期刊
ATMOSPHERIC RESEARCH
卷 258, 期 -, 页码 -出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.atmosres.2021.105631
关键词
Summer rainfall event; Anomaly-based synoptic analysis; Model product application; Southern China
资金
- innovative R&D project in Guangdong Province in China [2019ZT08G669]
- National Natural Science Foundation of China [41775067, J202001]
A novel anomaly-based synoptic analysis approach was used to study nine rainfall events in southern China in the summer of 2020, with the results compared to standard synoptic analysis. These events did not strictly follow the climatic seasonal march of the summer monsoon, highlighting the importance of anomaly-based analysis in predicting monsoonal rainfall patterns.
Nine rainfall events affecting southern China in the 2020 summer were studied by a novel anomaly-based synoptic analysis approach, and the results were compared to those of standard synoptic analysis. These rainfall events occurred in the Dragon-boat rainy season and Meiyu rainy season from the Pearl River to the Yangtze and Huaihe rivers and did not entirely follow the climatic seasonal march of the summer monsoon. The first rainfall event occurred within the Dragon-boat rainy season and was associated with a broad rainfall area with scattered precipitation centers related to a broad convergence area of anomalous flows with scattered moist vorticity anomaly (MVA) centers located between two anomalous anticyclonic systems at 925 hPa. The other eight rainfall events appeared in the Meiyu rainy season and were generally associated with only a narrow convergence belt of anomalous flows and a positive MVA belt, sometimes with an anomalous cyclone moving eastward in the lower troposphere. The ECMWF model successfully predicted these anomalous convergence areas and MVA belts with lead times of 2-7 days. The advantages of this anomaly-based analysis approach include its ability to visually identify the location of a rainfall event, pinpoint possible meteorological causes and extend the model forecast length by the earlier detection of predictors.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据