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Applied Physics B

, 124:184 | Cite as

An RBF neural network approach for retrieving atmospheric extinction coefficients based on lidar measurements

  • Hongxu Li
  • Jianhua Chang
  • Fan Xu
  • Binggang Liu
  • Zhenxing Liu
  • Lingyan Zhu
  • Zhenbo Yang
Article
  • 49 Downloads

Abstract

Lidar is an effective remote sensing method for obtaining the optical properties of aerosols, such as the aerosol extinction coefficient (AEC), the aerosol optical depth (AOD), and the related atmospheric visibility. However, improving the accuracy and efficiency of lidar data retrieval remains challenging due to the uncertainties associated in determining the AEC boundary value (AEC-BV) and the aerosol extinction-to-backscatter ratio (AEBR), as well as the complex and time-consuming calculations required. In this paper, we propose a novel method, a feedback radial basis function (RBF-FB), for retrieving high-precision AEC profiles based on a radial basis function neural network. First, using the secant method, we determine accurate values for AEC-BV and AEBR, and generate the AEC profiles by the Fernald method. We then choose a set of lidar signals and their corresponding AEC profiles as learning samples for network training and establish an RBF network model for AEC retrieval. Next, we correct the network output by introducing a feedback mechanism that uses the AOD measured by a sun photometer as the error criterion. Tests on measured signals confirm that the outputs of the proposed RBF-FB model are consistent with the Fernald method and have the advantages of speed and robustness.

Notes

Acknowledgements

This study was supported by the National Natural Science Foundation of China (61875089,11374161); the Primary Research & Development Plan of Jiangsu Province, China (BE2016756); the Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions, China (1081080015001), the Top-notch Academic Programs Project of Jiangsu Higher Education Institutions, China (1181081501003).

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Hongxu Li
    • 1
    • 2
  • Jianhua Chang
    • 1
    • 2
  • Fan Xu
    • 2
  • Binggang Liu
    • 2
  • Zhenxing Liu
    • 2
  • Lingyan Zhu
    • 2
  • Zhenbo Yang
    • 2
  1. 1.Collaborative Innovation Center of Atmospheric Environment and Equipment TechnologyNanjing University of Information Science and TechnologyNanjingChina
  2. 2.Jiangsu Key Laboratory of Meteorological Observation and Information ProcessingNanjing University of Information Science and TechnologyNanjingChina

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