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

Related authors

Valid time shifting ensemble Kalman filter (VTS-EnKF) for dust storm forecasting
Mijie Pang, Jianbing Jin, Arjo Segers, Huiya Jiang, Wei Han, Batjargal Buyantogtokh, Ji Xia, Li Fang, Jiandong Li, Hai Xiang Lin, and Hong Liao
Geosci. Model Dev., 17, 8223–8242, https://doi.org/10.5194/gmd-17-8223-2024,https://doi.org/10.5194/gmd-17-8223-2024, 2024
Short summary
Improving estimation of a record-breaking east Asian dust storm emission with lagged aerosol Ångström exponent observations
Yueming Cheng, Tie Dai, Junji Cao, Daisuke Goto, Jianbing Jin, Teruyuki Nakajima, and Guangyu Shi
Atmos. Chem. Phys., 24, 12643–12659, https://doi.org/10.5194/acp-24-12643-2024,https://doi.org/10.5194/acp-24-12643-2024, 2024
Short summary
A Novel Method for Quantifying the Contribution of Regional Transport to PM2.5 in Beijing (2013–2020): Combining Machine Learning with Concentration-Weighted Trajectory Analysis
Kang Hu, Hong Liao, Dantong Liu, Jianbing Jin, Lei Chen, Siyuan Li, Yangzhou Wu, Changhao Wu, Shitong Zhao, Xiaotong Jiang, Ping Tian, Kai Bi, Ye Wang, and Delong Zhao
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-157,https://doi.org/10.5194/gmd-2024-157, 2024
Preprint under review for GMD
Short summary
The sensitivity of aerosol data assimilation to vertical profiles: case study of dust storm assimilation with LOTOS-EUROS v2.2
Mijie Pang, Jianbing Jin, Ting Yang, Xi Chen, Arjo Segers, Hai Xiang Lin, Hong Liao, and Wei Han
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-113,https://doi.org/10.5194/gmd-2024-113, 2024
Preprint under review for GMD
Short summary
Observational operator for fair model calibration with ground NO2 measurements
Li Fang, Jianbing Jin, Arjo Segers, Ke Li, Ji Xia, Wei Han, Baojie Li, Hai Xiang Lin, Lei Zhu, Song Liu, and Hong Liao
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-216,https://doi.org/10.5194/gmd-2023-216, 2024
Revised manuscript accepted for GMD
Short summary

Related subject area

Subject: Aerosols | Research Activity: Atmospheric Modelling and Data Analysis | Altitude Range: Troposphere | Science Focus: Physics (physical properties and processes)
Revealing dominant patterns of aerosol regimes in the lower troposphere and their evolution from preindustrial times to the future in global climate model simulations
Jingmin Li, Mattia Righi, Johannes Hendricks, Christof G. Beer, Ulrike Burkhardt, and Anja Schmidt
Atmos. Chem. Phys., 24, 12727–12747, https://doi.org/10.5194/acp-24-12727-2024,https://doi.org/10.5194/acp-24-12727-2024, 2024
Short summary
Improving estimation of a record-breaking east Asian dust storm emission with lagged aerosol Ångström exponent observations
Yueming Cheng, Tie Dai, Junji Cao, Daisuke Goto, Jianbing Jin, Teruyuki Nakajima, and Guangyu Shi
Atmos. Chem. Phys., 24, 12643–12659, https://doi.org/10.5194/acp-24-12643-2024,https://doi.org/10.5194/acp-24-12643-2024, 2024
Short summary
Impact of biomass burning aerosols (BBA) on the tropical African climate in an ocean–atmosphere–aerosol coupled climate model
Marc Mallet, Aurore Voldoire, Fabien Solmon, Pierre Nabat, Thomas Drugé, and Romain Roehrig
Atmos. Chem. Phys., 24, 12509–12535, https://doi.org/10.5194/acp-24-12509-2024,https://doi.org/10.5194/acp-24-12509-2024, 2024
Short summary
Retrieval of refractive index and water content for the coating materials of aged black carbon aerosol based on optical properties: a theoretical analysis
Jia Liu, Cancan Zhu, Donghui Zhou, and Jinbao Han
Atmos. Chem. Phys., 24, 12341–12354, https://doi.org/10.5194/acp-24-12341-2024,https://doi.org/10.5194/acp-24-12341-2024, 2024
Short summary
Predicting hygroscopic growth of organosulfur aerosol particles using COSMOtherm
Zijun Li, Angela Buchholz, and Noora Hyttinen
Atmos. Chem. Phys., 24, 11717–11725, https://doi.org/10.5194/acp-24-11717-2024,https://doi.org/10.5194/acp-24-11717-2024, 2024
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, 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
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
Download
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.
Altmetrics
Final-revised paper
Preprint