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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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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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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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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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Yefim Kogan

1. Introduction The parameterization of clouds in numerical models depends among other things on the model’s grid size. The most accurate parameterization is possible in large-eddy simulation (LES) models that employ high spatial resolution and, therefore, are capable of accurate description of turbulent dynamics. In particular, finescale resolution of individual updrafts/downdrafts allows accurate calculation of local supersaturation and, therefore, a physically grounded representation of

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Steve Vavrus and Duane Waliser

paleoclimatic evidence and the existence of well-accepted amplifying processes such as the ice–albedo feedback. However, a major caveat of accurate climate model projections of Arctic amplification is the uncertain response of cloudiness, stemming from the historically unrealistic representation of polar clouds in the present climate system. This bias could distort the sign and magnitude of the simulated cloud feedback. The goals of this paper are to describe a parameterization that improves the simulation

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Han-Gyul Jin and Jong-Jin Baik

hailstones. Because the characteristics of these three ice hydrometeor types greatly differ from each other, the precipitation characteristics may vary significantly depending on the growth of each hydrometeor type by the accretion process (e.g., Wang and Georgakakos 2005 ; Morrison and Milbrandt 2011 ). Therefore, parameterizations for the accretion process that yield good performance should be used in cloud models for better precipitation predictions. In recent years, several cloud microphysics

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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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Charmaine N. Franklin, Paul A. Vaillancourt, and M. K. Yau

. Recent cloud physics studies such as that by Riemer and Wexler (2005) have used parameterizations developed from DNS that used a frozen flow field and nonsedimenting droplets. Franklin et al. (2005) demonstrated that the collision kernel in a frozen flow field can overestimate that in an evolving flow by 30%. In this study we have developed parameterizations specifically for cloud physics applications and demonstrate that these newly developed parameterizations reduce the errors of some of the

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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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