A multi-case study of ensemble-based assimilation of radar observations into cloud-resolving WRF using DART

The assimilation of radar observations for ensemble-based mesoscale and storm-scale applications is one of the most challenging partially-solved problems of data assimilation. Among many questions remaining are how best to introduce environmental variability, how to maintain sufficient ensemble spread, how to treat low-reflectivity observations, and how to optimize localization. In this study, we adopt a multi-case approach to investigate some of these interesting questions. 3 cases with distinctly different characteristics (one supercell, one multi-cell, and one linearly-organized system) are chosen to facilitate the investigation of above-mentioned questions under very different circumstances and to understand some of the common behaviors both of the numerical model and the assimilation scheme across varying situations.

The numerical model used for this study is the Weather Research and Forecasting (WRF) model in its idealized simulation mode. The same configuration, a 2-km resolution, open boundary conditions, explicit cumulus treatment, and a 6-species microphysical scheme, is used in all cases. Higher-resolution results will also be presented if time permits. As for the data assimilation part, we use the Data Assimilation Research Testbed (DART) software with the sequential square-root ensemble Kalman filter option utilized for all cases.