An Improved Method for Detectingand Separating Cloud from Drizzle Radar Signatures Using a Time Domain Parametric Technique

The separation of radar signatures depicting cloud and drizzle within a pulse radar volume is a fundamental problem whose solution is required to decouple the microphysical and dynamical processes introduced by turbulence. Such a solution would lead to the development of new meteorological products.In this presentation, a method to detect, separate and estimate multiple radar echoes from cloud and drizzle obtained from vertically pointing cloud Doppler spectra is described. In the case when only clouds are present, the Doppler spectrum is symmetrical and is well approximated by a Gaussian. To extract cloud echoes, a parametric maximum likelihood estimator in the time domain is employed using the recorded radar Doppler spectra data. To detect skewness in the radar spectrum, goodness of fit parameters are defined. It is shown that these new detection parameters exhibit a low level sensitivity to poor signal-to-noise ratios and large signal spectrum widths. The proposed method can consequently be applied to signals with shorter integration time; this significantly reduces the impact of small-scale dynamics present in the Doppler spectrum. Additionally, signals near the cloud top and cloud base are used as constraints to optimize the detection and estimation algorithm's performance.The applications of the technique include inference of the vertical air motion and the particle size distribution of the drizzle. The method will be tested on datasets that have been collected by the ARM cloud radars.