Impact of Assimilation of Ground-based Doppler Radar Data on the Initialization and Prediction Super Typhoon Jangmi (2008)

Landfalling Typhoon in Taiwan exist motion deflection under the influence of Taiwan Central Mountain topography. This study uses the ARPS 3DVAR and cloud analysis package to assimilate data from four ground-based radars over the Taiwan Island for Typhoon Jangmi (2008) when it was near landfall. Jangmi was the most intense tropical cyclone of 2008 worldwide. The impacts of assimilating radial velocity and/or reflectivity data on the analysis and prediction of the vortex structure, intensity and track (also track deflection) are examined. The assimilations are performed at 30-min interval in a 6-h period that ends around 1 h and 40 min before Jangmi's landfall in northeast Taiwan. Thirty-hour predictions are made. All experiments that assimilate radar data recover mesoscale features within the eyewall region that are much closer to the observations than the operational GFS analysis does, and the predictions of the vortex structure are improved by the data assimilation. Radial velocity data are found to help improve the track forecast more while the reflectivity data help improve the intensity forecast most. The water vapor adjustment within the cloud analysis using reflectivity data plays the most important role in improving intensity forecast, compared to the adjustment to other thermal and microphysics fields. The improvement to Jangmi's prediction lasts for the full length of 30 hour predictions, and the most significant improvement exists in the first 12 hours for this weakening typhoon. Best results are obtained when both reflectivity and radial velocity are assimilated with water vapor update. Furthermore, all assimilation experiments capture the track deflection when it passes over the Central Mountain Range of Taiwan.