Real-time measurement of atmospheric wind field parameters plays an important role in weather analysis and forecasting, including improving the efficiency of wind energy, particle tracking, boundary layer measurements, and airport security. In this study, a wind profile coherent wind Light Detection and Ranging (Lidar) measurement with a wavelength of 1.55 µm was developed and demonstrated based on the principle of eight-beam velocimetry. The wind speed information was retrieved, and vertical and horizontal profiles were calculated via power spectrum estimation of sampled echo signals through the measurement of the atmospheric wind field in Hefei for several consecutive days. The experimental results show that the wind profiles produced using different techniques are quite consistent and the standard error is less than 0.42 m/s compared with three-beam and five-beam wind measurements.

Wind fields provide direct power for exchanging energy and matter in the atmosphere. All-fiber coherent Doppler lidar is a powerful tool for detecting boundary-layer wind fields. According to the characteristics of the lidar echo signal, an adaptive noise-reduction peak retrieval (ANRPR) algorithm is proposed in this study. Firstly, the power spectrum data are divided into several continuous range gates according to the time series. Then, the adaptive iterative reweighted penalized least-squares (airPLS) method is used to reduce the background noise. Secondly, the continuity between spectra is enhanced by 2D Gaussian low-pass filtering. Finally, an adaptive peak-retrieval algorithm is employed to extract the Doppler shift, facilitating the synthesis of a spatial atmospheric 3D wind field through the vector synthesis method. When comparing data from different heights of the meteorological gradient tower, both the horizontal wind-speed correlation and the horizontal wind-direction correlation exceed 0.90. Experimental results show that the proposed algorithm has better robustness and a longer detection distance than the traditional algorithm.

Wind parameters play a vital role in studying atmospheric dynamics and climate change. In this study, a vehicle-mounted coherent wind measurement Lidar (CWML) with a wavelength of 1.55 µm is demonstrated based on a four-beam vertical azimuth display (VAD) scanning mode, and a method to estimate wind vector from power spectrum is proposed. The feasibility of the application of wind profile Lidar in vehicles is verified by calibration tests, comparison experiments, and continuous observation experiments, successively. The effective detection height of Lidar can reach 3 km. In contrasting experiments, the correlation coefficients of the magnitude and direction of horizontal wind speed measured by vehicle-mounted Lidar and fixed Lidar are 0.94 and 0.91, respectively. The experimental results reveal that the accuracies of wind speed and direction measurements with the vehicle-mounted CWML are better than 0.58 m/s and 4.20°, respectively. Furthermore, to understand the role of the wind field in the process of energy and material transport further, a proton-transfer reaction time-of-flight mass spectrometer (PTR-TOF-MS) is utilized to measure the concentration of volatile organic compounds (VOCs). Relevant experimental results indicate that the local meteorological conditions, including wind speed and humidity, influence the VOC concentrations.