Dust storms pose significant risks to health and property, necessitating accurate forecasting for preventive measures. Despite advancements, dust models grapple with uncertainties arising from emission and transport processes. Data assimilation addresses these by integrating observations to rectify model error, enhancing forecast precision. The ensemble Kalman filter (EnKF) is a widely used assimilation algorithm that effectively optimize model states, particularly in terms of intensity adjustment. However, the EnKF's efficacy is challenged by position errors between modeled and observed dust features, especially under substantial position errors. This study introduces the valid time shifting ensemble Kalman filter (VTS-EnKF), which combines stochastic EnKF with a valid time shifting mechanism. By recruiting additional ensemble members from neighboring valid times, this method not only accommodates variations in dust load but also explicitly accounts for positional uncertainties. Consequently, the enlarged ensemble better represents both the intensity and positional errors, thereby optimizing the utilization of observational data. The proposed VTS-EnKF was evaluated against two severe dust storm cases from spring 2021, demonstrating that position errors notably deteriorated forecast performance in terms of root mean square error (RMSE) and normalized mean bias (NMB), impeding the EnKF's effective assimilation. Conversely, the VTS-EnKF improved both the analysis and forecast accuracy compared to the conventional EnKF. Additionally, to provide a more rigorous assessment of its performance, experiments were conducted using fewer ensemble members and different time intervals.@en