Articles | Volume 17, issue 22
https://doi.org/10.5194/gmd-17-8223-2024
https://doi.org/10.5194/gmd-17-8223-2024
Development and technical paper
 | 
20 Nov 2024
Development and technical paper |  | 20 Nov 2024

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

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Cited articles

Akhlaq, M., Sheltami, T. R., and Mouftah, H. T.: A Review of Techniques and Technologies for Sand and Dust Storm Detection, Rev. Environ. Sci. Bio., 11, 305–322, https://doi.org/10.1007/s11157-012-9282-y, 2012. a
Amezcua, J. and Van Leeuwen, P. J.: Gaussian Anamorphosis in the Analysis Step of the EnKF: A Joint State-Variable/Observation Approach, Tellus A, 66, 23493, https://doi.org/10.3402/tellusa.v66.23493, 2014. a
An, L., Che, H., Xue, M., Zhang, T., Wang, H., Wang, Y., Zhou, C., Zhao, H., Gui, K., Zheng, Y., Sun, T., Liang, Y., Sun, E., Zhang, H., and Zhang, X.: Temporal and Spatial Variations in Sand and Dust Storm Events in East Asia from 2007 to 2016: Relationships with Surface Conditions and Climate Change, Sci. Total Environ., 633, 452–462, https://doi.org/10.1016/j.scitotenv.2018.03.068, 2018. a
Bannister, R. N.: A Review of Operational Methods of Variational and Ensemble-Variational Data Assimilation, Q. J. Roy. Meteor. Soc., 143, 607–633, https://doi.org/10.1002/qj.2982, 2017. a
Bergamaschi, P., Krol, M., Meirink, J. F., Dentener, F., Segers, A., van Aardenne, J., Monni, S., Vermeulen, A. T., Schmidt, M., Ramonet, M., Yver, C., Meinhardt, F., Nisbet, E. G., Fisher, R. E., O'Doherty, S., and Dlugokencky, E. J.: Inverse Modeling of European CH4 Emissions 2001–2006, J. Geophys. Res., 115, D22309, https://doi.org/10.1029/2010JD014180, 2010. a
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Short summary
The ensemble Kalman filter (EnKF) improves dust storm forecasts but faces challenges with position errors. The valid time shifting EnKF (VTS-EnKF) addresses this by adjusting for position errors, enhancing accuracy in forecasting dust storms, as proven in tests on 2021 events, even with smaller ensembles and time intervals.
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