Abstract

Abstract. Among lidar techniques, the pure rotational Raman (PRR) technique is the best suited for tropospheric and lower stratospheric temperature measurements. Calibration functions are required for the PRR technique to retrieve temperature profiles from lidar remote sensing data. Both temperature retrieval accuracy and number of calibration coefficients depend on the selected function. The commonly used calibration function (linear in reciprocal temperature 1∕T with two calibration coefficients) ignores all types of broadening of individual PRR lines of atmospheric N2 and O2 molecules. However, the collisional (pressure) broadening dominates over other types of broadening of PRR lines in the troposphere and can differently affect the accuracy of tropospheric temperature measurements depending on the PRR lidar system. We recently derived the calibration function in the general analytical form that takes into account the collisional broadening of all N2 and O2 PRR lines (Gerasimov and Zuev, 2016). This general calibration function represents an infinite series and, therefore, cannot be directly used in the temperature retrieval algorithm. For this reason, its four simplest special cases (calibration functions nonlinear in 1∕T with three calibration coefficients), two of which have not been suggested before, were considered and analyzed. All the special cases take the collisional PRR lines broadening into account in varying degrees and the best function among them was determined via simulation. In this paper, we use the special cases to retrieve tropospheric temperature from real PRR lidar data. The calibration function best suited for tropospheric temperature retrievals is determined from the comparative analysis of temperature uncertainties yielded by using these functions. The absolute and relative statistical uncertainties of temperature retrieval are given in an analytical form assuming Poisson statistics of photon counting. The vertical tropospheric temperature profiles, retrieved from nighttime lidar measurements in Tomsk (56.48° N, 85.05° E; Western Siberia, Russia) on 2 October 2014 and 1 April 2015, are presented as an example of the calibration functions application. The measurements were performed using a PRR lidar designed in the Institute of Monitoring of Climatic and Ecological Systems of the Siberian Branch of the Russian Academy of Sciences for tropospheric temperature measurements.

Highlights

  • The pure rotational Raman (PRR) technique is known to be the best suited for lower atmosphere temperature measurements (Wulfmeyer et al, 2015)

  • In our recent Optic Express paper, we considered the physics of our approach, derived mathematically the general calibration function that takes into account the collisional broadening of all N2 and O2 PRR lines, analyzed four nonlinear three-coefficient special cases of Eq (8) via simulation to be used in the temperature retrieval algorithm, and determined the best function among them

  • We have considered and used the linear and four nonlinear in x = 1/T calibration functions in the tropospheric temperature retrieval algorithm

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Summary

Introduction

The pure rotational Raman (PRR) technique is known to be the best suited for lower atmosphere temperature measurements (Wulfmeyer et al, 2015). All collisionally broadened PRR lines contribute to the signals detected in both lidar temperature channels due to the long Lorentzian tails of the line profiles (Measures, 1984), and the general calibration function takes on the form (Gerasimov and Zuev, 2016). In our recent Optic Express paper, we considered the physics of our approach, derived mathematically the general calibration function that takes into account the collisional broadening of all N2 and O2 PRR lines, analyzed four nonlinear three-coefficient special cases of Eq (8) via simulation to be used in the temperature retrieval algorithm, and determined the best function among them. The corresponding temperature retrieval function is derived www.atmos-meas-tech.net/10/315/2017/ Another three-coefficient special case of Eq (9) can be written as follows (Gerasimov and Zuev, 2016): A2. Analyze five vertical tropospheric temperature profiles retrieved from the lidar data using Eqs. (11), (13), (15), (18), and (20)

Raw lidar data averaging
Reference temperature points for the lidar calibration
Temperature profiles retrieved with different calibration functions
Summary and outlook
Linear calibration function
Full Text
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