Ensemble-based data assimilation and prediction for Hurricanes: Impacts of assimilating Doppler radar observations

The ensemble-based data assimilation, commonly known as ensemble-Kalman filter or EnKF, has recently been demonstrated to be an effective and maturing assimilation technique with simulated and real observations for NWP across a range of scales. Building on the recent progress of Chen and Snyder (2006) in directly assimilating vortex/hurricane intensity and position observations with the ensemble-based technique, the current study examines the impacts of assimilating Doppler radar observations in the initiation and prediction of Hurricane Katrina (2005) with an WRF-based EnKF with model grid spacing down to 1.5 km. Despite some sensitivities to the number of observations to be assimilated (after quality control and data thinning) and the radius of influence of a given observation, preliminary results show that assimilating both the ground-based and airborne radar observations is very beneficial for initializing the hurricane near its observed intensity with realistic asymmetry and for subsequent ensemble forecast (or forecast from ensemble mean analysis). For example, forecast from a mean EnKF analysis that assimilates both the KMAX and KBYX Doppler radar observations at 00Z on 26 August 2005 tracks the observed hurricane position very closely and brings the hurricane directly to New Orleans in 96 h. Moreover, the radar observations assimilated ensure the hot-start of a Category 2 hurricane in near observed intensity without commonly used bogussing or surgical relocation techniques. Ultimately, we plan to explore the structure, dynamics and predictability of hurricanes through assimilating all available observations and to determine minimum sufficient observations for monitoring and predicting tropical cyclones before diminishing returns.