A Review on the Ensemble-Based Data Assimilations for Severe Convective Storms

Predicting severe convective storms has long been recognized as one of the most important aspects as well as one of the most difficult part of weather forecast. With advanced ensemble-based data assimilation techniques like ensemble Kalman filter, the uncertainties in initial conditions of storm-scale weather prediction can be significantly reduced, leading to an improved performance of severe weather forecast. This review paper will briefly introduce the concepts and variations of ensemble Kalman filter, schemes to improve filter performance at storm scales, applications of various conventional and in situ observational platforms in ensemble data assimilation, and focuses on the severe weather prediction systems as well as operational and quasi-operational storm-scale ensemble data assimilation and prediction systems, the issues and difficulties that encountered in current applications, and possible future directions.