Ideal case study of adaptive localization in a storm-scale Ensemble Kalman filter assimilation

According to the correlation between the observation and the background field error of model state variable, the hierarchical ensemble filter can adaptively determine localized influence weights. This study attempts to place the adaptive localization into the WRF-EnSRF assimilation system and conducts a series of storm-scale data assimilation tests using the simulation Doppler radar data. Compared with the traditional empirical localization scheme, the application characteristics and assimilation effects of the adaptive localization scheme are analyzed. The results show that adaptive localization is feasible for storm-scale Ensemble Kalman filter data assimilation. Compared with empirical localization, the assimilation effect of adaptive localization is less sensitive to distance scale. Owing to the advantages of the adaptive localization itself, the method can significantly improve the assimilation effect during the rapid development of the storm system and the assimilation of the reflectivity. Therefore, it was necessary to reasonably determine the appropriate localization method in accordance with the characteristics of different observations and different stages of storm development. Furthermore, the reasonable combination of empirical and adaptive methods is conducive to the optimization of assimilation results.