Research On Remote Sensing Atmospheric Environment Measured By Ground-based Microwave Radiometer

Atmospheric environment, as the information transmitting path for radio system, has great effects on the performance of information systems, therefore remote sensing profiles of atmospheric parameters is very important for evaluating the performance of radio system. Moreover, atmospheric profiles can be applied to weather forecast, and improve its accuracy. The basic principle and retrieving techniques of remote sensing atmospheric profiles by ground-based microwave radiometer are reviewed in this thesis. Research focuses on the retrieving techniques and algorithms of dual-channel radiometer retrieving profiles of atmospheric parameters, integrated precipitable water vapor and integrated liquid water. The main works are:(1) A non-linear regression algorithm based on conventional linear regression algorithm is presented, which improves the accuracy of retrieving profiles by adding non-linear terms. The non-linear regression models are developed based upon the radiosonde data of Qingdao and Beijing. The results of the experiment in Qingdao show that retrieving profiles are identical with radiosonde profiles, and non-linear regression algorithm has better accuracy in retrieving water vapor profile.(2) Artificial neural network algorithm can easily adapt to non-linear problems, therefore a BP network used in radiometer to retrieve atmospheric profiles is introduced, and trained with the historical radiosonde data and its simulating brightness temperatures of Qingdao. The results show that retrieving profiles gained by BP network algorithm have good agreement with radiosonde data, which are also more accuracy than that gained by non-linear algorithm. Meanwhile, 12-channel radiometer retrieval simulation is made with neural network, and the comparison with the retrieving ones by dual-channel radiometer demonstrates that using multi-channel radiometer can improve the accuracy of profiling.(3) Methods to retrieve the integrated precipitable water vapor(V) and integrated liquid water(L) based on dual-channel radiometer are analyzed, and the linear regression models are developed with the simulation results by historical radiosonde data of Qingdao. Experiment demonstrates that these models can be used with good accuracies.(4) The inversion performances of linear regression algorithm, non-linear regression algorithm and neural network algorithm in different months in an average annual year are simulated by utilizing the historical radiosonde data of Qingdao and Beijing, and the simulation results show that the performances of different inversion methods present discrepancies apparently with the variety of weather condition. Therefore the proper selection of the inversion methods in different seasons may improve the retrieval accuracy.The models proposed in this thesis can be applied to remote sensing atmospheric environment by radiometer, which have important reference value for improving the accuracy of remote sensing atmospheric by radiometer.