Improving quantitative precipitation nowcasting with a local ensemble transform Kalman filter radar data assimilation system: observing system simulation experiments

This study develops a Doppler radar data assimilation system, which couples the local ensemble transform Kalman filter with the Weather Research and Forecasting model. The benefits of this system to quantitative precipitation nowcasting (QPN) are evaluated with observing system simulation experiments on Typhoon Morakot (2009), which brought record-breaking rainfall and extensive damage to central and southern Taiwan. The results indicate that the assimilation of radial velocity and reflectivity observations improves the three-dimensional winds and rain-mixing ratio most significantly because of the direct relations in the observation operator. The patterns of spiral rainbands become more consistent between different ensemble members after radar data assimilation. The rainfall intensity and distribution during the 6-hour deterministic nowcast are also improved, especially for the first 3 hours. The nowcasts with and without radar data assimilation have similar evolution trends driven by synoptic-scale conditions. Furthermore, we carry out a series of sensitivity experiments to develop proper assimilation strategies, in which a mixed localisation method is proposed for the first time and found to give further QPN improvement in this typhoon case.