GSTDTAP  > 地球科学
DOI10.1016/j.atmosres.2018.06.025
An improved retrieval method of atmospheric parameter profiles based on the BP neural network
Zhao, Yuxin; Zhou, Di; Yan, Hualong
2018-11-15
发表期刊ATMOSPHERIC RESEARCH
ISSN0169-8095
EISSN1873-2895
出版年2018
卷号213页码:389-397
文章类型Article
语种英语
国家Peoples R China
英文摘要

Surface-based microwave radiometer is used to measure the tropospheric parameter profiles continuously for 24 h. The measurement technology and retrieval methods are described clearly in this study. This paper focuses on the BP network and elaborates on it from a new perspective based on the Jacobian matrices between layers. Gradient descent is achieved by Jacobian matrices to train the network. A layered method is proposed to improve the efficiency and accuracy in training networks to obtain tropospheric water vapor and temperature profiles. Differently from the traditional method, the layered method divides the troposphere of 0-10 km into three layers based on the physical principles of cloud generation. Three networks, named as the bottom, the middle, and the upper network, are developed for the three layers. Therefore, three networks can be trained at the same time,using the same input and different output samples. According to the theories and the radiosonde data of 2012-2015 of Harbin China (45.46 degrees N 126.40 degrees E), a numerical experiment is designed to examine the layered method. The downwelling monochromatic radiative transfer model (MonoRTM) is used to calculate the atmospheric radiation brightness temperatures (BTs) with the radiosonde data. The experimental results show that the RMSEs of temperature and water vapor profiles of the layered method are reduced by 25.6% and 26.2%, respectively, at the altitude above 6 km, respectively, and the efficiency is improved by 20 times compared with the traditional method.


英文关键词Artificial neural network Jacobian matrix Layered retrieval method Vertical temperature and water vapor profiles
领域地球科学
收录类别SCI-E
WOS记录号WOS:000442169800033
WOS关键词MICROWAVE RADIOMETER ; PRECIPITATION ; OPTIMIZATION ; TEMPERATURE ; ACCURACY ; SHAPE
WOS类目Meteorology & Atmospheric Sciences
WOS研究方向Meteorology & Atmospheric Sciences
引用统计
文献类型期刊论文
条目标识符http://119.78.100.173/C666/handle/2XK7JSWQ/38619
专题地球科学
作者单位Harbin Engn Univ, Coll Automat, Harbin 150001, Heilongjiang, Peoples R China
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GB/T 7714
Zhao, Yuxin,Zhou, Di,Yan, Hualong. An improved retrieval method of atmospheric parameter profiles based on the BP neural network[J]. ATMOSPHERIC RESEARCH,2018,213:389-397.
APA Zhao, Yuxin,Zhou, Di,&Yan, Hualong.(2018).An improved retrieval method of atmospheric parameter profiles based on the BP neural network.ATMOSPHERIC RESEARCH,213,389-397.
MLA Zhao, Yuxin,et al."An improved retrieval method of atmospheric parameter profiles based on the BP neural network".ATMOSPHERIC RESEARCH 213(2018):389-397.
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