Abstract
As a prevalent meteorological hazard across China, severe thunderstorm winds originate directly from storm cells. With the dense networking of Doppler weather radars in recent years, networked radar observations have become an essential technical support for the operational monitoring and early warning of severe thunderstorm winds. Traditional storm cell identification, tracking and nowcasting methods are mainly tailored to single-radar datasets. To overcome the limitations of traditional methods, this study proposes a novel method for storm cell identification, tracking, and nowcasting based on networked radar data. The method comprises three steps: identifying 3D storm cells via digital image processing and assessment their intensity using fuzzy logic; constructing a minimum cost function for spatiotemporal matching and continuous tracking; and finally, adopting the unscented Kalman filter and the adaptive damped Holt method to forecast the future trajectory and intensity, respectively. Compared with the traditional operational nowcasting methods adopted by meteorological services in China, namely storm cell identification and tracking (SCIT) and thunderstorm identification, tracking, analysis and nowcasting (TITAN), the proposed method exhibits growing advantages in storm location prediction within the 60-min forecast lead time, with controllable decay of probability of detection (POD) and critical success index (CSI) and low false alarm rate (FAR). The developed intensity forecast product enriches the storm forecasting framework, with its mean absolute error (MAE) kept within a controllable range to support operational short-range nowcasting.