Articles | Volume 20, issue 23
Atmos. Chem. Phys., 20, 15207–15225, 2020
https://doi.org/10.5194/acp-20-15207-2020

Special issue: Dust aerosol measurements, modeling and multidisciplinary...

Atmos. Chem. Phys., 20, 15207–15225, 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 et al.

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Development of a regional feature selection-based machine learning system (RFSML v1.0) for air pollution forecasting over China
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Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-134,https://doi.org/10.5194/gmd-2022-134, 2022
Preprint under review for GMD
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Cited articles

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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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