摘要
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.
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.
基金
Project supported by the National Natural Science Foundation of China(Grant Nos.41174131,41274151,41304123,41121003 and 41025016)
