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Applying Artificial Neural Networks to Modeling the Middle Atmosphere


doi: 10.1007/s00376-009-9019-1

  • An artificial neural network (ANN) is used to model the middle atmosphere using a large number of TIMED/SABER limb sounding temperature profiles. A three-layer feed-forward network is chosen based on the back-propagation (BP) algorithm. Latitude, longitude, and height are chosen as the input vectors of the network while temperature is the output vector. The temperature observations during the period from 13 January through 16 March 2007, which are in the same satellite yaw, are taken as samples to train an ANN. Results suggest that the network has high quality for modeling spatial variations of temperature. Quantitative comparisons between the ANN outputs and those from the popular empirical NRLMSISE-00 model illustrate their generally consistent features and some specific differences. The NRLMSISE-00 models zonal mean temperatures are too high by ~6 K--10 K near the stratopause, and the amplitude and phase of the planetary wave number 1 activity are different in some respects from the ANN simulations above 45--50 km, suggesting improvement is needed in the NRLMSISE-00 model for more accurate simulation near and above the stratopause.
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    [7] REN Liliang, LI Chunhong, WANG Meirong, 2003: Application of Radar-Measured Rain Data in Hydrological Processes Modeling during the Intensified Observation Period of HUBEX, ADVANCES IN ATMOSPHERIC SCIENCES, 20, 205-211.  doi: 10.1007/s00376-003-0005-8
    [8] Wang Shaowu, Cai Jingning, Mu Qiaozhen, Xie Zhihui, Zhu Jinhong, Gong Daoyi, 2002: Modeling and Diagnostic Studies on the Variations of the Subtropical High over the Western Pacific from 1880 to 1999, ADVANCES IN ATMOSPHERIC SCIENCES, 19, 1148-1152.  doi: 10.1007/s00376-002-0072-2
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    [10] Lu ZHOU, Rong-Hua ZHANG, 2022: A Hybrid Neural Network Model for ENSO Prediction in Combination with Principal Oscillation Pattern Analyses, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 889-902.  doi: 10.1007/s00376-021-1368-4
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Manuscript History

Manuscript received: 10 July 2010
Manuscript revised: 10 July 2010
通讯作者: 陈斌, bchen63@163.com
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Applying Artificial Neural Networks to Modeling the Middle Atmosphere

  • 1. Center for Space Science and Applied Research, Chinese Academy of Sciences, Beijing 100190, Graduate University of Chinese Academy of Sciences, Beijing 100049,Center for Space Science and Applied Research, Chinese Academy of Sciences, Beijing 100190

Abstract: An artificial neural network (ANN) is used to model the middle atmosphere using a large number of TIMED/SABER limb sounding temperature profiles. A three-layer feed-forward network is chosen based on the back-propagation (BP) algorithm. Latitude, longitude, and height are chosen as the input vectors of the network while temperature is the output vector. The temperature observations during the period from 13 January through 16 March 2007, which are in the same satellite yaw, are taken as samples to train an ANN. Results suggest that the network has high quality for modeling spatial variations of temperature. Quantitative comparisons between the ANN outputs and those from the popular empirical NRLMSISE-00 model illustrate their generally consistent features and some specific differences. The NRLMSISE-00 models zonal mean temperatures are too high by ~6 K--10 K near the stratopause, and the amplitude and phase of the planetary wave number 1 activity are different in some respects from the ANN simulations above 45--50 km, suggesting improvement is needed in the NRLMSISE-00 model for more accurate simulation near and above the stratopause.

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