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1. Introduction Large-eddy simulations (LESs) of the dry convective boundary layer, with homogeneous bottom boundary conditions and in the absence of mean wind, are found to be dominated by plumes and thermals whose horizontal and vertical scales are comparable to the boundary layer depth z * (e.g., Schmidt and Schumann 1989 ). These length scales seem to require some explanation, given that when the equations of inviscid fluid dynamics are linearized about a convectively unstable basic
1. Introduction Large-eddy simulations (LESs) of the dry convective boundary layer, with homogeneous bottom boundary conditions and in the absence of mean wind, are found to be dominated by plumes and thermals whose horizontal and vertical scales are comparable to the boundary layer depth z * (e.g., Schmidt and Schumann 1989 ). These length scales seem to require some explanation, given that when the equations of inviscid fluid dynamics are linearized about a convectively unstable basic
1. Introduction The mixed layer is one of the three layers into which the convective boundary layer can be divided, with the surface layer and the entrainment zone lying respectively beneath and above it. It is characterized by an intense vertical mixing that tends to leave variables such as potential temperature and humidity nearly constant with height, even wind speed and direction ( Stull 1988 ). On Earth, it typically encompasses 40%–70% of the convective boundary layer height. The Martian
1. Introduction The mixed layer is one of the three layers into which the convective boundary layer can be divided, with the surface layer and the entrainment zone lying respectively beneath and above it. It is characterized by an intense vertical mixing that tends to leave variables such as potential temperature and humidity nearly constant with height, even wind speed and direction ( Stull 1988 ). On Earth, it typically encompasses 40%–70% of the convective boundary layer height. The Martian
1. Introduction Large-scale parallel computing has the potential to alter the landscape of turbulence simulations in the atmospheric and oceanic planetary boundary layers (PBLs) as increased computer power using O (10 4 –10 5 ) or more processors ( National Science Foundation 2007 ) will permit large-eddy simulations (LESs) of turbulent PBLs coupling small and large scales in realistic outdoor environments. Applications include, atmosphere–land interactions ( Patton et al. 2005 ), boundary
1. Introduction Large-scale parallel computing has the potential to alter the landscape of turbulence simulations in the atmospheric and oceanic planetary boundary layers (PBLs) as increased computer power using O (10 4 –10 5 ) or more processors ( National Science Foundation 2007 ) will permit large-eddy simulations (LESs) of turbulent PBLs coupling small and large scales in realistic outdoor environments. Applications include, atmosphere–land interactions ( Patton et al. 2005 ), boundary
1. Introduction The zero-order bulk model (ZOM) ( Zilitinkevich 1991 ) predicts well enough the mean entrainment rate of a dry, shear-free convective boundary layer (CBL) with constant surface buoyancy flux growing into a linearly stratified fluid. However, the ZOM cannot predict accurately the parameters that affect the mean entrainment rate (which we call entrainment rate parameters), such as the entrainment ratio and the buoyancy increment of CBLs with relatively thick entrainment zone (EZ
1. Introduction The zero-order bulk model (ZOM) ( Zilitinkevich 1991 ) predicts well enough the mean entrainment rate of a dry, shear-free convective boundary layer (CBL) with constant surface buoyancy flux growing into a linearly stratified fluid. However, the ZOM cannot predict accurately the parameters that affect the mean entrainment rate (which we call entrainment rate parameters), such as the entrainment ratio and the buoyancy increment of CBLs with relatively thick entrainment zone (EZ
polar tropospheric chemistry. One important factor influencing surface trace gas levels is the atmospheric boundary layer. Studies at the South Pole (SP) have shown that a combination of conditions—including low snow accumulation rates allowing for efficient recycling of nitrogen, emissions of nitrogen oxides (NO x : NO + NO 2 ) from the snowpack, a long fetch allowing NO x accumulation in the surface layer, and sustained shallow stable boundary layers—promote elevated levels of nitric oxide (NO
polar tropospheric chemistry. One important factor influencing surface trace gas levels is the atmospheric boundary layer. Studies at the South Pole (SP) have shown that a combination of conditions—including low snow accumulation rates allowing for efficient recycling of nitrogen, emissions of nitrogen oxides (NO x : NO + NO 2 ) from the snowpack, a long fetch allowing NO x accumulation in the surface layer, and sustained shallow stable boundary layers—promote elevated levels of nitric oxide (NO
