Application of the Deep Neural Network in Retrieving the Atmospheric Temperature and Humidity Profiles from the Microwave Humidity and Temperature Sounder Onboard the Feng-Yun-3 Satellite
Application of the Deep Neural Network in Retrieving the Atmospheric Temperature and Humidity Profiles from the Microwave Humidity and Temperature Sounder Onboard the Feng-Yun-3 Satellite
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发表时间：
2021
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通讯作者：
Jiaoyang Li
中科院分区：
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作者：
Qiurui He;Zhenzhan Wang;Jiaoyang Li
The shallow neural network (SNN) is a popular algorithm in atmospheric parameters.retrieval from microwave remote sensing. However, the deep neural network (DNN) has a stronger.nonlinear mapping capability compared to SNN and has great potential for applications in microwave.remote sensing. The Microwave Humidity and Temperature Sounder (Beijing, China, MWHTS).onboard the Fengyun-3 (FY-3) satellite has the ability to independently retrieve atmospheric temperature.and humidity profiles. A study on the application of DNN in retrieving atmospheric temperature.and humidity profiles from MWHTS was carried out. Three retrieval schemes of atmospheric parameters.in microwave remote sensing based on DNN were performed in the study of bias correction.of MWHTS observation and the retrieval of the atmospheric temperature and humidity profiles.using MWHTS observations. The experimental results show that, compared with SNN, DNN can.obtain better bias-correction results when applied to MWHTS observation, and can obtain higher.retrieval accuracy of temperature and humidity profiles in all three retrieval schemes. Meanwhile,.DNN shows higher stability than SNN when applied to the retrieval of temperature and humidity.profiles. The comparative study of DNN and SNN applied in different atmospheric parameter.retrieval schemes shows that DNN has a more superior performance.
