The Robustness of an Anti-Noise BP Neural Network Inversion Algorithm for Ground-Based Microwave Radiometer
文献类型：期刊论文
| 作者 | Sun, Shijie2,3; Gui, Huaqiao1 ; Jiang, Haihe2,3; Cheng, Tingqing2
|
| 刊名 | RADIO SCIENCE
 |
| 出版日期 | 2024-07-01 |
| 卷号 | 59 |
| 关键词 | robustness relative humidity profile temperature profile neural network |
| ISSN号 | 0048-6604 |
| DOI | 10.1029/2023RS007941 |
| 通讯作者 | Jiang, Haihe(hjiang@hfcas.ac.cn) |
| 英文摘要 | The ground-based microwave radiometer (MWR) retrieves atmospheric profiles with a high temporal resolution for temperature and relative humidity up to a height of 10 km. These profiles have been widely used in the field of meteorological observation. Due to the inherent fragility of neural networks, one of the important issues in this field is to improve the reliability and stability of MWR profiles based on neural network inversion. We propose a deep learning method that adds noise to the BP neural network inversion (NBPNN) process. Comparison of the radiosonde data and NBPNN results shows that if the error of MWR brightness temperature is in the range of -2-2 K, the root-mean-square error (RMSE) of the temperature profile is 2.15 K, and the RMSE of the relative humidity profile is 19.46 % inverted by NBPNN. The results are much less than the errors of the temperature profile and relative humidity profile inverted by the traditional backpropagation neural network inverse method. From the comparison, we demonstrated that NBPNN significantly increases the inversion accuracy and robustness under the condition of errors in brightness temperature, which can reduce requirements for BT accuracy of MWR and achieve MWR long-term stability. It's necessary to improve the reliability and stability of inversion for microwave radiometer We propose a deep learning method that adds noise to the BP neural network inversion process The new inversion process significantly increases the inversion accuracy and robustness under the condition of errors in brightness temperature |
| WOS关键词 | TEMPERATURE |
| 资助项目 | National Natural Science Foundation of China[U2133212] |
| WOS研究方向 | Astronomy & Astrophysics ; Geochemistry & Geophysics ; Meteorology & Atmospheric Sciences ; Remote Sensing ; Telecommunications |
| 语种 | 英语 |
| WOS记录号 | WOS:001268954100001 |
| 出版者 | AMER GEOPHYSICAL UNION |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/137095]  |
| 专题 | 中国科学院合肥物质科学研究院 |
| 通讯作者 | Jiang, Haihe |
| 作者单位 | 1.Chinese Acad Sci, Anhui Inst Opt & Fine Mech, Hefei Inst Phys Sci, Key Lab Environm Opt & Technol, Hefei, Peoples R China 2.Chinese Acad Sci, Inst Hlth & Med Technol, Hefei Inst Phys Sci, Anhui Prov Key Lab Med Phys & Technol, Hefei, Peoples R China 3.Univ Sci & Technol China, Hefei, Peoples R China |
| 推荐引用方式 GB/T 7714 | Sun, Shijie,Gui, Huaqiao,Jiang, Haihe,et al. The Robustness of an Anti-Noise BP Neural Network Inversion Algorithm for Ground-Based Microwave Radiometer[J]. RADIO SCIENCE,2024,59. |
| APA | Sun, Shijie,Gui, Huaqiao,Jiang, Haihe,&Cheng, Tingqing.(2024).The Robustness of an Anti-Noise BP Neural Network Inversion Algorithm for Ground-Based Microwave Radiometer.RADIO SCIENCE,59. |
| MLA | Sun, Shijie,et al."The Robustness of an Anti-Noise BP Neural Network Inversion Algorithm for Ground-Based Microwave Radiometer".RADIO SCIENCE 59(2024). |
入库方式： OAI收割
来源：合肥物质科学研究院
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