Separating Cloud and Drizzle Radar Moments during Precipitation Onset Using Doppler Spectra

Abstract

he retrieval of cloud, drizzle, and turbulence parameters using radar Doppler spectra is challenged by the convolution of microphysical and dynamical influences and the overall uncertainty introduced by turbulence. A new technique that utilizes recorded radar Doppler spectra from profiling cloud radars is presented here. The technique applies to areas in clouds where drizzle is initially produced by the autoconversion process and is detected by a positive skewness in the radar Doppler spectrum. Using the Gaussian-shape property of cloud Doppler spectra, the cloud-only radar Doppler spectrum is estimated and used to separate the cloud and drizzle contributions. Once separated, the cloud spectral peak can be used to retrieve vertical air motion and eddy dissipation rates, while the drizzle peak can be used to estimate the three radar moments of the drizzle particle size distribution. The technique works for nearly 50% of spectra found near cloud top, with efficacy diminishing to roughly 15% of spectra near cloud base. The approach has been tested on a large dataset collected in the Azores during the Atmospheric Radiation Measurement Program (ARM) Mobile Facility deployment on Graciosa Island from May 2009 through December 2010. Validation of the proposed technique is achieved using the cloud base as a natural boundary between radar Doppler spectra with and without cloud droplets. The retrieval algorithm has the potential to characterize the dynamical and microphysical conditions at cloud scale during the transition from cloud to precipitation. This has significant implications for improving the understanding of drizzle onset in liquid clouds and for improving model parameterization schemes of autoconversion of cloud water into drizzle.