Development of Direct Assimilation of Ground based Radar Reflectivity Observations for HWRF and HAFS: Methodology and Impact on Mesoscale Descending Inflow and Secondary Eyewall Formation in Hurricane Matthew (2016)

The OU Multiscale data Assimilation and Predictability (MAP) lab has developed an approach to directly assimilate the reflectivity observations from the ground-based radars in EnVar (Wang and Wang 2017). This study implemented this method for HWRF and HAFS and their associated 3DEnVar data assimilation (DA) system to improve the landfalling tropical cyclone prediction. In this study, the direct reflectivity assimilation method is first described. The impact of assimilating ground-based radar reflectivity on the asymmetric rainband processes and secondary eyewall formation of Hurricane Matthew (2016) is investigated. Compared to the control experiment (no radar reflectivity DA), the radar reflectivity DA experiment shows a clear signal of concentric eyewall and eyewall replacement cycle. Results demonstrate that the radar reflectivity DA improves the stratiform rainband analysis, resulting in the mid-level cooling associated with mesoscale descending inflow (MDI). The MDI further contributes to the low-level acceleration maximum with boundary layer dynamics and triggers new convective updrafts in the secondary eyewall formation region. Momentum budget analysis also suggests that the mean vertical advection of absolute angular momentum plays an important role in the local momentum tendency in the secondary eyewall formation region in Hurricane Matthew (2016). These results provide a new insight into the forecast of intensity and structure changes by the radar DA.