Keigo MATSUDA – Cloud radar reflectivity factor enhancement due to turbulent droplet clustering

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Date(s) - 12 octobre 2018

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Radar observation is a powerful tool to obtain a two- or three-dimensional distribution data of cloud and precipitation particles. The cloud radar reflectivity factor can be enhanced by the spatial correlation of scattering particles, which is referred to as particulate Bragg scattering. It is well known that inertial particles such as cloud droplets form a nonuniform distribution in air turbulence : That is, inertial particles concentrate in low-vorticity and high-strain-rate regions due to the centrifugal effect. We thus investigate the quantitative influence of turbulent droplet clustering on radar reflectivity factor by using the direct numerical simulation (DNS) of particle-laden isotropic turbulence. In the mechanism of particulate Bragg scattering, the increase of the factor is proportional to the power spectrum of droplet number density fluctuations. Firstly, we investigate the power spectrum for monodisperse droplets. The results show that the shape of obtained power spectrum is strongly dependent on the Stokes number.Quantitative estimate of the radar reflectivity factor for an idealized cloud scenario reveals that turbulent clustering can cause significant increase of the factor up to 14 dB for typical cloud droplet sizes. Secondly, we investigate the influence for the case of polydisperse droplets. The results show that the coherence of cross spectrum for bidisperse droplets can be parametrized by using the Stokes number difference. By using the parametrization, we propose a model for estimating the radar reflectivity factor enhancement for arbitrary droplet size distribution. Finally, possible errors of radar observation due to turbulent clustering is estimated by using the proposed model. Keigo MATSUDA [