期刊文献+

Applying Artificial Neural Networks to Modeling the Middle Atmosphere 被引量:2

Applying Artificial Neural Networks to Modeling the Middle Atmosphere
下载PDF
导出
摘要 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 model's 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. 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 model's 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.
作者 肖存英 胡雄
出处 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2010年第4期883-890,共8页 大气科学进展(英文版)
基金 supported by the National Natural Science Foundation of China under Grant No. 40774087
关键词 artificial neural network middle atmosphere MODELING back-propagation algorithm NRLMSISE- 00 model artificial neural network, middle atmosphere, modeling, back-propagation algorithm, NRLMSISE- 00 model
  • 相关文献

参考文献2

二级参考文献4

共引文献18

同被引文献41

引证文献2

二级引证文献10

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部