Articles | Volume 20, issue 23
Atmos. Chem. Phys., 20, 15207–15225, 2020

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

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

Related authors

How aerosol size matters in AOD assimilation and the optimization using Ångström exponent
Jianbing Jin, Bas Henzing, and Arjo Segers
Atmos. Chem. Phys. Discuss.,,, 2022
Preprint under review for ACP
Short summary
Development of a regional feature selection-based machine learning system (RFSML v1.0) for air pollution forecasting over China
Li Fang, Jianbing Jin, Arjo Segers, Hai Xiang Lin, Mijie Pang, Cong Xiao, Tuo Deng, and Hong Liao
Geosci. Model Dev., 15, 7791–7807,,, 2022
Short summary
Climate-driven deterioration of future ozone pollution in Asia predicted by machine learning with multisource data
Huimin Li, Yang Yang, Jianbing Jin, Hailong Wang, Ke Li, Pinya Wang, and Hong Liao
Atmos. Chem. Phys. Discuss.,,, 2022
Revised manuscript under review for ACP
Short summary
Composited analyses of the chemical and physical characteristics of co-polluted days by ozone and PM2.5 over 2013–2020 in the Beijing–Tianjin–Hebei region
Huibin Dai, Hong Liao, Ke Li, Xu Yue, Yang Yang, Jia Zhu, Jianbing Jin, and Baojie Li
Atmos. Chem. Phys. Discuss.,,, 2022
Revised manuscript under review for ACP
Short summary
Inverse modeling of the 2021 spring super dust storms in East Asia
Jianbing Jin, Mijie Pang, Arjo Segers, Wei Han, Li Fang, Baojie Li, Haochuan Feng, Hai Xiang Lin, and Hong Liao
Atmos. Chem. Phys., 22, 6393–6410,,, 2022
Short summary

Related subject area

Subject: Aerosols | Research Activity: Atmospheric Modelling | Altitude Range: Troposphere | Science Focus: Physics (physical properties and processes)
Instant and delayed effects of March biomass burning aerosols over the Indochina Peninsula
Anbao Zhu, Haiming Xu, Jiechun Deng, Jing Ma, and Shaofeng Hua
Atmos. Chem. Phys., 22, 15425–15447,,, 2022
Short summary
Aerosol–cloud interaction in the atmospheric chemistry model GRAPES_Meso5.1/CUACE and its impacts on mesoscale numerical weather prediction under haze pollution conditions in Jing–Jin–Ji in China
Wenjie Zhang, Hong Wang, Xiaoye Zhang, Liping Huang, Yue Peng, Zhaodong Liu, Xiao Zhang, and Huizheng Che
Atmos. Chem. Phys., 22, 15207–15221,,, 2022
Short summary
Survival probabilities of atmospheric particles: comparison based on theory, cluster population simulations, and observations in Beijing
Santeri Tuovinen, Runlong Cai, Veli-Matti Kerminen, Jingkun Jiang, Chao Yan, Markku Kulmala, and Jenni Kontkanen
Atmos. Chem. Phys., 22, 15071–15091,,, 2022
Short summary
The simulation of mineral dust in the United Kingdom Earth System Model UKESM1
Stephanie Woodward, Alistair A. Sellar, Yongming Tang, Marc Stringer, Andrew Yool, Eddy Robertson, and Andy Wiltshire
Atmos. Chem. Phys., 22, 14503–14528,,, 2022
Short summary
Dust pollution in China affected by different spatial and temporal types of El Niño
Yang Yang, Liangying Zeng, Hailong Wang, Pinya Wang, and Hong Liao
Atmos. Chem. Phys., 22, 14489–14502,,, 2022
Short summary

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,, 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,, 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