Articles | Volume 19, issue 15
Atmos. Chem. Phys., 19, 10009–10026, 2019
https://doi.org/10.5194/acp-19-10009-2019
Atmos. Chem. Phys., 19, 10009–10026, 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 et al.

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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
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