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Giovanni Leoncini, Roger A. Pielke Sr., and Philip Gabriel

numerically solve the equations of motion, but also use parameterizations to account for subgrid-scale processes (e.g., turbulence), short- and longwave radiative flux divergence, and other processes that cannot be explicitly simulated within the dynamical core that accounts for the pressure gradient, Coriolis effect, advection, and mass continuity. Land surface interactions are also among the parameterized processes. Because of computational constrains and limited physical knowledge, parameterizations

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Ming Liu, Jason E. Nachamkin, and Douglas L. Westphal

1. Introduction Solar and thermal infrared radiation is a fundamental mechanism for driving the energy exchange among air mass, clouds, aerosols, and land surface to maintain the thermal and dynamic systems in the atmosphere. The accurate prediction of atmospheric radiative processes, particularly cloud–radiation interaction, highly depends on the accurate calculation of radiative transfer fluxes (i.e., radiative transfer parameterizations). It has been well recognized that radiation modeling

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Matthew T. Bray, David D. Turner, and Gijs de Boer

Abstract

Despite a need for accurate weather forecasts for societal and economic interests in the U.S. Arctic, thorough evaluations of operational numerical weather prediction in the region have been limited. In particular, the Rapid Refresh Model (RAP), which plays a key role in short-term forecasting and decision making, has seen very limited assessment in northern Alaska, with most evaluation efforts focused on lower latitudes. In the present study, we verify forecasts from version 4 of the RAP against radiosonde, surface meteorological, and radiative flux observations from two Arctic sites on the northern Alaskan coastline, with a focus on boundary-layer thermodynamic and dynamic biases, model representation of surface inversions, and cloud characteristics. We find persistent seasonal thermodynamic biases near the surface that vary with wind direction, and may be related to the RAP’s handling of sea ice and ocean interactions. These biases seem to have diminished in the latest version of the RAP (version 5), which includes refined handling of sea ice, among other improvements. In addition, we find that despite capturing boundary-layer temperature profiles well overall, the RAP struggles to consistently represent strong, shallow surface inversions. Further, while the RAP seems to forecast the presence of clouds accurately in most cases, there are errors in the simulated characteristics of these clouds, which we hypothesize may be related to the RAP’s treatment of mixed-phase clouds.

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Keith M. Hines, Robert W. Grumbine, David H. Bromwich, and Richard I. Cullather

increasing static stability. The increasing static stability over East Antarctica contributes to decreasing turbulent heat flux downward to the surface ( King and Connolley 1997 ). Turbulent heat flux divergence, not radiative flux divergence, is the primary direct source of atmospheric cooling in the Antarctic winter boundary layer. The change with time of sensible heat flux and the 10-m wind speed are positively correlated for both East and West Antarctica ( Figs. 3b and 3d ). The decrease with time of

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Jia Sun, Hailun He, Xiaomin Hu, Dingqi Wang, Cen Gao, and Jinbao Song

6.7 m s −1 for the 1990–99, 2000–09, and 2010–17 periods, respectively ( Cangialosi 2018 ). One of the difficulties has to do with boundary layer processes, since air–sea heat fluxes are important energy sources for TCs ( Cione et al. 2013 ; Cione 2015 ). Larger transfer coefficients for heat and water vapor promote more heat absorption from the ocean, leading to greater TC intensity. Simultaneously, sea spray also supplies new heat pathways from ocean to TC, which are generated by the

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Kay Sušelj, Timothy F. Hogan, and João Teixeira

agree with a specific conceptual model of turbulence. Often, each of these parameterizations is based on a different conceptual model that is derived from a different archetypal turbulence structure regime. For example, planetary boundary layer parameterizations are most often derived by expansion of higher-order moments (e.g., Mellor and Yamada 1974 , 1982 ) and simplified to an eddy-diffusivity parameterization. Moist convection is usually represented by mass-flux models (e.g., Arakawa and

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Thierry Bergot, Dominique Carrer, Joël Noilhan, and Philippe Bougeault

able to provide enough valuable information, which would be useful in improving short-term ceiling and visibility predictions. The lack of accurate ceiling and visibility forecasts is the results of a variety of factors including the following: Quality forecasts are dependent upon having a higher density and dedicated observing network that can supply detailed information (e.g., radiative fluxes, vertical stability of the atmosphere, horizontal gradient of humidity). This high

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Ramesh Vellore, Darko Koračin, Melanie Wetzel, Steven Chai, and Qing Wang

at constant pressure, ρ a is the density of the air, w is the vertical component of the wind velocity, Z B is the mixed layer height (or height of the inversion base), F is the net radiative flux, and q L is the liquid water mixing ratio. The overbar represents a horizontal mean, and the primed quantities are turbulent fluctuations from the mean. Here, is the kinematic heat flux and is the total liquid water flux due to gravitational settling. The first term on the right-hand side

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Alexander Kann, Harald Seidl, Christoph Wittmann, and Thomas Haiden

radiative flux divergence at cloud top and misdiagnosis of the buoyant production of turbulence due to problems with the vertical differencing scheme. As part of the Austrian contribution to COST Action 722, the ability of the operational limited area model, Aire Limitée Adaptation Dynamique Développement International (ALADIN), in forecasting low stratus was evaluated. Results of the operational reference model are compared with a model version that includes an empirical enhancement scheme for

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Andrew W. Ellis and Daniel J. Leathers

1. Introduction At its peak in winter, snow covers approximately 46% of the land surface in the Northern Hemisphere, or about 46 million km 2 ( Robinson et al. 1995b ). Large changes in snow cover extent can modify atmospheric conditions through much of the earth’s troposphere due to the radiative effects of snow. Studies have indicated that snow cover can lower surface air temperatures over timescales of days to months by increasing the surface albedo and through the latent heat of melting (e

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