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气象:2009,35(11):137-142
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利用人工神经网络方法反演大气温度廓线
(1.南京信息工程大学,南京 210044;2.浙江省嘉兴市气象台)
Retrieving Atmospheric Temperature Profiles Using Artificial Neural Network Approach
(1.NUIST, Nanjing 210044;2.Jiaxing Meteorological Office of Zhejiang Province)
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投稿时间:2008-05-12    修订日期:2009-07-14
中文摘要: 高光谱大气红外探测仪AIRS(Atmospheric Infrared Sounder)资料能够显示小尺 度的大气温度垂直结构,为数值预报和天气诊断提供了更加准确精细的初始场。目前 处理数据主要使用晴空大气业务反演国际MODIS/AIRS处理软件包IMAPP (International MOD IS/AIRS Preprocessing Package)中的特征向量统计回归算法,由于统计法算法简单,反演 精度受到较大限制。现提出一种利用人工神经网络的算法来对晴空状况下AIRS模拟辐 射值进 行大气温度廓线反演的方法,并与特征向量统计法结果相比较。结果表明,神经网络方法与 特征向量统计法反演所耗时间相当,减小了反演误差,各高度层温度反演精度均有不同程度 的改进,获得了较好的反演结果。
Abstract:High spectral resolution Atmospheric Infrared Sounder (AIRS) data can be used to retrieve the small scale vertical structure of air temperature, which provid ed a more accurate and fine initial field for the numerical forecasting and the large scale weather analysis. In the previous studies, eigenvector regression algorithm in the IMAPP (International MODIS/AIRS Preprocessing Package) was ofte n used to process the data. Because of its simplicity, the inversion precision w as limited. Applying an artificial neural network to retrieve the clear sky atm ospheric temperature profiles from AIRS simulation radiation data and comparing with the eigenvector regression algorithm, the results indicate that the neural network consumed a same time as the eigenvector regression algorithm, but it red uced the atmospheric inversion error and made improvements in temperature measur ements at various levels. 
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基金项目:国家自然科学基金项目(40605009)
引用文本:
张雪慧,官莉,王振会,韩静,2009.利用人工神经网络方法反演大气温度廓线[J].气象,35(11):137-142.
Zhang Xuehui,Guan Li,Wang Zhenhui,Han Jing,2009.Retrieving Atmospheric Temperature Profiles Using Artificial Neural Network Approach[J].Meteor Mon,35(11):137-142.