Accurate inversion of tropospheric bottom temperature using pure rotational Raman lidar in polluted air condition

For pure rotational Raman temperature-measurement lidar, the suppression of elastic signals has always been a core technical issue. In practice, however, the surge of aerosol concentration, such as mist, haze, and short-time air pollution, tends to cause the intensity of the elastic scattering light to exceed the generally applicable suppression ratio of the lidar system, and the leaked elastic signal will result in some distortions of the Raman signals, especially in the near-field region. To solve this problem, here we propose to correct the signal distortion by forming a correction coefficient from the expression of the signal intensity ratio of the two Raman channels, which has been proven to be highly correlated with the intensity of the elastic signals. Simulated and experimental verification have been performed on the proposed method. The results demonstrated that using this coefficient to correct the Raman data will significantly improve the signal quality and the corresponding temperature inversion accuracy. Meanwhile, the correction coefficient can also be applied to modify the lidar returns in a similar atmospheric environment so as to enhance the detecting performance of the pure rotational Raman lidar in polluted air condition.