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Hyunho Lee and Jong-Jin Baik

–coalescence between cloud droplets that forms raindrops, thus plays an important role in the growth of drops in clouds. There have been many attempts to parameterize the autoconversion in bulk microphysics schemes, which are summarized in section 2 . Although the autoconversion is intrinsically the collision–coalescence between cloud droplets, most of the bulk microphysics schemes have parameterized the autoconversion based on the simple fitting to the observation data or to the results of bin microphysics

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Yefim L. Kogan and Alexei Belochitski

1. Introduction Parameterization of clouds in numerical models is complicated because of the need to account for processes on a wide range of scales. Large-eddy simulation (LES) models employ high spatial resolution and, therefore, are capable of accurate description of turbulent dynamics that, in turn, is the foundation for physically grounded representation of cloud microphysics. The latter can be implemented in LES models in two ways. The first approach, referred to as explicit microphysics

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Elie Bou-Zeid, Marc B. Parlange, and Charles Meneveau

nonnegligible (negligible wind shears corresponding to free convection conditions are a rather rare occurrence in the surface layer). In fact, MO similarity remains the primary approach for the computation of regional-scale surface fluxes using measurements in the atmospheric surface layer and for the parameterization of the lower ABL in large-scale weather and climate models ( Taylor 1987 ; Mason 1988 ; Claussen 1991 ; Parlange et al. 1995 ; Bou-Zeid et al. 2004 ). The MO similarity was developed for

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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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David B. Mechem and Yefim L. Kogan

to a “wet” nucleus of 20.2 μ m [see Table 1 in Kogan (1991) ], in the range of drop sizes that can initiate coalescence. Bulk microphysical parameterizations rely on a process termed autoconversion to represent the formation of embryonic drizzle drops by the coalescence of cloud drops. Various formulations of the autoconversion term exist, for example, based on simple ad hoc assumptions ( Kessler 1969 ; Tripoli and Cotton 1980 ), large eddy simulation (LES) with size-resolving microphysical

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Romy Ullrich, Corinna Hoose, Ottmar Möhler, Monika Niemand, Robert Wagner, Kristina Höhler, Naruki Hiranuma, Harald Saathoff, and Thomas Leisner

Intergovernmental Panel on Climate Change (IPCC AR5; Boucher et al. 2013 ) and Komurcu et al. (2014) showed that the simulated ice clouds in global climate models are very sensitive to the implemented parameterization of the aerosols’ ice-nucleating ability. The early descriptions for heterogeneous ice nucleation by Fletcher (1962) or Meyers et al. (1992) are neither aerosol specific nor applicable over the whole tropospherically relevant temperature and humidity range. The first ice-nucleating particle

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Jorgen S. Frederiksen and Steven M. Kepert

1. Introduction In recent years, there has been increased interest in developing and employing improved dynamical subgrid-scale parameterizations for atmospheric circulation models ( Koshyk and Boer 1995 ; Frederiksen et al. 1996 , 2003 ; Frederiksen and Davies 1997 ; Kaas et al. 1999 ) and for numerical weather prediction models ( Buizza et al. 1999 ; Palmer 2001 ). In particular, it has been demonstrated that some of the problems in climate simulations such as resolution dependence of

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Philippe Lopez

parameterizations of moist processes still rely on crude simplifying assumptions either for the sake of computational efficiency or because of uncertainties about individual processes, in particular microphysics and cloud-scale transport. In the past 10 yr, some progress has also been achieved in the assimilation of observations affected by clouds and precipitation in NWPMs with the aim of producing more realistic initial atmospheric states (or analyses ). Such measurements are already widely available with a

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Songmiao Fan, Paul Ginoux, Charles J. Seman, Levi G. Silvers, and Ming Zhao

obtain a balance of radiation in the models ( McCoy et al. 2016 ). An accurate representation of ice nucleation and mixed-phase clouds is essential not only for extending the range of numerical weather forecast but also for quantifying cloud feedbacks in future climate scenarios ( Tan et al. 2016 ). Various parameterizations of ice nucleation, ice crystal concentration, and ice–liquid phase partitioning have been implemented in atmospheric models, resulting in a wide spread of results among climate

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Han-Gyul Jin, Hyunho Lee, and Jong-Jin Baik

equation (SCE); this equation is also called the kinetic collection equation, population balance equation, or Smoluchowski equation. However, many bulk microphysics schemes still parameterize the accretion of cloud water by graupel using the simple continuous collection equation. Several attempts have been made to derive an analytic solution of the SCE for the accretion of cloud water by graupel ( Verlinde et al. 1990 ; Gaudet and Schmidt 2005 ; Seifert and Beheng 2006 ). Some bulk microphysics

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