Improved Length Scales for Turbulence Kinetic Energy–Based Planetary Boundary Layer Scheme for the Convective Atmospheric Boundary Layer

Abstract

<jats:title>Abstract</jats:title> <jats:p>Based on a priori analysis of large-eddy simulations (LESs) of the convective atmospheric boundary layer, improved turbulent mixing and dissipation length scales are proposed for a turbulence kinetic energy (TKE)-based planetary boundary layer (PBL) scheme. The turbulent mixing length incorporates surface similarity and TKE constraints in the surface layer, and makes adjustments for lateral entrainment effects in the mixed layer. The dissipation length is constructed based on balanced TKE budgets accounting for shear, buoyancy, and turbulent mixing. A nongradient term is added to the TKE flux to correct for nonlocal turbulent mixing of TKE. The improved length scales are implemented into a PBL scheme, and are tested with idealized single-column convective boundary layer (CBL) cases. Results exhibit robust applicability across a broad CBL stability range, and are in good agreement with LES benchmark simulations. It is then implemented into a community atmospheric model and further evaluated with 3D real-case simulations. Results of the new scheme are of comparable quality to three other well-established PBL schemes. Comparisons between simulated and radiosonde-observed profiles show favorable performance of the new scheme on a clear day.</jats:p>

Stability Dependence of the Turbulent Dissipation Rate in the Convective Atmospheric Boundary Layer

“…BouLac's L ɛ profiles overlap for all cases regardless of − z i / L o . This is because the upward and the downward length scales ( l u and l d ) in the BouLac scheme are virtually insensitive to atmospheric stability (Zhou et al., 2020). The opposing and the missing stability dependence in Figure 2c explain the biases of the parameterized ɛ found in Figures 1e and 1f.…”

“…Five quasi‐steady state CBL simulations from Zhou et al. (2020) are adopted for this study. The LESs are conducted by the Advanced Regional Prediction System (ARPS) developed at the Center for Analysis and Prediction of Storms at the University of Oklahoma (Xue et al., 2000, 2001).…”

Image Collection Simulation Using High-Resolution Atmospheric Modeling

“…The simulation uses the following physics packages: Thompson cloud microphysics [18], Rapid Radiative Transfer Model for General Circulation Models (RRTMG) radiative transfer [19], and 1.5-order Turbulence Kinetic Energy (TKE) subgrid-scale turbulence [20]. The Thompson scheme is a bulk cloud microphysics scheme that calculates evolutions and interactions of cloud hydrometeors in the categories of cloud liquid, cloud ice, rain, snow, and graupel; the RRTMG scheme calculates radiative transfer and heating due to longwave and shortwave fluxes based on the correlated k-distribution method [21][22][23]; the 1.5-order TKE scheme represents diffusion due to subgrid-scale turbulence based on the turbulent kinetic energy predicted based on wind shear, buoyancy, or external forcing [24][25][26].…”

Relationship between Snow and Temperature over Some Iraqi Meteorological Stations

“…S now is a type of precipitation in the form of fine ice crystals that occurs in the cold season, but it does not occur in all countries of the world. Snow abundance and intensity increase as move closer to the North and South poles [1], [2]. This phenomenon takes place at the convergence of moist and warm air currents with cold currents whose temperature is 12.5 °C (285.65K) below 0 °C (273.15K), and for the formation of snow, the condensation nuclei on snow are very small solid particles suspended in the upper atmosphere such as dust particles or ash, and when they exist, the three conditions that enable steam to be available water is to transform from the gaseous state to the solid state, forming ice [3].…”