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A new Southwest Vortex objective identification algorithm based on precipitation perspective
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AI
DOI：10.1088/1748-9326/ad86d0.png)
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Abstract
En 中文
This study developed an objective identification algorithm for the Southwest Vortex (SWV), the dominant rainstorm system prevalent in Southwest China. Based on the TempestExt package, the newly developed SWV identification algorithm employed a 12 h minimum duration threshold, utilizing the high-resolution ERA5 dataset with 1 h intervals. The results reveal that the algorithm precisely identifies the SWV records documented in the SWV Yearbooks, achieving an optimal balance of high probability of detection and low false alarm rate. In addition, the algorithm can detect the SWVs in advance, offering a significant enhancement to SWV monitoring. Notably, the algorithm-detected SWVs exhibit a strong correlation with rainstorms, with a correlation coefficient of 0.70. From an on-site precipitation perspective, the algorithm-detected SWVs account for 87.6% of the observed rainstorm days. Collectively, this study introduces an updated tool for the SWV identification and underscores the algorithm's promising potential in improving rainstorm forecasts in Southwest China.
Keywords:
Southwest Vortex
algorithmic detection
rainstorm
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