Uncertainty Evaluation on Temperature Detection of Middle Atmosphere by Rayleigh Lidar

Xinqi Li; Kai Zhong; Xianzhong Zhang; Tong Wu; Yijian Zhang; Yu Wang; Shijie Li; Zhaoai Yan; Degang Xu; Jianquan Yao

doi:10.3390/rs15143688

Remote Sensing (Jul 2023)

Uncertainty Evaluation on Temperature Detection of Middle Atmosphere by Rayleigh Lidar

Xinqi Li,
Kai Zhong,
Xianzhong Zhang,
Tong Wu,
Yijian Zhang,
Yu Wang,
Shijie Li,
Zhaoai Yan,
Degang Xu,
Jianquan Yao

Affiliations

Xinqi Li: Key Laboratory of Optoelectronic Information Technology (Ministry of Education), School of Precision Instruments and Opto-Electronic Engineering, Tianjin University, Tianjin 300072, China
Kai Zhong: Key Laboratory of Optoelectronic Information Technology (Ministry of Education), School of Precision Instruments and Opto-Electronic Engineering, Tianjin University, Tianjin 300072, China
Xianzhong Zhang: Key Laboratory of Optoelectronic Information Technology (Ministry of Education), School of Precision Instruments and Opto-Electronic Engineering, Tianjin University, Tianjin 300072, China
Tong Wu: School of Marine Science and Technology, Tianjin University, Tianjin 300072, China
Yijian Zhang: Key Laboratory of Optoelectronic Information Technology (Ministry of Education), School of Precision Instruments and Opto-Electronic Engineering, Tianjin University, Tianjin 300072, China
Yu Wang: Key Laboratory of Optoelectronic Information Technology (Ministry of Education), School of Precision Instruments and Opto-Electronic Engineering, Tianjin University, Tianjin 300072, China
Shijie Li: Key Laboratory of Optoelectronic Information Technology (Ministry of Education), School of Precision Instruments and Opto-Electronic Engineering, Tianjin University, Tianjin 300072, China
Zhaoai Yan: Key Laboratory of Science and Technology on Environmental Space Situation Awareness, National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China
Degang Xu: Key Laboratory of Optoelectronic Information Technology (Ministry of Education), School of Precision Instruments and Opto-Electronic Engineering, Tianjin University, Tianjin 300072, China
Jianquan Yao: Key Laboratory of Optoelectronic Information Technology (Ministry of Education), School of Precision Instruments and Opto-Electronic Engineering, Tianjin University, Tianjin 300072, China

DOI: https://doi.org/10.3390/rs15143688
Journal volume & issue: Vol. 15, no. 14
p. 3688

Abstract

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Measurement uncertainty is an extremely important parameter for characterizing the quality of measurement results. In order to measure the reliability of atmospheric temperature detection, the uncertainty needs to be evaluated. In this paper, based on the measurement models originating from the Chanin-Hauchecorne (CH) method, the atmospheric temperature uncertainty was evaluated using the Guide to the Expression of Uncertainty in Measurement (GUM) and the Monte Carlo Method (MCM) by considering the ancillary temperature uncertainty and the detection noise as the major uncertainty sources. For the first time, the GUM atmospheric temperature uncertainty framework was comprehensively and quantitatively validated by MCM following the instructions of JCGM 101: 2008 GUM Supplement 1. The results show that the GUM method is reliable when discarding the data in the range of 10–15 km below the reference altitude. Compared with MCM, the GUM method is recommended to evaluate the atmospheric temperature uncertainty of Rayleigh lidar detection in terms of operability, reliability, and calculation efficiency.

Published in Remote Sensing

ISSN: 2072-4292 (Online)
Publisher: MDPI AG
Country of publisher: Switzerland
LCC subjects: Science
Website: http://www.mdpi.com/journal/remotesensing/

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