A method for extraction of cloud microphysical properties using a continuous wavelet transform of cloud radar spectra

Description

Stratiform clouds across the globe frequently contain both liquid- and ice-water particles in the same volume; that is, they are often mixed-phase clouds. Millimeter-wavelength cloud radar data are an important source of information on the microphysical properties and dynamical processes within these clouds. However, retrieving and quantifying the climatological radiatively important liquid-phase particles within these clouds remains a challenge because the radar signal is frequently dominated by the returns from the ice particles within these volumes. The ice masks the small reflectivity contribution from the liquid phase.

Here, we present a technique that extracts the weak cloud-liquid drop contributions to millimeter-wavelength cloud radar (MMCR)/Ka-band ARM zenith radar (KAZR) Doppler-velocity spectra in which ice-particle returns dominate. In this approach spectra are first decomposed using a continuous wavelet transform. The resulting coefficients are then used to identify regions in the spectra where cloud drops contribute; Gaussian distributions are subsequently fit to these regions. Our preliminary results indicate this approach is capable of separating the cloud- and ice-particle contributions to the Doppler-velocity spectra. In the process the volume air motion and its turbulent broadening are also extracted.

We will present results derived from synthetic spectra based on hydrometeor size distributions produced by state-of-the-art cloud-resolving model (CRM) simulations of Arctic mixed-phase clouds. The retrieval of the liquid- and ice-water contributions will be evaluated against the microphysical parameters extracted from the CRM and used to generate the synthetic spectra. We will also present the comparison between the retrievals based on the radar observation data from the Mixed-Phase Arctic Cloud Experiment (M-PACE) and the other in situ observations.