The retrievals and analysis of clear-sky water vapor density in the Arctic regions from MWHS measurements on FY-3A satellite

• Main Authors: He, Jieying, Zhang, Shengwei, Wang, Zhenzhan, He, JY (reprint author), Chinese Acad Sci, Ctr Space Sci & Appl Res, Beijing 100190, Peoples R China.
• Format: Report
• Language: English
• Published: 2012
• Subjects: 微波遥感; Arctic
• Online Access: http://ir.nssc.ac.cn/handle/122/3457

The atmospheric humidity profiles of clear sky in the Arctic regions were retrieved from the microwave humidity sounder on China's FY-3A satellite using the back-propagation neural network algorithm. The algorithm was developed using the reliable measurements of surface temperature, humidity, and pressure as well as atmospheric temperature and humidity profiles from radiosonde observations. Considering the influence of sounding geometry, different surface types, and atmospheric conditions, we improved the commonly used back-propagation artificial neural network by treating the Mexican hat wavelet function as a transfer function and transforming the input data space. The retrieved root-mean-square (RMS) error is about 0.12 g/m(3) in absolute humidity (water vapor density) profiles and 12.7% in relative humidity profiles. Water vapor density retrievals in winter are in acceptable agreement with profiles from radiosonde, but the agreement of the summer data was not as good. Furthermore, the retrieval model has been used in another Arctic station with a mean water vapor density RMS error of 0.185 g/m(3) and 18.3% for a relative humidity profile for all seasons in 2008 at 12:00 UT. The atmospheric humidity profiles of clear sky in the Arctic regions were retrieved from the microwave humidity sounder on China's FY-3A satellite using the back-propagation neural network algorithm. The algorithm was developed using the reliable measurements of surface temperature, humidity, and pressure as well as atmospheric temperature and humidity profiles from radiosonde observations. Considering the influence of sounding geometry, different surface types, and atmospheric conditions, we improved the commonly used back-propagation artificial neural network by treating the Mexican hat wavelet function as a transfer function and transforming the input data space. The retrieved root-mean-square (RMS) error is about 0.12 g/m(3) in absolute humidity (water vapor density) profiles and 12.7% in relative humidity profiles. Water vapor density retrievals in winter are in acceptable agreement with profiles from radiosonde, but the agreement of the summer data was not as good. Furthermore, the retrieval model has been used in another Arctic station with a mean water vapor density RMS error of 0.185 g/m(3) and 18.3% for a relative humidity profile for all seasons in 2008 at 12:00 UT.