Retrieval of water vapor profiles with radio occultation measurements using an artificial neural network
- 126 Downloads
A new method applying an artificial neural network (ANN) to retrieve water vapor profiles in the troposphere is presented. In this paper, a fully-connected, three-layer network based on the back-propagation algorithm is constructed. Month, latitude, altitude and bending angle are chosen as the input vectors and water vapor pressure as the output vector. There are 130 groups of occultation measurements from June to November 2002 in the dataset. Seventy pairs of bending angles and water vapor pressure profiles are used to train the ANN, and the sixty remaining pairs of profiles are applied to the validation of the retrieval. By comparing the retrieved profiles with the corresponding ones from the Information System and Data Center of the Challenging Mini-Satellite Payload for Geoscientific Research and Application (CHAMP-ISDC), it can be concluded that the ANN is relatively convenient and accurate. Its results can be provided as the first guess for the iterative methods or the non-linear optimal estimation inverse method.
Key wordsradio occultation water vapor artificial neural network back-propagation
Unable to display preview. Download preview PDF.
- Engeln, A. V., G. Nedoluha, G. Kirchengast, and S. Büler, 2003: One-dimensional variational (1-D Var) retrieval of temperature, water vapor, and a reference pressure from radio occultation measurements: A sensitivity analysis.J. Geophys. Res.,108(D11), 4337, doi: 10.1029/2002JD002908.CrossRefGoogle Scholar
- Healy, S. B., 1998: A statistical comparison of GPS/MET radio occultation data with numerical weather prediction analyses, UKMO Tech. Memo, No. 247, 44pp.Google Scholar
- Kursinski, E. R., G. A. Hajj, S. S. Leroy, and B. Herman, 2000: The GPS radio occultation technique.Terrestrial, Atmospheric and Oceanic Sciences,11, 53–114.Google Scholar
- Høeg, P., and Coauthors, 2001: Review of water vapour retrieval and analysis of observations. [Available from Danish Meteorological Institute, Copenhagen, Danemark].Google Scholar
- Wang Xin, Xue Zhengang, and Du Xiaoyong, 2003: Inversion of atmospheric water vapour using radio occultation data.Chinese Journal of Radio Science,18(4), 462–465. (in Chinese)Google Scholar
- Yunck, T. P., 2002: An overview of atmospheric radio occultation.Journal of Global Positioning Systems,1(1), 58–60.Google Scholar
- Yang Jiangang, and Coauthors, 2001:The Teaching Material of the Artificial Neural Network. Zhejiang University Press, 41–45. (in Chinese)Google Scholar