利用微波辐射传输模型PWR(P. W. Rosenkranz)和反向传播神经网络方法,分别构建了正演下行辐射亮温和反演大气相对湿度廓线的模型,并研究了晴空条件下高光谱微波辐射计反演大气相对湿度廓线的通道选择问题。研究结果表明,200个通道的信息含量大于微波辐射计7个通道的信息含量;增加探测通道数量可提升大气相对湿度廓线的反演精度,选取信息含量排在前面的120个通道进行仿真时,在0~2 km和6~10 km高度范围内大气相对湿度廓线的反演精度提升了4%~10%,在2~6 km高度范围内的相对湿度廓线的反演精度提升了约10%;当通道数继续增加时,反演精度的提升并不明显。

The microwave radiative transfer modeling PWR (P. W. Rosenkranz) and back propagation neural network method are used to construct the models of forward modeling of downward radiance brightness temperature and inversion of atmospheric relative humidity profile respectively, and the channel selection of inversion of atmospheric relative humidity profile by hyperspectral microwave radiometer under clear sky conditions is studied. The research results show that the information content of 200 channels is greater than the information content of 7 channels of microwave radiometer and the increase of the detection channel can improve the inversion accuracy of the atmospheric relative humidity profile. When we use the first 120 channels of information content for simulation experiments, the inversion accuracy of the atmospheric relative humidity profile in the range of 0-2 km and 6-10 km increases by 4%~10%, and the inversion accuracy of the relative humidity profile in the range of 2-6 km increases by about 10%. When the channel continues to increase, the inversion accuracy of atmospheric relative humidity profile not improves much.