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

Data sets

Ground-based air quality measurements during the 2021 spring super dust storms J. Jin https://doi.org/10.5281/zenodo.6459866

Model code and software

Source code of PyFilter (v1.1) Mijie Pang https://doi.org/10.5281/zenodo.7611976

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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.