ISSN 1000-0526
CN 11-2282/P
CN 11-2282/P
Archive > Volume 52 Issue 3 > 2026,52(3):325-336. DOI:10.7519/j.issn.1000-0526.2025.091601 Prev Next
Machine Learning-Based Grid-Point Warning of Thunderstorm Gale in Sichuan Basin
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Abstract:
Based on thunderstorm gale cases in Sichuan Basin from March 1 to September 30 in 2018-2022, combined with three-dimensional radar mosaic data and surface maximum wind observations, this paper constructs a thunderstorm gale sample dataset and develops a grid-point thunderstorm gale warning model. Independent validation is performed on thunderstorm gale events in 2023 and the warning performance of four models is evaluated. The results show that the LightGBM model achieves the highest probability of detection (POD), reaching 0.536 at a 15 min lead time and a 10 km evaluation radius, but it also exhibits the highest false alarm rate (FAR). The random forest model demonstrates the optimal comprehensive performance, with the highest critical success index (CSI) being 0.306 at a 30 min lead time and a 10 km evaluation radius. Both CSI and POD decrease significantly with prolonging warning lead time or decreasing evaluation radius, with a particularly notable decline in CSI when the lead time extends from 30 to 45 min. Synoptic conditions significantly influence the warning performance. Under pronounced cold air influence, factors such as echo intensity, echo top height, and 45 dBz echo top height are more likely to have high values, favoring the development of severe convection. However, newly initiated storms at convective fronts often lead to the increase in missed detections. In the absence of strong cold air, thunderstorm gales mainly occur at the leading edge of convective systems, resulting in higher POD. The temporal variation of vertically integrated liquid water content contributes most to the decision-making of models, followed by vertically integrated liquid water content density, echo top height, and maximum reflectivity factor. This highlights the central role of deep convection in the generation of thunderstorm gales. In the scenarios without cold air intrusion, downdrafts play a dominant role in thunderstorm gale warnings. Analysis of key feature values and high SHAP values reveals that temporal variations in convective echoes are critical for effective warnings. Samples with high echo-tracking wind speeds often correspond to positive SHAP values, indicating an increasing probability of convective wind events when echo motion accelerates.
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