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Alex M. Kowaleski and Jenni L. Evans

; Emanuel and Rotunno 2011 ), ocean–air moist entropy disequilibrium at the radius of maximum winds (RMW) and tropopause temperature matter most in determining maximum TC intensity (expressed as either maximum sustained wind speed or minimum central pressure). Within the PI framework a TC functions as a Carnot engine; moist entropy is acquired from surface latent and sensible heat fluxes under the eyewall and exported at the much colder tropopause. The maximum moist entropy increase and tropopause

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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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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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John S. Kain, Michael E. Baldwin, and Steven J. Weiss

“updraft mass flux” (UMF*) field predicted by the scheme. The magnitude of this field provides a measure of how much mass this CPS transports through cloud base as part of its internal procedure for stabilizing the local environment. As such, it provides a unique prediction of convective intensity, a measure that is not always well correlated with the precipitation rate or any other routinely available output field. It is important to emphasize that UMF* is different from diagnostic parameters such as

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Richard J. Reed and Adrian J. Simmons

M^RCH 1991 NOTES AND CORRESPONDENCE 117Numerical Simulation of an Explosively Deepening Cyclone over the North Atlantic that was Unaffected by Concurrent Surface Energy Fluxes RICHARD J. REEDDepartment of Atmospheric Sciences, University of Washington, Seattle, Washington ADRIAN J. SIMMONSEuropean Centre for Medium Range

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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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Justin R. Minder, W. Massey Bartolini, Christopher Spence, Newell R. Hedstrom, Peter D. Blanken, and John D. Lenters

1. Introduction Lake-effect snow (LeS) storms occur when cold air is advected over relatively warm lake water. The resulting vertical temperature and moisture gradients drive strong turbulent surface sensible heat and moisture fluxes. These fluxes moisten and destabilize the boundary layer, leading to the formation of convective clouds, often shallow in depth, which may be organized into different morphologies by boundary layer and mesoscale circulations (e.g., LeMone 1973 ; Hjelmfelt 1990

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Paul A. Dirmeyer and Subhadeep Halder

segments of the feedback pathway: the terrestrial segment that links land states to surface fluxes that affect the atmosphere near the surface and the atmospheric segment that quantifies the responsiveness of the troposphere to surface fluxes ( Guo et al. 2006 ). In both segments, the feedbacks operate through the water and energy cycles. Most metrics are defined around a covariance or contingency relationship between variables expected to be physically connected through feedback processes, which are

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Yimin Ma, Noel E. Davidson, Yi Xiao, and Jian-Wen Bao

help us to understand TC development in an integrated way. The energy exchanges are composed of momentum, sensible heat, and latent heat (moisture) fluxes. Over water, a “bulk” exchange parameterization is commonly applied in numerical modeling to compute the fluxes in TC models. The fluxes are parameterized as being proportional to exchange coefficients and the differences in wind, temperature, and moisture between the surface and lowest model level in the boundary layer (e.g., Fairall et al

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Jongil Han and Christopher S. Bretherton

1. Introduction Recently, the National Centers for Environmental Prediction’s (NCEP) Global Forecast System (GFS) has implemented a hybrid eddy-diffusivity mass-flux (EDMF) planetary boundary layer (PBL) scheme replacing an eddy-diffusivity countergradient (EDCG) mixing scheme that underpredicted the daytime convective boundary layer development ( Han et al. 2016 ). In the hybrid scheme, the EDMF approach was applied to strongly unstable PBLs and the EDCG approach to weakly unstable PBLs. Use

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