Comparative Analysis and Optimal Selection of Calibration Functions in Pure Rotational Raman Lidar Technique
The pure rotational Raman (PRR) lidar technique relies on calibration functions (CFs) to extract temperature information from raw detection data. The choice of CF significantly impacts the accuracy of the retrieved temperature. In this study, we propose a method that combines multiple Monte Carlo si...
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2024-10-01
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| author | Yinghong Yu Siying Chen Wangshu Tan Rongzheng Cao Yixuan Xie He Chen Pan Guo Jie Yu Rui Hu Haokai Yang Xin Li |
| author_facet | Yinghong Yu Siying Chen Wangshu Tan Rongzheng Cao Yixuan Xie He Chen Pan Guo Jie Yu Rui Hu Haokai Yang Xin Li |
| author_sort | Yinghong Yu |
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| description | The pure rotational Raman (PRR) lidar technique relies on calibration functions (CFs) to extract temperature information from raw detection data. The choice of CF significantly impacts the accuracy of the retrieved temperature. In this study, we propose a method that combines multiple Monte Carlo simulation experiments with a statistical analysis, and we first conduct simulated comparisons of the calibration effects of different CFs while considering the impact of noise. We categorized ten common CFs into four groups based on their functional form and the number of calibration coefficients. Based on functional form, specifically, we defined 1/<i>T</i> = f(ln<i>Q</i>) as a forward calibration function (FCF) and ln<i>Q</i> = g(1/<i>T</i>) as a backward calibration function (BCF). Here, T denotes temperature, and Q denotes the signal intensity ratio. Their performance within and outside the calibration interval is compared across different integration times, smoothing methods, and reference temperature ranges. The results indicate that CFs of the same category exhibit similar calibration effects, while those of different categories exhibit notable differences. Within the calibration interval, the FCF performs better, especially with more coefficients. However, outside the calibration interval, the linear calibration function (which can be considered a two-coefficient FCF) has an obvious advantage. Conclusions based on the simulation results are validated with actual data, and the factors influencing calibration errors are discussed. Utilizing these findings to guide CF selection can enhance the accuracy and stability of PRR lidar detection. |
| format | Article |
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| language | English |
| publishDate | 2024-10-01 |
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| spelling | doaj-art-8bdf00f2a2b3464fa89da16d1501c4592025-08-20T01:47:36ZengMDPI AGRemote Sensing2072-42922024-10-011619369010.3390/rs16193690Comparative Analysis and Optimal Selection of Calibration Functions in Pure Rotational Raman Lidar TechniqueYinghong Yu0Siying Chen1Wangshu Tan2Rongzheng Cao3Yixuan Xie4He Chen5Pan Guo6Jie Yu7Rui Hu8Haokai Yang9Xin Li10School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, ChinaAcademy of Military Sciences, Academy of Military Medicine Sciences, Beijing 100085, ChinaThe pure rotational Raman (PRR) lidar technique relies on calibration functions (CFs) to extract temperature information from raw detection data. The choice of CF significantly impacts the accuracy of the retrieved temperature. In this study, we propose a method that combines multiple Monte Carlo simulation experiments with a statistical analysis, and we first conduct simulated comparisons of the calibration effects of different CFs while considering the impact of noise. We categorized ten common CFs into four groups based on their functional form and the number of calibration coefficients. Based on functional form, specifically, we defined 1/<i>T</i> = f(ln<i>Q</i>) as a forward calibration function (FCF) and ln<i>Q</i> = g(1/<i>T</i>) as a backward calibration function (BCF). Here, T denotes temperature, and Q denotes the signal intensity ratio. Their performance within and outside the calibration interval is compared across different integration times, smoothing methods, and reference temperature ranges. The results indicate that CFs of the same category exhibit similar calibration effects, while those of different categories exhibit notable differences. Within the calibration interval, the FCF performs better, especially with more coefficients. However, outside the calibration interval, the linear calibration function (which can be considered a two-coefficient FCF) has an obvious advantage. Conclusions based on the simulation results are validated with actual data, and the factors influencing calibration errors are discussed. Utilizing these findings to guide CF selection can enhance the accuracy and stability of PRR lidar detection.https://www.mdpi.com/2072-4292/16/19/3690pure rotational Raman temperature measurement technologycalibration function comparisonMonte Carlo methodleast squares fitting |
| spellingShingle | Yinghong Yu Siying Chen Wangshu Tan Rongzheng Cao Yixuan Xie He Chen Pan Guo Jie Yu Rui Hu Haokai Yang Xin Li Comparative Analysis and Optimal Selection of Calibration Functions in Pure Rotational Raman Lidar Technique Remote Sensing pure rotational Raman temperature measurement technology calibration function comparison Monte Carlo method least squares fitting |
| title | Comparative Analysis and Optimal Selection of Calibration Functions in Pure Rotational Raman Lidar Technique |
| title_full | Comparative Analysis and Optimal Selection of Calibration Functions in Pure Rotational Raman Lidar Technique |
| title_fullStr | Comparative Analysis and Optimal Selection of Calibration Functions in Pure Rotational Raman Lidar Technique |
| title_full_unstemmed | Comparative Analysis and Optimal Selection of Calibration Functions in Pure Rotational Raman Lidar Technique |
| title_short | Comparative Analysis and Optimal Selection of Calibration Functions in Pure Rotational Raman Lidar Technique |
| title_sort | comparative analysis and optimal selection of calibration functions in pure rotational raman lidar technique |
| topic | pure rotational Raman temperature measurement technology calibration function comparison Monte Carlo method least squares fitting |
| url | https://www.mdpi.com/2072-4292/16/19/3690 |
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