In this study, we analyzed the brightness temperature characteristics of short-duration heavy rainfall (SDHR) along the Chengdu–Chongqing Railway (CCR), an important corridor of economic and transportation activity in southwest China. Our findings could prove useful in the monitoring and advance warning of SDHR events: (1) SDHR predominantly occurred from July to August, with a peak frequency in July in the CCR area. In terms of diurnal variation, SDHR was mainly observed at night, particularly between 22:00–05:00 and 06:00–09:00 (local time), with a peak at 01:00; (2) The relationship between SDHR and equivalent blackbody temperature (TBB) further showed that the occurrence of SDHR was accompanied by TBB decreasing to its minimum value, after which it increased, and SDHR ceased. In cases where TBB approached its minimum value after 1 h but continued to decrease slightly, SDHR continued. When SDHR occurred, the majority of the TBB values were recorded in the range 190–230 K; within this range, values between 190 and 200 K were most frequently recorded. In general, lower TBB values are associated with more intense SDHR. Based on this finding, we used linear regression to establish an estimating equation for SDHR.

Tornadoes are severe convective weather exhibiting localized and sudden occurrences. Weather radar is widely regarded as the most effective tool for monitoring tornadoes and issuing early warnings. Dual-polarization updating has significantly improved tornado prediction and forecasting abilities. This article proposes an innovative tornado detection algorithm based on XGBoost which is suitable for dual-polarization radar data, was upgraded and has been used in China since 2019, and has been applied in the Tornado Key Open Laboratory of the China Meteorological Administration. The characteristics associated with the velocity attributes, reflectivity, velocity spectrum width, differential reflectivity, and correlation coefficient are used in the algorithm to achieve real-time tornado detection. Experimental evaluations have demonstrated that the proposed algorithm significantly improves tornado detection rates and leading times. Compared with the traditional TDA-RF algorithm based on Doppler weather radar data, the TDA-XGB algorithm introduces dual polarization parameters (such as differential reflectivity and the correlation coefficient), which effectively improves tornado identification performance. In addition, the TDA-XGB algorithm combines artificial intelligence-assisted learning to optimize the traditional algorithm based on the tornado vortex signature (TVS) and tornado debris signature (TDS), further improving the detection effect. Furthermore, the algorithm provides classification probabilities in the genesis and evolution of tornadoes, thereby supporting forecasters in their efforts to anticipate and issue timely tornado warnings.