Because of the importance of gravity waves （GWs） in coupling different atmospheric regions, further studies are necessary to investigate the characteristics of GW propagation in a non-isothermal atmosphere. Using a nonlinear numerical model, we simulate the propagation of small amplitude GWs with various wavelengths in different non-isothermal atmospheres. Our re- sults show that the GW vertical wavelength undergoes sharp changes above the stratopause and mesopause region. Specifically for a GW with an initial vertical wavelength of 5 km, the seasonal background temperature structure difference at 50° latitude can cause the vertical wavelength to vary by -2 krn in the mesosphere and by as large as -4.5 km in the lower thermosphere. In addition, the GW paths exhibit great divergence in the height range of -65-110 kin. Our results also show that the variations of GW path, vertical wavelength and horizontal phase velocity are not synchronized in a non-isothermal atmosphere as in an isothermal atmosphere. Despite the fact that all GWs change their characteristics as they propagate upward in a non-isothermal atmosphere, the variations relative to the initial parameters at a reference height are similar for different initial vertical wavelengths. Our results indicate that the changing characteristics of a gravity wave in a non-isothermal atmosphere need to be considered when investigating the relationship of GWs at two different heights.
针对小波分析在处理多维图形时不能充分利用数据本身特有几何特征的缺陷,使用第二代曲波变换(the second generation of curvelet transform,SGCT)方法进行人脸图像的处理,选取具有最大标准差的尺度层系数,以完成对人脸图像的特征提取,同时结合基于双向二维主成分分析(bidirectional two dimensional principal component analysis,B2DPCA)的数据降维,构造一种基于混合投票机制极限学习机(voting extreme learning machine,VELM)的人脸识别算法.通过与其他算法的分类结果对比,证明该算法具有更高的识别正确率.
