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《Journal of Southwest University(Natural Science Edition)》 2019-01
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Research of an Inversion Method Based on Pseudoinverse Learning Algorithm for Ground-Based Microwave Radiometer Measurement

FAN Xu;MAO Wen-qian;WU Xiao-yan;QU Zong-xi;ZHANG Bei-dou;ZHANG Wen-yu;College of Atmospheric Sciences,Lanzhou University/Key Laboratory for Semi-Arid Climate Change of the Ministry of Education;  
In order to estimate the application of pseudoinverse learning algorithm in the localization of ground-based microwave radiometer meteorological elements inversion algorithms,the observed data of the ground-based microwave radiometer from the Semi-Arid Climate and Environment Observatory(SACOL)of Lanzhou University and the radiosonde data from the Yuzhong Station during 2009 to 2010are used to establish a neural network(PIFN)for temperature,humidity and water vapor density inversion based on the pseudoinverse learning algorithm(PLA),and the inversion results are compared with the products of the ground-based microwave radiometers.The result show that the maximum mean square root error of temperature,relative humidity and water vapor density inversed by PIFN are 6.41 K,31.21% and 1.5g/m3,respectively,and the maximum root mean squared root error of temperature,relative humidity,and water vapor density products recorded by the ground-based microwave radiometers are11.93 K,53.18%and 3.06g/m3,respectively.PIFN significantly improves the inversion performance of temperature,relative humidity and water vapor density profiles between 2~10km,0~3km,1~7km,respectively.It is concluded that the inversion result of PLFN has a better performance than the microwave radiometer's own products and is more close to the radiosonde data and that PLA can be introduced to ground-based microwave radiometer inversion algorithm localization field.
【Fund】: 国家自然科学基金项目(41741023);; 兰州大学中央高校基本科研业务费专项资金(lzujbky-2017-57)
【CateGory Index】: P407.7
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