Identifying cloud, precipitation, windshear, and turbulence by deep analysis of the power spectrum of coherent Doppler wind lidar

Abstract

Researches on the atmospheric boundary layer (ABL) need accurate measurements with high temporal and spatial resolutions from a series of different instruments. Here, a method for identifying cloud, precipitation, windshear, and turbulence in the ABL using a single coherent Doppler wind lidar (CDWL) is proposed and demonstrated. Based on deep analysis of the power spectrum of the backscattering signal, multiple lidar products, such as carrier-to-noise (CNR), spectrum width, spectrum skewness, turbulent kinetic energy dissipation rate (TKEDR), and shear intensity are derived for weather identification. Firstly, the cloud is extracted by Haar wavelet covariance transform (HWCT) algorithm based on the CNR after range correction. Secondly, since the spectrum broadening may be due to turbulence, windshear or precipitation, the spectrum skewness is introduced to distinguish the precipitation from two other conditions. Whereas wind velocity is obtained by single peak fitting in clear weather condition, the double-peak fitting is used to retrieve wind and rainfall velocities simultaneously in the precipitation condition. Thirdly, judging from shear intensity and TKEDR, turbulence and windshear are classified. As a double check, the temporal continuity is used. Stable wind variances conditions such as low-level jets are identified as windshear, while arbitrary wind variances conditions are categorized as turbulence. In the field experiment, the method is implemented on a micro-pulse CDWL to provide meteorological services for the 70th anniversary of the China’s National Day, in Inner Mongolia, China (43°54′N, 115°58′E). All weather conditions are successfully classified. By comparing lidar results to that of microwave radiometer (MWR), the spectrum skewness is found be more accurate to indicate precipitation than spectrum width or vertical speed. Finally, the parameter relationships and distributions are analyzed statistically in different weather conditions.

Cloud Seeding Evidenced by Coherent Doppler Wind Lidar

“…The range-varying resolution is designed to improve the detection probability in the high altitude where the aerosol concentration is low. More detailed parameters and applications of the CDWL are introduced in previous work [41,47,51].…”

“…In addition, clear air turbulence, windshear, and aircraft wake lead to spectrum broadening due to strong velocity dispersion. Spectrum broadening due to multiple spectral components is more obvious than that of other factors, as statistically analyzed in [51]. The normalized skewness (Sk) is introduced to improve measurement of spectra shape, as in the following:…”

“…The azimuth angle scanned from 0° to 300°, with an angle step of 5°. The CDWL results of cloud, precipitation, windshear, and turbulence intensity during the experiment are given in [51,53].…”

“…Atmospheric profiles provided by the microwave radiometer (MWR) are shown in Figure 5. Application and detail specifications of the MWR are provided in [51]. Temperature, water-vapour, and liquid water content of the seeding region at 5:30 p.m. were −5.8~−9.3 °C, 2.5-3.2 gm −3 , and 0.12-0.15 gm −3 , respectively.…”

“…Recently, lidars have been extended to precipitation detection for their ability to detect the aerosol and precipitation signals simultaneously under rain conditions [46][47][48][49][50]. By deep analysis of the power spectrum, the CDWL can provide precise wind and rain detection with high spatial and temporal resolutions [51]. Based on accurate spectral measurements, the CDWL has the potential to reflect cloud phases, and thus it can be developed as a new way for cloud seeding detection.…”