DOI: 10.1016/j.atmosres.2018.06.025
Scopus记录号: 2-s2.0-85049860422
论文题名: An improved retrieval method of atmospheric parameter profiles based on the BP neural network
作者: Zhao Y. ; Zhou D. ; Yan H.
刊名: Atmospheric Research
ISSN: 1698095
出版年: 2018
卷: 213 起始页码: 389
结束页码: 397
语种: 英语
英文关键词: Artificial neural network
; Jacobian matrix
; Layered retrieval method
; Vertical temperature and water vapor profiles
Scopus关键词: Atmospheric radiation
; Atmospheric temperature
; Efficiency
; Information retrieval
; Microwave devices
; Neural networks
; Numerical methods
; Radiative transfer
; Radiosondes
; Troposphere
; Water vapor
; Atmospheric parameters
; Measurement technologies
; Microwave radiometers
; Numerical experiments
; Radiative transfer model
; Retrieval methods
; Temperature profiles
; Water vapor profile
; Jacobian matrices
; artificial neural network
; brightness temperature
; parameterization
; radiative transfer
; radiometer
; radiosonde
; troposphere
; water vapor
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°N 126.40°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. © 2018 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/108767
Appears in Collections: 影响、适应和脆弱性 气候变化事实与影响
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作者单位: College of Automation, Harbin Engineering University, Harbin, 150001, China; College of Automation, Harbin Engineering University, Harbin, 150001, China
Recommended Citation:
Zhao Y.,Zhou D.,Yan H.. An improved retrieval method of atmospheric parameter profiles based on the BP neural network[J]. Atmospheric Research,2018-01-01,213