资源环境科技发展态势分析平台

Freezing rain (FZRA) is a weather hazard that can severely impact on power transmission, transportation, communication, and the economy. Nevertheless, forecasting FZRA is still a challenging problem. The Ramer scheme, which has been widely used in freezing rain forecasting, exhibits some issues for predicting FZRA events in China. The statistical characteristics of freezing rain and prognostic weather variables observed at ground and sounding stations in China from 2008 to 2017 were analyzed. After that, several sensitivity tests were conducted on the thresholds and the combined effect of different weather variables. Based on the results of the sensitivity tests, the Ramer scheme was modified to form an Altitude-dependent Ramer Scheme (ARS). The ARS was tested by simulating 572 FZRA events from 2008 to 2017, and 26 from 2018 to 2020. The results show that the ARS was more suitable for diagnosis of FZRA in China. It improved the Critical success index (CSI) to 0.47–0.54, and reduced the false alarm rate (FAR) to 0.40–0.53 from the original Ramer Scheme. The Ramer scheme and ARS were applied to the Weather Research and Forecasting (WRF) model forecast output to simulate an extreme FZRA event occurred in Southern China from 25 to 27 January 2018. In this case, the ARS significantly outperformed the original Ramer scheme in forecasting the FZRA, with CSI 148% higher and FAR 53% lower than the Ramer Scheme. The modified Ramer scheme provides more accurate information for forecasting and warning against ice accumulation on wires, road icing and other icing hazards.