Wind retrieval algorithm of Rayleigh Doppler lidar

Abstract

The wind measurement principle and the structure of Rayleigh Doppler lidar are introduced. The method for Fabry-Perot(FP) etalon transmission curve calibration is given. The problem is pointed out that to fit the transmission curve by using Lorentz or Voigt function may induce large error: the relative error would be up to 8% by Lorentz fitting especially. A least-square nonlinear fitting procedure is proposed, which can eliminate the fitting error and improve the wind precision. After the dominant role that the temperature uncertainty plays in wind retrieval process is considered, a nonlinear iterative algorithm is proposed, which can retrieve both wind temperature and atmospheric temperature. Simulation results show that the algorithm proposed can improve wind retrieval accuracy effectively compared with the traditional method without the Mie-induced effect, and the wind retrieval accuracy of the algorithm proposed will be degraded with Mie-induced effect but still better than that of traditional method.