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Remote Sensing Technology and Application
    
Consistency Analysis of Experimental and Simulated Brightness Temperature based on Ground-based Microwave Radiometer and Cloud Detection
Ma Lina1,2,Li Qing1,2,Jiang Sulin1,2,Lei Lianfa3,Li Dongshuai4,Wang Zhenhui1,2
(1.Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters,China Meteorlogical Administration CMA Key Laboratory of Aerosol-Cloud-Precipitation,Nanjing University of Information Science & Technology,Nanjing 210044,China;2.School of Atmospheric Physics,Nanjing University of Information Science & Technology,Nanjing 210044,China;3.Xi’an Electronic Engineering Research Institute,Xi’an 710000,China;
4.Earth System Simulation Center,Nanjing University of Information Science &Technology,Nanjing 210044,China)
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Abstract  In order to estimate the working state of the radiometry channels by using “clear sky” samples and to find out the correction relationship for each channel to further investigate the “cloud” samples,we firstly make a consistency analysis of observed brightness temperature data (T BM) obtained from Microwave Radiometer located in Conghua district of Guangzhou province and the simulated brightness temperature data (T BC) calculated by using NCEP atmospheric profile data based on the radiative transfer equation.Then,the “clear sky” samples and "cloud" samples (including few ambiguous data) are identified using multi-channel brightness temperature difference threshold method.Finally,the linear correction for the "clear sky" samples based on the regression analysis are used to establish the correction relationship between T BM and T BC to estimate the working state of the ground-based microwave radiometer.According to the comparison of the time series between the cloud bottom height data of radiometer and the corrected brightness temperature,it is proved that the multi-channel brightness temperature difference threshold method is good to identify the clear sky.The corrected “clear sky” samples can be directly used in the subsequent air temperature and humidity inversion process,and the identified “cloud” samples (including rain samples) provide a base for the subsequent cloud parameter inverse algorithm and the cloud effect correction of the brightness temperature data,which can be further used in the subsequent inverse of the air temperature and humidity for all the samples.


Key words:  Ground-based microwave radiometer      Regression analysis      Linear correction     
Received:  02 March 2016      Published:  16 March 2018
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Ma Lina
Li Qing
Jiang Sulin

Cite this article: 

Ma Lina,Li Qing,Jiang Sulin. Consistency Analysis of Experimental and Simulated Brightness Temperature based on Ground-based Microwave Radiometer and Cloud Detection. Remote Sensing Technology and Application, 2018, 33(1): 68-77.

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http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2018.1.0068     OR     http://www.rsta.ac.cn/EN/Y2018/V33/I1/68

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