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Discussion papers | Copyright
https://doi.org/10.5194/acp-2018-480
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 13 Jun 2018

Research article | 13 Jun 2018

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This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Chemistry and Physics (ACP).

Turbulent enhancement of radar reflectivity factor for polydisperse cloud droplets

Keigo Matsuda and Ryo Onishi Keigo Matsuda and Ryo Onishi
  • Center for Earth Information Science and Technology (CEIST), Japan Agency for Marine-Earth Science and Technology (JAMSTEC), 3173-25 Showa-machi, Kanazawa-ku, Yokohama 236-0001, Japan

Abstract. The radar reflectivity factor is important for estimating cloud microphysical properties; thus, in this study, we determine the quantitative influence of microscale turbulent clustering of polydisperse droplets on the radar reflectivity factor. The theoretical solution for particulate Bragg scattering is obtained without assuming monodisperse droplet sizes. The scattering intensity is given by an integral function including the cross spectrum of number density fluctuations for two different droplet sizes. We calculate the cross spectrum based on turbulent clustering data, which are obtained by the direct numerical simulation (DNS) of particle-laden homogeneous isotropic turbulence. The results show that the coherence of the cross spectrum is close to unity for small wavenumbers and decreases almost exponentially with increasing wavenumber. This decreasing trend is dependent on the combination of Stokes numbers. A critical wavenumber is introduced to characterize the exponential decrease of the coherence and parametrized using the Stokes number difference. Comparison with DNS results confirms that the proposed model can reproduce the rp3-weighted power spectrum, which is proportional to the clustering influence on the radar reflectivity factor, to a sufficiently high accuracy. The model is then applied to high-resolution cloud-simulation data obtained from a spectral-bin cloud simulation. The result shows that the influence of turbulent clustering can be significant for the near-top of turbulent clouds.

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Keigo Matsuda and Ryo Onishi
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Keigo Matsuda and Ryo Onishi
Keigo Matsuda and Ryo Onishi
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This paper presents a parameterization to predict the influence of microscale turbulent clustering of cloud droplets on the radar reflectivity factor, based on a direct numerical simulation (DNS) of turbulence. The proposed parameterization takes into account of the turbulent clustering structure of droplets with arbitrary size distributions. This paper also discuss quantitative influence on realistic radar observations, applying the parameterization to high-resolution cloud-simulation data.
This paper presents a parameterization to predict the influence of microscale turbulent...
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