657
High-resolution Realtime Microscale Weather Analysis and Forecasting at Shenzhen, China

- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner
Wednesday, 5 February 2014
Hall C3 (The Georgia World Congress Center )
Yuewei Liu, NCAR, Boulder, CO; and Y. Liu, L. Pan, L. Li, Y. Jiang, Y. Zhang, W. Cheng, and G. Roux

Shenzhen is a major city located in the Pearl River Delta in southern China. The municipality covers an area of 2,050 square kilometers including urban and rural areas. In order to improve the weather service to the city, SZMB has implemented a high-density observation network with advanced remote sensing instruments in the Shenzhen metropolitan and surrounding area. The observation system includes ultra-dense surface Automatic Weather Station (AWS), wind profiler, radiometer, met-tower, Doppler radar, the Global Positioning System (GPS), lightning, and other network platforms. These observational systems/networks provide unique ultra-high spatiotemporal resolution observations for leveraging urban-scale numerical weather prediction. Toward this end, the NCAR WRF-based RTFDDA (Realtime Four Dimensional Data Assimilation) forecasting system has been deployed at SZMB and configured for the Shenzhen area. The system has been running in realtime to provide continuous weather analysis and forecast, with SZMB observation data assimilated. The modeling system contains four nested domains with horizontal grid sizes of 27km, 9km, 3km and 1 km, respectively. The 1km domain covers the Shenzhen municipality, Hong Kong, and neighboring areas. The model system is currently running at three hourly forecast cycles, and it will be upgraded to hourly cycles by the end of the project. The modeling system, the data assimilated in the system, in particular, the data quality control and model analysis and forecast assessment will be reported during the conference. Case studies will be discussed to illustrate the model system optimization for this region. The data impact, especially radar data impact on forecast of high-impact weather events and the data assimilation across different time scales will be addressed.