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

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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.