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
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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
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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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