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This study investigates the influence of microscale turbulent clustering of cloud droplets on the radar reflectivity factor and proposes a new parameterization to account for it. A three-dimensional direct numerical simulationofparticle-ladenisotropicturbulenceisperformedtoobtainturbulentclusteringdata.Theclusteringdata arethenusedtocalculatethepowerspectraofdropletnumberdensityfluctuations,whichshowadependenceon the Taylor microscale-based Reynolds number (Rel) and the Stokes number (St). First, the Reynolds number dependency of the turbulent clustering influence is investigated for 127 , Rel , 531. The spectra for this wide range of Rel values reveal that Rel 5 204 is sufficiently large to be representative of the whole wavenumber range relevant for radar observations of atmospheric clouds. The authors then investigate the Stokes number dependencyforRel5204andproposeanempiricalmodelfortheturbulentclusteringinfluenceassumingpower laws for the number density spectrum. For Stokes numbers less than 2, the proposed model can estimate the influence of turbulenceonthe spectrumwith an RMS errorless than 1dB when calculated over thewavenumber range relevant for radar observations. For larger Stokes number droplets, the model estimate has larger errors, but the influence of turbulence is likely negligible in typical clouds. Applications of the proposed model to two idealized cloud observing scenarios reveal that microscale turbulent clustering can cause a significant error in estimating cloud droplet amounts from radar observations with microwave frequencies less than 13.8 GHz.

Influence of Microscale Turbulent Droplet Clustering on Radar Cloud Observations