Articles | Volume 20, issue 23
https://doi.org/10.5194/acp-20-15207-2020
https://doi.org/10.5194/acp-20-15207-2020
Research article
 | 
08 Dec 2020
Research article |  | 08 Dec 2020

Source backtracking for dust storm emission inversion using an adjoint method: case study of Northeast China

Jianbing Jin, Arjo Segers, Hong Liao, Arnold Heemink, Richard Kranenburg, and Hai Xiang Lin

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Cited articles

Alfaro, S. C., Gaudichet, A., Gomes, L., and Maillé, M.: Mineral aerosol production by wind erosion: Aerosol particle sizes and binding energies, Geophys. Res. Lett., 25, 991–994, https://doi.org/10.1029/98gl00502, 1998. a
An, X. Q., Zhai, S. X., Jin, M., Gong, S., and Wang, Y.: Development of an adjoint model of GRAPES–CUACE and its application in tracking influential haze source areas in north China, Geosci. Model Dev., 9, 2153–2165, https://doi.org/10.5194/gmd-9-2153-2016, 2016. a
Basart, S., Pérez, C., Nickovic, S., Cuevas, E., and Baldasano, J.: Development and evaluation of the BSC-DREAM8b dust regional model over Northern Africa, the Mediterranean and the Middle East, Tellus B, 64, 18539, https://doi.org/10.3402/tellusb.v64i0.18539, 2012. a, b
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Short summary
Data assimilation provides a powerful tool to estimate emission inventories by feeding observations. This emission inversion relies on the correct assumption about the emission uncertainty, which describes the potential spatiotemporal spreads of sources. However, an unrepresentative uncertainty is unavoidable. Especially in the complex dust emission, the uncertainties can hardly all be taken into account. This study reports how adjoint can be used to detect errors in the emission uncertainty.
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