Uncertainty of Pure Rotational Raman–Rayleigh LiDAR for Temperature Measurement in Middle-to-Upper Atmosphere: Evaluation Method

Abstract

This article constitutes the second installment in a series dedicated to exploring the uncertainties associated with pure rotational Raman–Rayleigh temperature measurement LiDARs in the middle-to-upper atmosphere (20–90 km). It presents uncertainty evaluation methods aimed at addressing the challenge of uncertainty assessment. These methods leverage both the Guide to the Expression of Uncertainty in Measurement (GUM) method and the Monte Carlo method (MCM) to ascertain the uncertainty of air density and temperature derived from LiDAR signals. Within the framework of these evaluation methods, various uncertainty sources are considered, encompassing saturation correction, photon noise, background noise, reference temperature, and two special uncertainty sources: atmospheric transmittance correction in Rayleigh LiDAR and the calibration process in Raman LiDAR. An uncertainty evaluation example in accordance with the proposed evaluation method is provided by employing raw signals from full-chain simulation system introduced in another series paper. In this example, we compare the evaluation results of the GUM method with those derived from the MCM. Our findings reveal consistent trends in different uncertainty components between the two methods. In addition, the uncertainty caused by atmospheric transmittance correction is involved with detailed considerations of correlation and iterative methods. The uncertainty resulting from the calibration process in Raman LiDAR, attributed to its nonlinear characteristics, can only be evaluated using the MCM. These findings affirm the applicability of the proposed uncertainty evaluation methods of pure rotational Raman–Rayleigh LiDAR. This research enhances our understanding of the sources of uncertainty in LiDAR systems and provides a convenient approach for evaluating the data quality of temperature LiDAR.