基于2015~2017年台湾地区“苏迪罗”、“鲇鱼”、“海棠”等3次台风事件，利用地基GPS数据反演得到大气可降雨量（PWV），初步分析台风期间PWV与降雨量的关系，并利用交叉小波和小波相干进一步分析PWV与降雨量的时空变化规律。结果表明，台风发生期间，PWV总体呈先上升后下降的趋势，波动性剧烈；降雨发生时，PWV一定发生剧烈变化；在研究时域内，PWV与降雨量存在很强的正相关关系，相关系数达到0.7，PWV超前降雨量变化，PWV变化后的0~3 h发生强降雨。研究PWV与降雨量的时空变化特征可为短时降雨预报提供参考。"/>
Correlation Analysis of PWV and Rainfall during Typhoon
" />
This paper is based on the three typhoon events of “Soudelor”, “Megi” and “Haitang” in Taiwan from 2015 to 2017. We use ground-based GPS data to retrieve the atmospheric rainfall (PWV), and carry out preliminary analysis of the relationship between PWV and rainfall during typhoon.Second,we use cross-wavelet and wavelet coherence to further analyze the temporal and spatial variation of PWV and rainfall. The results show that: during the typhoon, the PWV generally rises and then decreases, and the volatility is severe. When the rainfall occurs, the PWV must change drastically. In the research time domain, PWV has a strong positive correlation with rainfall, and the correlation coefficient reaches 0.7; PWV changes in pre-existing rainfall, and heavy rainfall occurs 0-3 h after PWV changes. Studying the temporal and spatial variation characteristics of PWV and rainfall provides a good reference for short-term rainfall forecasting."/>
<div style="line-height: 150%">Correlation Analysis of PWV and Rainfall during Typhoon</div>
<div style="line-height: 150%"> </div>
Abstract This paper is based on the three typhoon events of “Soudelor”, “Megi” and “Haitang” in Taiwan from 2015 to 2017. We use ground-based GPS data to retrieve the atmospheric rainfall (PWV), and carry out preliminary analysis of the relationship between PWV and rainfall during typhoon.Second,we use cross-wavelet and wavelet coherence to further analyze the temporal and spatial variation of PWV and rainfall. The results show that: during the typhoon, the PWV generally rises and then decreases, and the volatility is severe. When the rainfall occurs, the PWV must change drastically. In the research time domain, PWV has a strong positive correlation with rainfall, and the correlation coefficient reaches 0.7; PWV changes in pre-existing rainfall, and heavy rainfall occurs 0-3 h after PWV changes. Studying the temporal and spatial variation characteristics of PWV and rainfall provides a good reference for short-term rainfall forecasting.
