Abstract

It is shown that the a priori data on the atmospheric conditions, in addition to the climate’s characteristics, can enhance the efficiency of the algorithms for reconstructing the atmospheric profiles using satellite microwave radiometric observations. Such additional data are sought and their efficiency in remote sensing is estimated. The possibility of expanding the statistical approach by including new types of a priori data on the temperature and humidity of atmospheric conditions is discussed. It is demonstrated that the developed technique can be used to estimate the efficiency of using the following types of additional a priori data in the statistical regularization method: (i) the covariance matrix of the full vector of temperature and humidity variations within a vertical atmospheric column, (ii) the covariance matrix of the temperature and humidity variations within horizontal atmospheric strata, (iii) physical limits of the humidity variation amplitude, and (iv) statistically average model representations of the microwave radiation transfer parameters in the cloud layer.

中文翻译：

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利用卫星微波光谱仪的数据提高重建云层温度和湿度剖面的效率

摘要

结果表明，关于大气状况的先验数据，除了气候的特征之外，还可以提高使用卫星微波辐射观测数据重建大气廓线的算法的效率。寻找此类附加数据，并估计其在遥感中的效率。讨论了通过包括关于大气条件的温度和湿度的新型先验数据来扩展统计方法的可能性。结果表明，所开发的技术可用于估计在统计正则化方法中使用以下类型的其他先验数据的效率：（i）垂直大气柱内温度和湿度变化的完整矢量的协方差矩阵，