基于星载红外高光谱观测用机器学习算法反演大气温湿廓线

星载红外高光谱垂直探测仪GIIRS （Geostationary Interferometric Infrared Sounder）能够实现大气温度和湿度参数高垂直分辨率的观测，为数值天气预报提供精度更高的初始场。基于GIIRS观测辐射值采用BP神经网络（Back Propagation Neural Network）法和深度学习的卷积神经网络（Convolutional Neural Networks, CNN）法反演大气温度、湿度垂直廓线，重点在于CNN法模型的构建与参数的优化，得到反演精度最高的网络模型配置。将训练样本根据不同地表类型和是否有云的影响分为三种方案（方案一:不分类、方案二:陆地/洋面分类、方案三:晴空/有云分类），分别进行建模、反演和检验。结果表明两种反演算法都有较好的反演精度，相对而言CNN法在所有高度层上反演偏差、均方根误差和平均相对误差均较小，反演精度更高。CNN法温度反演在高层10~200 hPa改进较大，三种分类方案改进的最大值分别为1.15 K、1.06 K和1.02 K；湿度反演在对流层低层500~1000 hPa改进较大，三种分类方案分别平均改进了0.43 g/kg、0.41 g/kg和0.34 g/kg。BP神经网络法方案三时（即分晴空和云时）温度和水汽混合比廓线反演精度最好；CNN算法方案一时（即不对样本数据进行任何分类）反演精度最高。

Atmospheric temperature and humidity profile retrievals using a machine learning algorithm based on satellite-based infrared hyperspectral observations

The satellite-based infrared hyperspectral Geostationary Interferometric Infrared Sounder (GIIRS) can achieve high vertical resolution observations of atmospheric temperature and humidity parameters, which provide a more accurate initial field for numerical weather forecasting. Based on GIIRS observation radiation, a back propagation (BP) neural network and deep learning convolutional neural networks (CNNs) are used to retrieve atmospheric temperature and humidity profiles, and the focus is on the construction of the CNN model and the optimization of parameters, thus obtaining the network model configuration with the highest retrieval accuracy. The training samples are divided into three schemes according to different surface types and the influence of whether there are clouds (scheme 1: no classification, scheme 2: land or ocean surface, scheme 3: clear or clouds) and modelling, retrieving and testing. The results show that the two retrieval algorithms both have good retrieval precision. Relatively speaking, the CNN method has a smaller retrieval bias, root-mean-square error and mean relative error at all altitudes, and the retrieval precision is higher. The temperature retrieval of the CNN method is greatly improved in the high level at 10-200 hPa, and the maximum values of the three classification schemes are 1.15 K, 1.06 K, and 1.02 K, respectively, and the humidity retrieval of the CNN method also shows improvement in the lower troposphere at 500-1000 hPa, and the averages of the three classification schemes are 0.43 g/kg, 0.41 g/kg, and 0.34 g/kg, respectively. The third scheme (clear or clouds) of the BP neural network method has the best retrieval precision of temperature and water vapour mixing ratio profiles, and the first scheme (no classification of sample data) of the CNN algorithm has the most accurate retrieval results.