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1. Introduction Sensible and latent heat fluxes at the land or sea surface are usually calculated using bulk flux formulas derived from surface layer similarity theory ( Garratt 1992 ) that yields expressions for the requisite exchange coefficients. However, published measurements of sensible heat fluxes in the nearly neutral regime at high wind speeds are not completely in accord with the predicted values of the exchange coefficient for heat C H . Smedman et al. (2007b) report enhanced
1. Introduction Sensible and latent heat fluxes at the land or sea surface are usually calculated using bulk flux formulas derived from surface layer similarity theory ( Garratt 1992 ) that yields expressions for the requisite exchange coefficients. However, published measurements of sensible heat fluxes in the nearly neutral regime at high wind speeds are not completely in accord with the predicted values of the exchange coefficient for heat C H . Smedman et al. (2007b) report enhanced
1. Background For more than 50 years, Monin–Obukhov similarity theory (MOST) has been used to quantify near-surface exchanges of heat, moisture, and momentum in numerical weather prediction (NWP) models. MOST expresses gradients in surface-layer wind, temperature, and moisture fields as a function of a dimensionless stability length ζ , defined as (1) ζ = z − d L . In Eq. (1) , d is the displacement height of the vegetation, z is the sampling height, and L is the Monin
1. Background For more than 50 years, Monin–Obukhov similarity theory (MOST) has been used to quantify near-surface exchanges of heat, moisture, and momentum in numerical weather prediction (NWP) models. MOST expresses gradients in surface-layer wind, temperature, and moisture fields as a function of a dimensionless stability length ζ , defined as (1) ζ = z − d L . In Eq. (1) , d is the displacement height of the vegetation, z is the sampling height, and L is the Monin
1. Introduction Surface–atmosphere exchanges, mainly momentum, water, and heat surface fluxes, drive the boundary layer evolution and influence the formation of low-level clouds and more generally the synoptic flows and climate system. The modeling of these fluxes is performed by specific surface schemes: soil–vegetation–atmosphere transfer schemes for vegetation [ Chen et al. (1997) review the vegetation schemes used in the intercomparison exercise on the Cabauw, Netherlands, grass site
1. Introduction Surface–atmosphere exchanges, mainly momentum, water, and heat surface fluxes, drive the boundary layer evolution and influence the formation of low-level clouds and more generally the synoptic flows and climate system. The modeling of these fluxes is performed by specific surface schemes: soil–vegetation–atmosphere transfer schemes for vegetation [ Chen et al. (1997) review the vegetation schemes used in the intercomparison exercise on the Cabauw, Netherlands, grass site
1. Introduction The surface mixed layer of the ocean is a weakly stratified layer often encountered below the air–sea interface, where turbulent mixing is strong in response to atmospheric forcing. The processes that set the stratification and ventilation of the mixed layer are an essential part of the coupled climate system, because this layer regulates the exchange of heat, freshwater, and all other climatically relevant tracers between the atmosphere and the ocean. Traditional models assume
1. Introduction The surface mixed layer of the ocean is a weakly stratified layer often encountered below the air–sea interface, where turbulent mixing is strong in response to atmospheric forcing. The processes that set the stratification and ventilation of the mixed layer are an essential part of the coupled climate system, because this layer regulates the exchange of heat, freshwater, and all other climatically relevant tracers between the atmosphere and the ocean. Traditional models assume
1. Introduction The turbulent transfer of energy and momentum between the atmosphere and the earth’s surface determines the structure of the atmospheric boundary layer and the dry and moist convection, while also affecting the hydrological surface budget through evaporation. Although in recent years several turbulent transfer measurement projects have spread out a good number of fast-response instrumented meteorological towers, a global flux measurement network is far from being implemented for
1. Introduction The turbulent transfer of energy and momentum between the atmosphere and the earth’s surface determines the structure of the atmospheric boundary layer and the dry and moist convection, while also affecting the hydrological surface budget through evaporation. Although in recent years several turbulent transfer measurement projects have spread out a good number of fast-response instrumented meteorological towers, a global flux measurement network is far from being implemented for
