Ensemble Kalman Filter Assimilation of Coastal WSR-88D Radar Data and Forecasting for Hurricane Ike (2008)

In this study, Ensemble Kalman Filter (EnKF) assimilation and forecasting experiments are performed for the case of Hurricane Ike (2008), the third most destructive hurricane hitting the United States. Data from two coastal WSR-88D radars were carefully quality controlled, including automatic and manual velocity dealiasing. For the control experiment, 32 ensemble members are used in the EnKF system, and Z and Vr data from the two coastal radars are assimilated at 10-minute intervals over a 2-hour periods shortly before Ike made landfall. Compared the NCEP GFS analysis, the assimilation resulted in a much improved vortex intensity at the final analysis time, although it is still weaker than observed. Compared to the forecast starting from GFS analysis valid at the same time, the forecast intensity, track and structure of Ike over a 12 hour period are improved in terms of both deterministic and ensemble forecasts. The ensemble spread is well maintained with the help of larger multiplicative covariance inflation and posterior additive perturbations in sensitivity experiments. vity experiments. Assimilation of Vr or Z alone both has shown improvement on intensity, track and quantified precipitation forecast. Vr has shown more improvement in terms of all the aspects we examined, emphasizing more importance of Vr data. Ensemble forecast has shown uncertainty growth in track forecast but not in intensity forecast.30-minute interval has the similar results with 10-minute interval and 60-minute interval shows weaker intensity forecast. Further experiments are being conducted to further tune the EnKF assimilation system.