Ensemble Kalman Filter (EnKF) Assimilating the Dropsonde Observations to Reduce the Forecast Track Error of Typhoon Soulik (2013) Based On the Cloud-resolving Model

- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner
Wednesday, 5 February 2014: 11:00 AM
Room C201 (The Georgia World Congress Center )
Baoguo Xie, IBM Research, Beijing, China; and M. Zhang and H. Du


A cloud-resolving mesoscale model Weather Research and Forecast (WRF) and Ensemble Kalman Filter (EnKF) data assimilation method was used to assimilate the dropsonde observations for reducing the track error of Typhoon Soulik (2013). Typhoon Soulik was a powerful tropical cyclone that caused widespread damage in Taiwan and East China in July 2013. 1,620 thousand people were impacted and 8 people were dead during its landfall in Taiwan and east China. Taiwan "Dropsonde Observation for Typhoon Surveillance near the TAiwan Region" (DOTSTAR) initialed an observing task at 1200 UTC July 11 2013 which is 18 hours before the landfall of Soulik near Yilan City northern Taiwan. 16 dropsondes were released from about the 200 hPa pressure level to the ground during 4 hours of fight around and in front of moving direction of Soulik. Several assimilation experiments which assimilated all the dropsondes, the dropsondes in different four quadrants respectively were conducted to explore the impact of the observations in different direction relative to typhoon center to typhoon track forecast. It was found that observations in different directions relative to typhoon center have different impact on improving the typhoon tract and intensity forecast. Although the forecast without data assimilation is good enough for an operation run, this study still has its insight from the scientific aspect. It confirmed again that not all of the observations have positive impact on the forecast; some may degraded the forecast accuracy.