Machine Learning Model-Based Retrieval of Temperature and Relative Humidity Profiles Measured by Microwave Radiometer
The accuracy of temperature and relative humidity (RH) profiles retrieved by the ground-based microwave radiometer (MWR) is crucial for meteorological research. In this study, the four-year measurements of brightness temperature measured by the microwave radiometer from Huangpu meteorological statio...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/15/3838 |
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| author | Yuyan Luo Hao Wu Taofeng Gu Zhenglin Wang Haiyan Yue Guangsheng Wu Langfeng Zhu Dongyang Pu Pei Tang Mengjiao Jiang |
| author_facet | Yuyan Luo Hao Wu Taofeng Gu Zhenglin Wang Haiyan Yue Guangsheng Wu Langfeng Zhu Dongyang Pu Pei Tang Mengjiao Jiang |
| author_sort | Yuyan Luo |
| collection | DOAJ |
| description | The accuracy of temperature and relative humidity (RH) profiles retrieved by the ground-based microwave radiometer (MWR) is crucial for meteorological research. In this study, the four-year measurements of brightness temperature measured by the microwave radiometer from Huangpu meteorological station in Guangzhou, China, and the radiosonde data from the Qingyuan meteorological station (70 km northwest of Huangpu station) during the years from 2018 to 2021 are compared with the sonde data. To make a detailed comparison on the performance of machine learning models in retrieving the temperature and RH profiles, four machine learning algorithms, namely Deep Learning (DL), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost) and Random Forest (RF), are employed and verified. The results show that the DL model performs the best in temperature retrieval (with the root-mean-square error and the correlation coefficient of 2.36 and 0.98, respectively), while the RH of the four machine learning methods shows different excellence at different altitude levels. The integrated machine learning (ML) RH method is proposed here, in which a certain method with the minimum RMSE is selected from the four methods of DL, GBM, XGBoost and RF for a certain altitude level. Two cases on 29 January 2021 and on 10 February 2021 are used for illustration. The case on 29 January 2021 illustrates that the DL model is suitable for temperature retrieval and the ML model is suitable for RH retrieval in Guangzhou. The case on 10 February 2021 shows that the ML RH method reaches over 85% before precipitation, implying the application of the ML RH method in pre-precipitation warnings. |
| first_indexed | 2024-03-11T00:17:22Z |
| format | Article |
| id | doaj.art-4fd0c3ac1fed42f18983835ddb0d2a54 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T00:17:22Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-4fd0c3ac1fed42f18983835ddb0d2a542023-11-18T23:31:32ZengMDPI AGRemote Sensing2072-42922023-08-011515383810.3390/rs15153838Machine Learning Model-Based Retrieval of Temperature and Relative Humidity Profiles Measured by Microwave RadiometerYuyan Luo0Hao Wu1Taofeng Gu2Zhenglin Wang3Haiyan Yue4Guangsheng Wu5Langfeng Zhu6Dongyang Pu7Pei Tang8Mengjiao Jiang9Plateau Atmospheres and Environment Key Laboratory of Sichuan Province, School of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610225, ChinaKey Laboratory of China Meteorological Administration Atmospheric Sounding, School of Electrical Engineering, Chengdu University of Information Technology, Chengdu 610225, ChinaGuangzhou Meteorological Observatory, Guangzhou 511430, ChinaPlateau Atmospheres and Environment Key Laboratory of Sichuan Province, School of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610225, ChinaGuangzhou Emergency Warning Information Release Center, Guangzhou 511430, ChinaGuangzhou Meteorological Observatory, Guangzhou 511430, ChinaKey Laboratory of China Meteorological Administration Atmospheric Sounding, School of Electrical Engineering, Chengdu University of Information Technology, Chengdu 610225, ChinaKey Laboratory of China Meteorological Administration Atmospheric Sounding, School of Electrical Engineering, Chengdu University of Information Technology, Chengdu 610225, ChinaZhongshan Meteorological Service, Zhongshan 528400, ChinaPlateau Atmospheres and Environment Key Laboratory of Sichuan Province, School of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610225, ChinaThe accuracy of temperature and relative humidity (RH) profiles retrieved by the ground-based microwave radiometer (MWR) is crucial for meteorological research. In this study, the four-year measurements of brightness temperature measured by the microwave radiometer from Huangpu meteorological station in Guangzhou, China, and the radiosonde data from the Qingyuan meteorological station (70 km northwest of Huangpu station) during the years from 2018 to 2021 are compared with the sonde data. To make a detailed comparison on the performance of machine learning models in retrieving the temperature and RH profiles, four machine learning algorithms, namely Deep Learning (DL), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost) and Random Forest (RF), are employed and verified. The results show that the DL model performs the best in temperature retrieval (with the root-mean-square error and the correlation coefficient of 2.36 and 0.98, respectively), while the RH of the four machine learning methods shows different excellence at different altitude levels. The integrated machine learning (ML) RH method is proposed here, in which a certain method with the minimum RMSE is selected from the four methods of DL, GBM, XGBoost and RF for a certain altitude level. Two cases on 29 January 2021 and on 10 February 2021 are used for illustration. The case on 29 January 2021 illustrates that the DL model is suitable for temperature retrieval and the ML model is suitable for RH retrieval in Guangzhou. The case on 10 February 2021 shows that the ML RH method reaches over 85% before precipitation, implying the application of the ML RH method in pre-precipitation warnings.https://www.mdpi.com/2072-4292/15/15/3838microwave radiometerradiosondetemperature and humidity profilesmachine learning |
| spellingShingle | Yuyan Luo Hao Wu Taofeng Gu Zhenglin Wang Haiyan Yue Guangsheng Wu Langfeng Zhu Dongyang Pu Pei Tang Mengjiao Jiang Machine Learning Model-Based Retrieval of Temperature and Relative Humidity Profiles Measured by Microwave Radiometer Remote Sensing microwave radiometer radiosonde temperature and humidity profiles machine learning |
| title | Machine Learning Model-Based Retrieval of Temperature and Relative Humidity Profiles Measured by Microwave Radiometer |
| title_full | Machine Learning Model-Based Retrieval of Temperature and Relative Humidity Profiles Measured by Microwave Radiometer |
| title_fullStr | Machine Learning Model-Based Retrieval of Temperature and Relative Humidity Profiles Measured by Microwave Radiometer |
| title_full_unstemmed | Machine Learning Model-Based Retrieval of Temperature and Relative Humidity Profiles Measured by Microwave Radiometer |
| title_short | Machine Learning Model-Based Retrieval of Temperature and Relative Humidity Profiles Measured by Microwave Radiometer |
| title_sort | machine learning model based retrieval of temperature and relative humidity profiles measured by microwave radiometer |
| topic | microwave radiometer radiosonde temperature and humidity profiles machine learning |
| url | https://www.mdpi.com/2072-4292/15/15/3838 |
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