The impact of radar radial velocity data assimilation using variational and EnKF systems on the forecast of Super Typhoon Hato (2017) with Rapid Intensification

Abstract

The prediction of the Rapid Intensification (RI) of Tropical Cyclones (TCs) is challenging in the nearshore areas of the northern South China Sea. In this study, we investigated the impact of radar Radial Velocity (RV) Data Assimilation (DA) on the initiation, development, and forecasts of the severe Typhoon Hato (2017), which is featured with rapid movement and intensifications. The investigation was based on rapid update cycling schemes based on variational and Ensemble Kalman Filter (EnKF) analyses by assimilating radar RV and conventional observations. Two EnKF DA experiments are designed to compare the horizontal localization scheme. It is found that, compared to the variational DA experiment, the two EnKF DA experiments tend to improve the dynamic and thermodynamic information of typhoon in the background more effectively, with the background error covariance estimated by the ensemble sampling. It seems the EnKF analyses based on the Successive Covariance Localization (SCL) method is able to more effectively adjust multiple scales even when the inner core of Hato is not completely covered by the RV observations.