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Lightning Prediction by Wavelet Analysis of Atmospheric Pressure, Wind Speed and Electrostatic Field Signals

通过大气压、风速和静电场信号的小波分析进行闪电预测

基本信息

批准号：
04650231
负责人：
TAKEUCHI Nobunao
金额：
$ 1.09万
依托单位：
Tohoku University
依托单位国家：
日本
项目类别：
Grant-in-Aid for General Scientific Research (C)
财政年份：
1992
资助国家：
日本
起止时间：
1992 至 1993
项目状态：
已结题

项目摘要

To investigate a winter thundercloud structure, we have bee acquiring data of the electrostatic field and the meteorological variables (atmospheric pressure and wind speed) over a full winter season.These obtained data have been investigating by a wavelet transform method that is a mathematical technique introduced recently for analyzing seismic and acoustic signals.Microscopic variations in time series of the pressure and the wind speed are extracted from not-processed ones using a moving average method, then analyzed by a wavelet method. For each variable, wavelet transform coefficients (modules) are displayd using gray scales as its grade. These figures provide a clearly interpretable visual representation of time series signals. Namely, it is easily understood what the significant scales exist at the concerned time.Mutual correlation coefficients of modules between the pressure and the wind speed variation are also calculated to clarify how these two variables are relted each other. A scale parameter and a phase shift in time are adopted as two dimensional coordinates in the same manner as the wavelet transform. This figure shows whether there is a strong correlation between two elements or not, and if it was, its time and/or position.When thunderclouds pass nearby the observation station, characteristic oscilations are detected in a pressure variation. A strong correlation between the pressure and the wind speed has been proved by a method of a wavelet transform analysis. On the other hand, when a climate is quiet, there is no correlation.A correlation between the presuure variation and the electrostatic fiield at the ground has been also clarified.

为了研究冬季雷暴云的结构，我们正在收集静电场和气象变量的数据本文利用小波变换方法分析了整个冬季的气压和风速的时间序列，并从非时间序列中提取了气压和风速时间序列的微观变化。用移动平均法处理，然后用小波方法分析。对于每一个变量，小波变换系数（模）显示使用灰度作为其等级。这些图提供了时间序列信号的清晰可解释的视觉表示。计算了气压和风速变化的模的相关系数，阐明了气压和风速变化之间的相互关系。以与小波变换相同的方式采用尺度参数和时间上的相移作为二维坐标。该图显示了两个要素之间是否存在强相关，如果存在，则显示其时间和/或位置。当雷暴云经过观测站附近时，在气压变化中检测到特征振荡。用小波变换分析的方法证明了风压和风速之间的强相关性。当气候平静时，气压变化与地面静电场之间没有相关性，而气压变化与地面静电场之间存在相关性。