coefficient is evaluated, a dimensionless constant σ = 5 for stable and 12 for unstable regimes (e.g., Zilitinkevich 1970 ), β = g / T 0 is the buoyancy parameter, g = 9.81 m s −2 , T 0 is the absolute temperature near the surface, and Pr is the Prandtl number. The gradient of potential temperature θ within the surface layer is calculated from that of the absolute temperature T as dθ / dz = dT / dz + γ a with the lapse rate γ a = −0.01 K m −1 . Based on Eq. (1) , the GG97
coefficient is evaluated, a dimensionless constant σ = 5 for stable and 12 for unstable regimes (e.g., Zilitinkevich 1970 ), β = g / T 0 is the buoyancy parameter, g = 9.81 m s −2 , T 0 is the absolute temperature near the surface, and Pr is the Prandtl number. The gradient of potential temperature θ within the surface layer is calculated from that of the absolute temperature T as dθ / dz = dT / dz + γ a with the lapse rate γ a = −0.01 K m −1 . Based on Eq. (1) , the GG97
1. Introduction Mesoscale models require land surface, surface layer, and planetary boundary layer (PBL) parameterizations to represent the transfer of heat, moisture, and momentum between the surface and atmosphere. A new land surface and PBL physical parameterization have been implemented in version 3.0 of the Weather Research and Forecasting model (WRF), Advanced Research WRF (ARW) core ( Skamarock et al. 2008 ). The Pleim–Xiu land surface model (PX LSM; Xiu and Pleim 2001 ; Pleim and Xiu
1. Introduction Mesoscale models require land surface, surface layer, and planetary boundary layer (PBL) parameterizations to represent the transfer of heat, moisture, and momentum between the surface and atmosphere. A new land surface and PBL physical parameterization have been implemented in version 3.0 of the Weather Research and Forecasting model (WRF), Advanced Research WRF (ARW) core ( Skamarock et al. 2008 ). The Pleim–Xiu land surface model (PX LSM; Xiu and Pleim 2001 ; Pleim and Xiu
). These surface fluxes are of crucial importance not only because they influence the steady state of the atmosphere, but also because they determine the mean profiles in the atmospheric boundary layer ( Holtslag and Nieuwstadt 1986 ; Beljaars and Holtslag 1991 ). The input parameters, such as exchange coefficients, boundary layer height, required in short-range forecasts and air pollution modeling are dependent on the surface fluxes. Further, in single-column and large-eddy simulation models for the
). These surface fluxes are of crucial importance not only because they influence the steady state of the atmosphere, but also because they determine the mean profiles in the atmospheric boundary layer ( Holtslag and Nieuwstadt 1986 ; Beljaars and Holtslag 1991 ). The input parameters, such as exchange coefficients, boundary layer height, required in short-range forecasts and air pollution modeling are dependent on the surface fluxes. Further, in single-column and large-eddy simulation models for the
1. Introduction In the oceanic surface mixed layer, primary turbulent processes include wind-driven shear turbulence, convective turbulence, surface wave breaking, and Langmuir circulation (LC). Previous results provide reliable wind-driven shear and convective turbulence scalings ( Shay and Gregg 1986 ; Lombardo and Gregg 1989 ). Recent studies report progress on the parameterization of turbulence mixing due to surface wave breaking ( Terray et al. 1996 ; Drennan et al. 1996 ; Anis and Moum
1. Introduction In the oceanic surface mixed layer, primary turbulent processes include wind-driven shear turbulence, convective turbulence, surface wave breaking, and Langmuir circulation (LC). Previous results provide reliable wind-driven shear and convective turbulence scalings ( Shay and Gregg 1986 ; Lombardo and Gregg 1989 ). Recent studies report progress on the parameterization of turbulence mixing due to surface wave breaking ( Terray et al. 1996 ; Drennan et al. 1996 ; Anis and Moum
1. Introduction The surface layer (SL) is the lowest portion of the planetary boundary layer (PBL), encompassing roughly 10% of the depth of the PBL. Understanding coastal SL weather is important for many applications, such as offshore wind farming, coastal oceanography, electromagnetic ducting, and human safety. Parameterization of vertical fluxes of momentum, heat, and moisture within this layer is currently necessary within numerical weather modeling and prediction (NWP), as even the
1. Introduction The surface layer (SL) is the lowest portion of the planetary boundary layer (PBL), encompassing roughly 10% of the depth of the PBL. Understanding coastal SL weather is important for many applications, such as offshore wind farming, coastal oceanography, electromagnetic ducting, and human safety. Parameterization of vertical fluxes of momentum, heat, and moisture within this layer is currently necessary within numerical weather modeling and prediction (NWP), as even the
