摘要
由于天气受到多种因素综合影响,具有时变性和不确定性,大气电场对气溶胶含量、水汽含量、云量、温度等要素的变化有着敏锐的反应,不同的天气条件下大气电场呈现出不同的特性。为提高天气识别准确率,提出一种利用大气电场特征的天气识别算法。首先利用统计学和小波能量谱分析方法,提取大气电场的时幅域、频域特征,然后进行归一化处理,最后采用BP神经网络技术对特征进行训练,建立天气现象识别模型。实验结果表明,大气电场的特征,有助于了解大气电场与气候变化之间的关系,可对晴天、阴天、雨天和雷暴等典型天气进行识别,提高了天气现象自动化观测水平。
The weather which is affected by many factors has changeable and uncertain, Aerosol content, moisture content, cloud cover, temperature, and other factors have a keen effect on the atmospheric electric field. Under differ- ent weather conditions, atmospheric electric field exhibit different characteristics. A weather phenomenon recognition algorithm is put forward based on the characteristics of the atmospheric electric field. Atmospheric electric field am- plitude domain,frequency domain characteristics are extracted by the use of statistical methods and wavelet energy spectrum analysis and then normalized, and finally are trained by using BP neural network technology features,weath- er phenomena recognition model is established. Experimental results show that characteristics of atmospheric electric field are helpful to understand the relationship between climate change and atmospheric electric field. The algorithm can achieve the recognition of sunny, cloudy, rainy and thunderstorms weather phenomena. These works are of great significance to promote the automatic ground meteorological observation of all elements.
出处
《计算机仿真》
CSCD
北大核心
2014年第12期312-315,324,共5页
Computer Simulation
基金
国家高技术研究发展计划(863计划)(2011AA7033045)
关键词
大气电场
天气识别
小波能量谱
神经网络
Atmospheric electric field
Weather identification
Wavelet energy spectrum
Neural network
