用福建连续重力资料研究台风“麦德姆”活动特征

福建省地处我国东南沿海,每年5—10月都会受到台风的侵袭,目前建有厦门、漳州、福州三个连续重力观测台站。以2014年第10号强台风"麦德姆"为例,利用重力观测资料研究:(1)台风引起的地脉动信号的强度和卓越频率的变化规律;(2)台风卓越周期与台风强度和移动速度的关系;(3)连续重力资料非潮汐信息提取。结果表明:(1)台风靠近台站时地脉动信号增强,登陆后迅速减弱,福州台记录的信号卓越频率要比其他台站高;(2)台风卓越周期与台风强度和台风移动速度的关系比较复杂;(3)用现代滤波器能有效滤除长期存在的干扰信号。

Research on the Characteristics of Typhoon "Matmo" Using Continuous Gravity Data from Fujian Province

Fujian Province is located on the southeast coast of China, and is affected by a number of typhoons between May and October every year. At present, there are three continuous gravity observation stations in Fujian Province(Xiamen, Zhangzhou, and Fuzhou stations) and gPhone gravity meters are used at these stations. The tenth strong typhoon of 2014, which was named "Matmo", crossed the island of Taiwan, landed at the southern coastal town of Gaoshan, Fuqing City, Fujian Province, on July 23 at 15:30, and then weakened into a severe tropical storm. Taking this typhoon as an example, we used the continuous gravity data to study:(1) the rules governing changes in the strength and predominant frequency of the microtremor signal caused by the typhoon;(2) the relationship between the typhoon''s predominant period and its strength and moving speed; and(3) extraction of non-tidal information from the continuous gravity data. The results were as follows:(1) The amplitude of the signal that was recorded by the three stations changed from strong to weak as typhoon "Matmo" moved from close by to far away, and weakened rapidly after landing. This result was consistent with the actual situation. The predominant frequencies with time changed similarly because Xiamen station is relatively close to Zhangzhou station. The closer the distances are, the higher the frequencies are:this may be associated with the signal propagation path and the nature of the medium.(2) The relationship between the typhoon''s predominant period and its strength and moving speed is complex, which may be related to the twisting path and complex terrain.(3) The coastal continuous gravity observation interference can be divided into two categories:long-term and short-term. A Kalman filter can effectively remove the long-standing, more regular interference components. Under normal circumstances the filtering result is almost a straight line, which is useful for judging whether the data are normal.