Improved wind speed estimation and rain quantification with continuous-wave wind lidar

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.