利用美国海军研究实验室在MSIS 系列模型基础上发展的大气模型NRLMSISE-00,模拟研究了2001 年-2013 年中国上空过渡流区80～140 km 高度的大气状态.对于中国东部和中部过渡流区,基于模拟数据得到的月平均结果显示大气密度和温度呈现-致...利用美国海军研究实验室在MSIS 系列模型基础上发展的大气模型NRLMSISE-00,模拟研究了2001 年-2013 年中国上空过渡流区80～140 km 高度的大气状态.对于中国东部和中部过渡流区,基于模拟数据得到的月平均结果显示大气密度和温度呈现-致的变化趋势,还表现出与太阳活动的显著关联.谱分析的结果显示,在90 km 高度以上大气密度呈现半年周期变化,在90～110 km 高度范围这种半年变化的幅度随纬度增大.在100 km 高度,上半年出现的半年周期过程中密度的最大、最小值分别出现在3 月和6 月,明显超前中、高热层的半年周期过程.展开更多
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-propag...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.展开更多
文摘利用美国海军研究实验室在MSIS 系列模型基础上发展的大气模型NRLMSISE-00,模拟研究了2001 年-2013 年中国上空过渡流区80～140 km 高度的大气状态.对于中国东部和中部过渡流区,基于模拟数据得到的月平均结果显示大气密度和温度呈现-致的变化趋势,还表现出与太阳活动的显著关联.谱分析的结果显示,在90 km 高度以上大气密度呈现半年周期变化,在90～110 km 高度范围这种半年变化的幅度随纬度增大.在100 km 高度,上半年出现的半年周期过程中密度的最大、最小值分别出现在3 月和6 月,明显超前中、高热层的半年周期过程.
基金supported by the National Natural Science Foundation of China under Grant No. 40774087
文摘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.
