A Comparison of Physical-Based and Statistical-Based Radiative Transfer Models in Retrieving Atmospheric Temperature Profiles from the Microwave Temperature Sounder-II Onboard the Feng-Yun-3 Satellite
The statistical retrieval of atmospheric parameters will be greatly affected by the accuracy of the simulated brightness temperatures (BTs) derived from the radiative transfer model. However, it is challenging to further improve a physical-based radiative transfer model (RTM) developed based on the...
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2025-01-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/16/1/44 |
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| author | Qiurui He Xiao Guo Ruiling Zhang Jiaoyang Li Lanjie Zhang Junqi Jia Xuhui Zhou |
| author_facet | Qiurui He Xiao Guo Ruiling Zhang Jiaoyang Li Lanjie Zhang Junqi Jia Xuhui Zhou |
| author_sort | Qiurui He |
| collection | DOAJ |
| description | The statistical retrieval of atmospheric parameters will be greatly affected by the accuracy of the simulated brightness temperatures (BTs) derived from the radiative transfer model. However, it is challenging to further improve a physical-based radiative transfer model (RTM) developed based on the physical mechanisms of wave transmission through the atmosphere. We develop a deep neural network-based RTM (DNN-based RTM) to calculate the simulated BTs for the Microwave Temperature Sounder-II onboard the Fengyun-3D satellite under different weather conditions. The DNN-based RTM is compared in detail with the physical-based RTM in retrieving the atmospheric temperature profiles by the statistical retrieval scheme. Compared to the physical-based RTM, the DNN-based RTM can obtain higher accuracy for simulated BTs and enables the statistical retrieval scheme to achieve higher accuracy in temperature profile retrieval in clear, cloudy, and rainy sky conditions. Due to its ability to simulate microwave observations more accurately, the DNN-based RTM is valuable for the theoretical study of microwave remote sensing and the application of passive microwave observations. |
| format | Article |
| id | doaj-art-014ff681b76c40b39ea1440c312c2c08 |
| institution | Kabale University |
| issn | 2073-4433 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj-art-014ff681b76c40b39ea1440c312c2c082025-01-24T13:21:49ZengMDPI AGAtmosphere2073-44332025-01-011614410.3390/atmos16010044A Comparison of Physical-Based and Statistical-Based Radiative Transfer Models in Retrieving Atmospheric Temperature Profiles from the Microwave Temperature Sounder-II Onboard the Feng-Yun-3 SatelliteQiurui He0Xiao Guo1Ruiling Zhang2Jiaoyang Li3Lanjie Zhang4Junqi Jia5Xuhui Zhou6School of Information Technology, Luoyang Normal University, Luoyang 471934, ChinaHebi Institute of Engineering and Technology, Henan Polytechnic University, Hebi 458030, ChinaSchool of Information Technology, Luoyang Normal University, Luoyang 471934, ChinaSchool of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, ChinaSchool of Information and Communication Engineering, Beijing Information Science and Technology University, Beijing 100101, ChinaSchool of Information Technology, Luoyang Normal University, Luoyang 471934, ChinaSchool of Information Technology, Luoyang Normal University, Luoyang 471934, ChinaThe statistical retrieval of atmospheric parameters will be greatly affected by the accuracy of the simulated brightness temperatures (BTs) derived from the radiative transfer model. However, it is challenging to further improve a physical-based radiative transfer model (RTM) developed based on the physical mechanisms of wave transmission through the atmosphere. We develop a deep neural network-based RTM (DNN-based RTM) to calculate the simulated BTs for the Microwave Temperature Sounder-II onboard the Fengyun-3D satellite under different weather conditions. The DNN-based RTM is compared in detail with the physical-based RTM in retrieving the atmospheric temperature profiles by the statistical retrieval scheme. Compared to the physical-based RTM, the DNN-based RTM can obtain higher accuracy for simulated BTs and enables the statistical retrieval scheme to achieve higher accuracy in temperature profile retrieval in clear, cloudy, and rainy sky conditions. Due to its ability to simulate microwave observations more accurately, the DNN-based RTM is valuable for the theoretical study of microwave remote sensing and the application of passive microwave observations.https://www.mdpi.com/2073-4433/16/1/44statistical retrievalphysical-based radiative transfer modelstatistical-based radiative transfer modeldeep neural networkatmospheric temperature profile |
| spellingShingle | Qiurui He Xiao Guo Ruiling Zhang Jiaoyang Li Lanjie Zhang Junqi Jia Xuhui Zhou A Comparison of Physical-Based and Statistical-Based Radiative Transfer Models in Retrieving Atmospheric Temperature Profiles from the Microwave Temperature Sounder-II Onboard the Feng-Yun-3 Satellite Atmosphere statistical retrieval physical-based radiative transfer model statistical-based radiative transfer model deep neural network atmospheric temperature profile |
| title | A Comparison of Physical-Based and Statistical-Based Radiative Transfer Models in Retrieving Atmospheric Temperature Profiles from the Microwave Temperature Sounder-II Onboard the Feng-Yun-3 Satellite |
| title_full | A Comparison of Physical-Based and Statistical-Based Radiative Transfer Models in Retrieving Atmospheric Temperature Profiles from the Microwave Temperature Sounder-II Onboard the Feng-Yun-3 Satellite |
| title_fullStr | A Comparison of Physical-Based and Statistical-Based Radiative Transfer Models in Retrieving Atmospheric Temperature Profiles from the Microwave Temperature Sounder-II Onboard the Feng-Yun-3 Satellite |
| title_full_unstemmed | A Comparison of Physical-Based and Statistical-Based Radiative Transfer Models in Retrieving Atmospheric Temperature Profiles from the Microwave Temperature Sounder-II Onboard the Feng-Yun-3 Satellite |
| title_short | A Comparison of Physical-Based and Statistical-Based Radiative Transfer Models in Retrieving Atmospheric Temperature Profiles from the Microwave Temperature Sounder-II Onboard the Feng-Yun-3 Satellite |
| title_sort | comparison of physical based and statistical based radiative transfer models in retrieving atmospheric temperature profiles from the microwave temperature sounder ii onboard the feng yun 3 satellite |
| topic | statistical retrieval physical-based radiative transfer model statistical-based radiative transfer model deep neural network atmospheric temperature profile |
| url | https://www.mdpi.com/2073-4433/16/1/44 |
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