbon emission increases by 45% (to 136 Tg/yr), compared with the standard emissions. Remaining errors in global emissions are estimated at 62% (dust), 18% (sea salt) and 78% (carbons), with the large errors over land mostly due to the sparseness of AERONET observations. The new emissions are verified by comparing a forecast run against independent MODIS Aqua AOT, which shows significant improvement over both ocean and land. This paper confirms the usefulness of remote sensing observations for improving global aerosol modelling.
Keywords: aerosol; emission estimation; Kalman smoother; MODIS; AERONET aerosol; emission estimation; Kalman smoother; MODIS; AERONET
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Schutgens, N.; Nakata, M.; Nakajima, T. Estimating Aerosol Emissions by Assimilating Remote Sensing Observations into a Global Transport Model. Remote Sens. 2012, 4, 3528-3543.

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