云对地基微波辐射计反演湿度廓线的影响

摘要

There are a lot of limitations on measurement accuracy,cost and continuity of time in the meteorologi-cal sounding operations,which are two or four times a day.In order to obtain continuous atmospheric pro-file data,many methods are developed,among which the way of measuring atmospheric temperature and humidity profiles by the microwave radiometer is relatively mature.However,the ability of the microwave radiometer with infrared sensors is very limited in measuring the cloud,it can only get the height of cloud, and sometimes it brings large deviations.The deviation result in great uncertainty in distributed cloud mi-crowave absorption,causing errors during the inversion of temperature and humidity profiles,so how to improve the accuracy of inversion on cloud is an urgent problem to solve.A method is implemented using atmospheric profiles from L-band sounding radar and brightness temperature observed with microwave ra-diometer,and MonoRTM is taken as a forward atmospheric radiative transfer model and the tool of re-trieval is BP neural network.The matching cloud information is added and a new model of retrieval is crea-ted when retrieving atmospheric humidity profiles.Root mean square error (RMSE)values on each height layer with two kinds of inversion method are obtained and the impact of cloud information on atmospheric humidity profile retrieval is analyzed through comparison. Results show that the average of correlation between inversion humidity profiles is improved from 0.6850 to 0.8050 after adding cloud information.Compared with inversion profiles without cloud informa-tion,RMSE values on the vast majority of height layers after adding cloud information are reduced to vari-ous degrees,which is particularly obvious at layers with cloud. The study shows that the method of adding cloud information on the process of inversion is feasible. In order to improve the ability to observe the atmospheric profile lines in cloudy days,combined informa-tion of cloud distribution and brightness temperature of microwave radiation can be used to retrieve the temperature and humidity,in condition the joint observation of cloud radar and microwave radiation is available.%利用中国气象局大气探测试验基地的 L 波段探空数据和微波辐射计观测数据，采用 MonoRTM 辐射传输模型作为正演亮温模型，BP（back propagation）神经网络作为反演工具，在由亮温反演大气湿度廓线的过程中，添加与样本匹配的云底高度和云厚度信息，建立新的反演模型，使新反演模型得到的反演湿度廓线和未添加云信息的反演湿度廓线分别与探空数据进行对比，获取两种反演方法各高度层的均方根误差，分析云信息对反演大气湿度廓线的影响。对比结果表明：未添加云信息时，测试样本的反演湿度廓线与探空廓线的相关系数平均值为0．685，而添加云信息后，相关系数平均值为0．805。相比未添加云信息的反演廓线，添加云信息之后多数高度层的均方根误差均有不同程度减小，而在有云以上高度层表现尤为明显。