摘要
讨论了12通道地基微波辐射计遥感反演温度、相对湿度和云液态水廓线的BP神经网络反演方法,利用探空资料,对北京春、夏、秋、冬四个季节的大气廓线进行神经网络训练,并对训练好的网络的反演能力进行数值检验,分析了反演精度;对北京南郊观象台12通道微波辐射计的观测亮温资料进行实际反演,结果表明,神经网络(BPNN)反演的廓线与微波辐射计自带RadiomeNN的相比更加接近实际。
It is discussed that the method of remote sensing retrieval of temperature,relative humidity and cloud liquid water profiles from 12-channel ground-based microwave radiometer by BP neural network,which is on the basis of sounding data,training the neural network with atmospheric profiles in four seasons,testing and analizing the accuracy of the network.Finally,the results of retrieval with southern suburb of Beijing of 12-channel microwave radiometer data show that： With only a few training cases,the retrieval method BPNN referred in this article is more realistic than that of the microwave radiometer.
出处
《高原气象》
CSCD
北大核心
2010年第6期1514-1523,共10页
Plateau Meteorology
基金
国家自然科学基金项目(40905018/D0503)资助
关键词
微波辐射计
BP神经网络
温度廓线
相对湿度廓线
云液态水廓线
Microwave radiometer
BP neural network
Temperature profile
Relative humidity profile
Cloud liquid water profile
