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Yefim Kogan

nucleation, drop condensational growth, and evaporation, in addition to coagulation and gravitational fallout (sedimentation). These microphysical processes can be formulated in LES models in two ways. In the first approach, referred to as explicit microphysics, cloud drop size distributions (DSD) are described by many size categories and evolve in unconstrained manner according to dynamic and microphysical processes. The computationally less expensive approach is to predict several moments of DSD rather

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Hugh Morrison and Jason A. Milbrandt

1. Introduction Proper representation of cloud microphysical and precipitation processes is critical for the simulation of weather and climate in atmospheric models. Despite decades of advancement, microphysics parameterization schemes still contain many uncertainties. This is due to an incomplete understanding of the important physical processes as well as the inherent complexity of hydrometeors in the real atmosphere. To represent the range of particles and their physical properties within

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K. Gayatri, S. Patade, and T. V. Prabha

effects on deep convective clouds and precipitation are difficult to determine because of the coupling between microphysics and dynamics ( Tao et al. 2012 ; Altaratz et al. 2014 ; Fan et al. 2016 ). Several observational and modeling studies support the hypothesis that higher aerosol loading leads to the invigoration of DCC [see, e.g., Andreae et al. (2004) , Khain et al. (2005) , Rosenfeld et al. (2008) , Fan et al. (2013) , Storer et al. (2014) , an extensive review article by Altaratz et al

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István Geresdi, Lulin Xue, Noémi Sarkadi, and Roy Rasmussen

way including comprehensive field investigations, statistical evaluation experiments, and numerical studies have been tried since the 1950s. Limitations in the fundamental understanding of cloud dynamics, microphysics, and seeding mechanisms; the capabilities of instruments to detect the key features and physical processes; and the model capability and computing resources in the early studies [see reviews from Smith (1979) , Elliott (1986) , Rangno and Hobbs (1987) , Reynolds (1988) , Orville

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Miroslaw Andrejczuk, Wojciech W. Grabowski, Szymon P. Malinowski, and Piotr K. Smolarkiewicz

due to entrainment and mixing is critical for radiative properties of stratocumulus ( Chosson et al. 2004 ) and shallow convection ( Grabowski 2006 ), cloud systems essential for the earth’s climate. Herein, we investigate interactions between the cloud microphysical processes and turbulence with the emphasis on the net effect on the spectrum of cloud droplets. This paper extends our earlier study ( Andrejczuk et al. 2004 , hereafter AGMS ) that reported results from the pilot series of numerical

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So-Young Kim and Song-You Hong

. 2011 ). Since then, there have been continuous efforts to improve the realism of the microphysics scheme by adding the number concentration of hydrometeors (e.g., Ferrier 1994 ; Seifert and Beheng 2001 ; Milbrandt and Yau 2005 ; Morrison et al. 2005 ; Thompson et al. 2008 ; Lim and Hong 2010 ) or by introducing spectral bin microphysics (e.g., Lynn et al. 2005 ). As the parameterization of the effects of partial cloudiness and cloud vertical overlap was earlier popular for radiation in

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Franziska Glassmeier and Ulrike Lohmann

feedback-type responses comprise dynamical, thermodynamical, radiative or microphysical effects that act locally and/or on the cloud scale and will be referred to as adjustments in the following ( Stevens and Feingold 2009 ). Local and especially microphysical effects occur in any cloudy volume of air, while cloud-scale effects are usually associated with some degree of convection. Adjustments to aerosol-induced changes in droplet and crystal number are tightly linked to precipitation ( Stevens and

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Masaki Satoh and Yuya Matsuda

to directly calculate the mesoscale circulations associated with deep convection. Although recent studies show that much higher resolution is required to really resolve deep clouds ( Bryan et al. 2003 ; Deng and Stauffer 2006 ), we use the term CRMs to refer to numerical models that explicitly calculate deep convective circulations using a cloud microphysics scheme. CRMs can simultaneously produce deep convection and anvil clouds. Instead of the cloud parameterization of GCMs, the behaviors of

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Tatsuya Seiki, Chihiro Kodama, Akira T. Noda, and Masaki Satoh

; the modeling of those processes has been an issue over the last few decades. Another way to contribute to understanding cloud processes is global high-resolution simulations using detailed cloud microphysics schemes. Recently, global cloud-system-resolving models (GCRMs) and multiscale modeling frameworks (MMFs) have been developed to investigate the roles of convective cloud systems in large-scale atmospheric disturbances and general circulation using cloud microphysics schemes without cumulus

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Yign Noh, Donggun Oh, Fabian Hoffmann, and Siegfried Raasch

1. Introduction Warm cloud microphysical parameterizations usually divide the droplet spectrum within a cloud into cloud droplets and raindrops by size and calculates their physical quantities separately, following Kessler (1969 , hereafter K69 ). Cloud droplets with small terminal velocity are assumed to remain within a cloud, and larger raindrops with appreciable terminal velocities are assumed to settle gravitationally, causing precipitation. The value of a separation radius between

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