Articles | Volume 19, issue 15
https://doi.org/10.5194/acp-19-10009-2019
https://doi.org/10.5194/acp-19-10009-2019
Research article
 | 
09 Aug 2019
Research article |  | 09 Aug 2019

Machine learning for observation bias correction with application to dust storm data assimilation

Jianbing Jin, Hai Xiang Lin, Arjo Segers, Yu Xie, and Arnold Heemink

Related authors

Observational operator for fair model evaluation 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., 17, 8267–8282, https://doi.org/10.5194/gmd-17-8267-2024,https://doi.org/10.5194/gmd-17-8267-2024, 2024
Short summary
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

Related subject area

Subject: Aerosols | Research Activity: Atmospheric Modelling and Data Analysis | Altitude Range: Troposphere | Science Focus: Physics (physical properties and processes)
Representation of iron aerosol size distributions of anthropogenic emissions is critical in evaluating atmospheric soluble iron input to the ocean
Mingxu Liu, Hitoshi Matsui, Douglas S. Hamilton, Sagar D. Rathod, Kara D. Lamb, and Natalie M. Mahowald
Atmos. Chem. Phys., 24, 13115–13127, https://doi.org/10.5194/acp-24-13115-2024,https://doi.org/10.5194/acp-24-13115-2024, 2024
Short summary
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

Cited articles

Benedetti, A., Di Giuseppe, F., Jones, L., Peuch, V.-H., Rémy, S., and Zhang, X.: The value of satellite observations in the analysis and short-range prediction of Asian dust, Atmos. Chem. Phys., 19, 987–998, https://doi.org/10.5194/acp-19-987-2019, 2019. a
Berry, T. and Harlim, J.: Correcting Biased Observation Model Error in Data Assimilation, Mon. Weather Rev., 145, 2833–2853, https://doi.org/10.1175/MWR-D-16-0428.1, 2017. a
Brasseur, G. P., Xie, Y., Petersen, A. K., Bouarar, I., Flemming, J., Gauss, M., Jiang, F., Kouznetsov, R., Kranenburg, R., Mijling, B., Peuch, V.-H., Pommier, M., Segers, A., Sofiev, M., Timmermans, R., van der A, R., Walters, S., Xu, J., and Zhou, G.: Ensemble forecasts of air quality in eastern China – Part 1: Model description and implementation of the MarcoPolo–Panda prediction system, version 1, Geosci. Model Dev., 12, 33–67, https://doi.org/10.5194/gmd-12-33-2019, 2019. a
Cesnulyte, V., Lindfors, A. V., Pitkänen, M. R. A., Lehtinen, K. E. J., Morcrette, J.-J., and Arola, A.: Comparing ECMWF AOD with AERONET observations at visible and UV wavelengths, Atmos. Chem. Phys., 14, 593–608, https://doi.org/10.5194/acp-14-593-2014, 2014. a
Chen, G., Li, S., Knibbs, L. D., Hamm, N. A. S., Cao, W., Li, T., Guo, J., Ren, H., Abramson, M. J., and Guo, Y.: A machine learning method to estimate PM2.5 concentrations across China with remote sensing, meteorological and land use information, Sci. Total Environ., 636, 52–60, https://doi.org/10.1016/j.scitotenv.2018.04.251, 2018. a, b
Download
Altmetrics
Final-revised paper
Preprint