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

Abstract

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.

• radar data assimilation
• local ensemble transform Kalman filter
• quantitative precipitation nowcasting
• observing system simulation experiment
• mixed localisation method

6. Acknowledgements

The authors are thankful for the valuable comments from the peer reviewers and suggestions provided by Prof. Eugenia Kalnay from the University of Maryland, Dr. Chris Snyder and Dr. Juanzhen Sun from the National Center for Atmospheric Research, Prof. Ming Xue from the University of Oklahoma, Prof. Fuqing Zhang from the Pennsylvania State University, Dr. Kao-Shen Chung from Environment Canada and Dr. Takemasa Miyoshi from the Institute of Physical and Chemical Research, Japan. This research is sponsored by Taiwan National Science Council, under Grants NSC-100-2625-M-008-002, NSC-101-2625-M-008-003 and NSC-101-2111-M-008-020, and Taiwan Typhoon and Flood Research Institute.

Notes

¹For real radar data, the lower limit of Z _h depends on the sensitivity of the radar and the criteria of quality control.

²With 8-cpu parallel computing, the wall-clock time needed to advance one 15-minute analysis cycle for CTRL is approximately 85 minutes (including 70 minutes for one forecast step of the 40-member ensemble and 15 minutes for one LETKF analysis step).