Since 2008, China Meteorological Administration (CMA) has been developing the Severe Weather Automatic Nowcast System (SWAN), a forecaster workstation aiming at providing an integrated, state-of-the-art and prompt tool for monitoring, warning and nowcasting severe weathers such as damaging wind gusts, tornadoes, thunderstorms and large hails. The prototype of SWAN was derived from two major nowcast systems that were originally developed in South China Regional Meteorological Center and Central China Regional Meteorological Center respectively, SWIFT and MYNOS. SWAN has been deployed in all provincial forecast offices and some affiliated offices in China.

SWAN's structure has been constructed on a Client/Server architecture. The data server utilizes several programming techniques or mechanisms such as multithreading programming, thread-dependent, network message receiving and triggering, modulation programming for its applications. The server can ingest all types of data ranging from Doppler weather radars, automatic weather stations, meteorological satellites, and mesoscale numerical weather prediction models, etc. It provides product generation of thunderstorm identification, tracking and nowcastings. The client software of SWAN has been built on the MICAPS v3.1 software framework. It provides a man-machine interactive interface for forecasters that can easily analyze all weather observations and prepare severe weather nowcast information. The Client products include a 3-D radar reflectivity mosaic, storm identification and tracking, radar quantitative precipitation estimate (QPE), radar echo extrapolation forecasts, short-range quantitative precipitation forecast (QPF), strong-echo-area identification and warning, radar echo displacement vector (COTREC wind), etc. The client program can also offer functions such as real-time severe weather alarming, cross-sectional analysis of three-dimensional radar reflectivities, thunderstorm positioning and severe weather nowcast preparedness.

The use of SWAN can enhance the effectiveness of severe weather analysis and reduce the lead time of severe weather warnings.