1. Introduction The boundary layer is known to play an important role in the energy transport processes of a hurricane, regulating the radial and vertical distributions of momentum and enthalpy that are closely related to storm development and intensification (e.g., Ooyama 1969 ; Emanuel 1986 ; Wroe and Barnes 2003 ; Smith et al. 2008 ; Rotunno et al. 2009 ; Smith and Montgomery 2010 ). Numerical studies have shown that the simulated hurricane intensity is very sensitive to the selection
1. Introduction The boundary layer is known to play an important role in the energy transport processes of a hurricane, regulating the radial and vertical distributions of momentum and enthalpy that are closely related to storm development and intensification (e.g., Ooyama 1969 ; Emanuel 1986 ; Wroe and Barnes 2003 ; Smith et al. 2008 ; Rotunno et al. 2009 ; Smith and Montgomery 2010 ). Numerical studies have shown that the simulated hurricane intensity is very sensitive to the selection
1. Introduction Understanding the dynamical processes that occur in the lower atmosphere and upper ocean is important for a full understanding of air–sea interaction. Correct parameterization of the air–sea momentum fluxes in the atmospheric boundary layer is important for atmospheric, oceanic, and wave models. Currently, large-scale models only allow the momentum flux, τ tot , to be positive, from atmosphere to ocean. Recent observations have reported unusual behavior during conditions of
1. Introduction Understanding the dynamical processes that occur in the lower atmosphere and upper ocean is important for a full understanding of air–sea interaction. Correct parameterization of the air–sea momentum fluxes in the atmospheric boundary layer is important for atmospheric, oceanic, and wave models. Currently, large-scale models only allow the momentum flux, τ tot , to be positive, from atmosphere to ocean. Recent observations have reported unusual behavior during conditions of
2002 ; James et al. 2006 ; Richardson et al. 2007 ). Weckwerth et al. (1996) have shown that the thermodynamic variability observed among soundings within the boundary layer strongly depends on whether the sounding is launched through the updraft or downdraft of circulations induced by boundary layer convective overturning. Soundings launched through a boundary layer updraft depicted deeper, richer moist layers, implying less convective inhibition (CIN) and larger convective available potential
2002 ; James et al. 2006 ; Richardson et al. 2007 ). Weckwerth et al. (1996) have shown that the thermodynamic variability observed among soundings within the boundary layer strongly depends on whether the sounding is launched through the updraft or downdraft of circulations induced by boundary layer convective overturning. Soundings launched through a boundary layer updraft depicted deeper, richer moist layers, implying less convective inhibition (CIN) and larger convective available potential
. Section 2 describes the dye experiments while section 3 characterizes the tidal period and spring–neap variations in mixing observed with the dye tracer. In section 4a we focus on flood tide mixing and show that boundary layer growth occurs due to entrainment and that this growth rate is consistent with the Trowbridge (1992) model. In section 4a we also access the relative roles of entrainment and tidal straining to boundary layer growth. This is followed by analysis that quantifies vertical
. Section 2 describes the dye experiments while section 3 characterizes the tidal period and spring–neap variations in mixing observed with the dye tracer. In section 4a we focus on flood tide mixing and show that boundary layer growth occurs due to entrainment and that this growth rate is consistent with the Trowbridge (1992) model. In section 4a we also access the relative roles of entrainment and tidal straining to boundary layer growth. This is followed by analysis that quantifies vertical
1. Introduction In this paper, a climatology of 11 yr of observations of the nocturnal boundary layer (NBL) is presented in terms of geostrophic wind speed. As we will focus on clear-sky cases only, mechanical forcing is expected to be the major factor determining the evolution of the NBL. For offline numerical studies of the NBL, for example, using single-column models or large-eddy simulations (LESs), the geostrophic wind is often taken as an a priori known external parameter that is either
1. Introduction In this paper, a climatology of 11 yr of observations of the nocturnal boundary layer (NBL) is presented in terms of geostrophic wind speed. As we will focus on clear-sky cases only, mechanical forcing is expected to be the major factor determining the evolution of the NBL. For offline numerical studies of the NBL, for example, using single-column models or large-eddy simulations (LESs), the geostrophic wind is often taken as an a priori known external parameter that is either
