Articles | Volume 20, issue 23
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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Subject: Aerosols | Research Activity: Atmospheric Modelling and Data Analysis | Altitude Range: Troposphere | Science Focus: Physics (physical properties and processes)
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Cited articles

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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,, 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,, 2012. a, b
Basart, S., Nickovic, S., Terradellas, E., Cuevas, E., García-Pando, C. P., García-Castrillo, G., Werner, E., and Benincasa, F.: The WMO SDS-WAS Regional Center for Northern Africa, Middle East and Europe, in: E3S Web of Conferences, vol. 99, EDP Sciences, 2019. a
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.
Final-revised paper