Shangguan Mingjia

Correction of temperature influence on the wind retrieval from a mobile Rayleigh Doppler lidar

A mobile Rayleigh Doppler lidar based on double-edge technique is implemented for simultaneously observing wind and temperature at heights of 15 km–60 km away from ground. Before the inversion of the Doppler shift due to wind, the Rayleigh response function should be calculated, which is a convolution of the laser spectrum, Rayleigh backscattering function, and the transmission function of the Fabry–Perot interferometer used as the frequency discriminator in the lidar. An analysis of the influence of the temperature on the accuracy of the line-of-sight winds shows that real-time temperature profiles are needed because the bandwidth of the Rayleigh backscattering function is temperature-dependent. An integration method is employed in the inversion of the temperature, where the convergence of this method and the high signal-to-noise ratio below 60 km ensure the accuracy and precision of the temperature profiles inverted. Then, real-time and on-site temperature profiles are applied to correct the wind instead of using temperature profiles from a numerical prediction system or atmosphere model. The corrected wind profiles show satisfactory agreement with the wind profiles acquired from radiosondes, proving the reliability of the method.

Comprehensive wind correction for a Rayleigh Doppler lidar from atmospheric temperature and pressure influences and Mie contamination

A correction considering the effects of atmospheric temperature, pressure, and Mie contamination must be performed for wind retrieval from a Rayleigh Doppler lidar (RDL), since the so-called Rayleigh response is directly related to the convolution of the optical transmission of the frequency discriminator and the Rayleigh–Brillouin spectrum of the molecular backscattering. Thus, real-time and on-site profiles of atmospheric pressure, temperature, and aerosols should be provided as inputs to the wind retrieval. Firstly, temperature profiles under 35 km and above the altitude are retrieved, respectively, from a high spectral resolution lidar (HSRL) and a Rayleigh integration lidar (RIL) incorporating to the RDL. Secondly, the pressure profile is taken from the European Center for Medium range Weather Forecast (ECMWF) analysis, while radiosonde data are not available. Thirdly, the Klett–Fernald algorithms are adopted to estimate the Mie and Rayleigh components in the atmospheric backscattering. After that, the backscattering ratio is finally determined in a nonlinear fitting of the transmission of the atmospheric backscattering through the Fabry–Perot interferometer (FPI) to a proposed model. In the validation experiments, wind profiles from the lidar show good agreement with the radiosonde in the overlapping altitude. Finally, a continuous wind observation shows the stability of the correction scheme.