The full-width at half-maximum or probe length of the Lorentzian weighting function of continuous-wave Doppler lidars increases quadratically with the focus distance, which results in a deterioration in the spatial resolution of measurements. What is worse, a Doppler lidar is susceptible to moving objects that are far away from the intended measurement point. Therefore, we suggest a novel configuration to mitigate these problems by deploying two co-planar quarter-wave plates with orthogonal fast axes in the conventional continuous-wave lidar system, without any change to the other optical or electronic components. If the vertically polarized laser beam that we emit goes out and its backscattered beam returns back through the same quarter-wave plate, the returned beam will become horizontally polarized. The horizontally polarized backscattered beam cannot beat with the vertically polarized local oscillator to generate a Doppler signal. However, the polarization of the returned beam will remain unchanged if the emitted beam travels out through one plate and returns through the other. In this way, the influence of a moving backscattering particle far away from the focus point can be reduced. Both theoretical and experimental results show that, in a proper configuration, the probe length of the continuous-wave lidar can be reduced by 10%, compared with that of the conventional lidar. In addition, the fat tails of the Lorentzian weighting function can be suppressed by up to 80% to reduce the return from a cloud, albeit with a large reduction (perhaps 90%) in the signal power. This investigation provides a potential method to increase the spatial resolution of Doppler wind lidars and suppress the low-hanging cloud return.
Abstract. In moderate to heavy precipitation, rain droplets may deteriorate Doppler lidars' accuracy for measuring the line-of-sight wind velocity because their projected velocity on the beam direction differs greatly from that of air. Therefore, we propose a method of effectively filtering away the adverse effects of rain on velocity estimation by sampling the Doppler spectra faster than the rain drops' beam transit time. By using a special averaging procedure, we can suppress the rain signal by sampling the spectrum at 3 kHz. On a moderately rainy day with a maximum rain intensity of 4 mm/h, three ground-based continuous-wave Doppler lidars were used to conduct a field measurement campaign at the Risø campus of the Technical University of Denmark. We demonstrate that the rain bias can effectively be removed by normalizing the noise-flattened Doppler spectra with their peak values before they are averaged down to 50 Hz prior to the determination of the speed. In comparison to the sonic anemometer measurements acquired at the same location, the wind velocity bias at 50 Hz is reduced from up to −1.58 m/s of the conventional lidar data to −0.18 m/s of the normalized lidar data. This significant reduction of the bias occurs at the minute with the highest amount of rain when the measurement distance of the lidar is 103.9 m with a corresponding probe length being 9.8 m. With the smallest probe length, 1.2 m, the rain-induced bias was only present at the period with the highest rain intensity and was also effectively eliminated with the procedure. Thus the proposed method for reducing the impact of rain on continuous-wave Doppler lidar measurements of air velocity is promising, without requiring much computational effort.
The presence of raindrops has an adverse impact on the line-of-sight wind speed measurement of Doppler lidars. Here, we propose a method to improve the accuracy of wind speed estimation through a filtering process on rapidly sampled (3000 Hz) lidar data. For this purpose, we conducted a field study at the Risø campus of the Technical University of Denmark using a ground-based, continuous-wave Doppler lidar. Data was acquired during a three-hour period with rain. We propose that we can differentiate between the rain and aerosol back-scattering signals by assessing the maximum of the noise-normalized Doppler spectra. To reduce the influence of rain of the velocity signal, we filter away the Doppler spectra where the maximum is larger than a given threshold. The comparison between the raw and the filtered lidar data with sonic anemometer measurements acquired at the same location, shows that we can effectively remove rain signals and improve the measurement accuracy of a Doppler lidar. However, this method is not applicable when the back-scattering of aerosols and rain are characterized by the same statistics.
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