摘要
To better understand the relationship between anticyclones in Siberia and cold-air activities and temperature changes in East Asia,this study proposes a 2D anticyclone identification method based on a deep-learning model,Mask R-CNN,which can reliably detect the changes in the morphological characteristics of anticyclones.Using the new method,the authors identified the southeastward-extending Siberian cold high(SEESCH),which greatly affects wintertime temperatures in China.This type of cold high is one of the main synoptic systems(45.7%)emerging from Siberia in winter.Cold air carried by SEESCH has a significant negative correlation with the temperature changes in the downstream area,and 52% of SEESCHs are accompanied by cold-air accumulation in North and East China,which has a significant impact on regional cooling.These results provide clues for studying the interconnection between SEESCHs and extreme cold events.
为了更好地研究西伯利亚地区反气旋与冷空气活动,东亚地区气温变化之间的关联,本文提出一种基于Mask R-CNN的反气旋识别方法,能够较为准确地刻画反气旋形态特征变化.使用该方法能够识别对中国冬季气温具有较大影响的东南延伸型西伯利亚冷高压(SEESCH),这种冷高压是冬季出现在西伯利亚地区的主要天气系统之一(45.7%).SEESCH携带的冷空气与下游地区温度变化呈显著负相关,52%的SEESCH伴随着华北华东地区冷空气聚集,对区域降温有显著影响.这些结果为研究SEESCH与极端寒冷事件之间的联系提供线索.
基金
supported jointly by the National Key Research and Development Program of China[grant number 2019YFC1510201]
the National Natural Science Foundation of China[grant number 41975073].
