Influence of grid aspect ratio on planetary boundary layer turbulence in large-eddy simulations

Abstract

Abstract. We examine the influence of the grid aspect ratio of horizontal to vertical grid spacing on turbulence in the planetary boundary layer (PBL) in a large-eddy simulation (LES). In order to distinguish them as much as possible from other artificial effects caused by numerical schemes, we used a fully compressible meteorological LES model with a fully explicit scheme of temporal integration. The influences are investigated with a series of sensitivity tests with parameter sweeps of spatial resolution and grid aspect ratio. We confirmed that the mixing length of the eddy viscosity and diffusion due to sub-grid scale turbulence plays an essential role in reproducing the theoretical −5/3 slope of the energy spectrum. If we define the filter length in LES modeling based on consideration of the numerical scheme, and introduce a corrective factor for the grid aspect ratio into the mixing length, the theoretical slope of the energy spectrum can be obtained; otherwise, spurious energy piling appears at high wavenumbers. We also found that the grid aspect ratio has influence on the turbulent statistics, especially the skewness of the vertical velocity near the top of the PBL, which becomes spuriously large with large aspect ratio, even if a reasonable spectrum is obtained.

A convective‐scale 1,000‐member ensemble simulation and potential applications

“…Our simulation applies the Scalable Computing for Advanced Library and Environment Localized Ensemble Transform Kalman Filter (SCALE‐LETKF) DA system (Lien et al ., ). The SCALE‐LETKF system combines the open source Scalable Computing for Advanced Library and Environment Regional Model (SCALE‐RM: version 5.1.2; see Nishizawa et al ., ; Sato et al ., ; Nishizawa and Kitamura, ) and a Localized Ensemble Transform Kalman Filter (LETKF: Hunt et al ., ). The LETKF assimilates conventional observations using a 3‐hr assimilation window on the 15‐km grid.…”

Impacts of cloud microphysics on trade wind cumulus: which cloud microphysics processes contribute to the diversity in a large eddy simulation?

“…Based on the rough estimation of Nishizawa et al (2015), the non-dimensional coefficient (γ) should be smaller than O (10 . The strength of the numerical diffusion for each γ is shown in Table 3.…”

“…The time step of dynamics was determined by the Courant-Friedrichs-Lewy (CFL) condition for the acoustic wave. The time step for physics (except for cloud microphysics) was set as 10 × Δt dyn based on the sensitivity experiment (Nishizawa et al, 2015). The Δt microphy was determined in sensitivity experiments examining the ratio of Δt microphy to Δt dyn .…”

30-second-Update 100-m-Mesh Data Assimilation Experiments: A Sudden Local Rain Case in Kobe on 11 September 2014

“…Recently, Miyoshi et al (2016aMiyoshi et al ( , 2016b reported an innovation of the "Big Data Assimilation" (BDA) technology, implementing a 30-second-update, 100-m-mesh local ensemble transform Kalman filter (LETKF; Hunt et al 2007) to assimilate data from a Phased Array Weather Radar (PAWR) at Osaka University (Ushio et al 2014) into regional NWP models known as the Japan Meteorological Agency non-hydrostatic model (JMA-NHM, Saito et al 2006Saito et al , 2007 and the Scalable Computing for Advanced Library and Environment-Regional Model (SCALE-RM, Nishizawa et al 2015). The PAWR captures the rapid development of convective activities every 30 seconds at approximately 100-m resolution.…”

“…Among others, much attention has been paid to skillful NWP for severe weather (e.g., Kain et al 2006, Hohenegger and Schär 2007a, b;Kawabata et al 2007;Roberts and Lean 2008). Recently, the ensemble Kalman filter (EnKF;Evensen 1994Evensen , 2003 has become a major method in data assimilation (DA), and has contributed to investigate convection-permitting regional NWP (e.g., Zhang et al 2007;Stensrud et al 2009Stensrud et al , 2013Clark et al 2010;Schwartz et al 2010; Baldauf et al 2011;Melhauser and Zhang 2012; Yussolf et al 2013, Kunii 2014a, Weng and Zhang 2016.Recently, Miyoshi et al (2016aMiyoshi et al ( , 2016b reported an innovation of the "Big Data Assimilation" (BDA) technology, implementing a 30-second-update, 100-m-mesh local ensemble transform Kalman filter (LETKF;Hunt et al 2007) to assimilate data from a Phased Array Weather Radar (PAWR) at Osaka University (Ushio et al 2014) into regional NWP models known as the Japan Meteorological Agency non-hydrostatic model (JMA-NHM, Saito et al 2006Saito et al , 2007 and the Scalable Computing for Advanced Library and Environment-Regional Model (SCALE-RM, Nishizawa et al 2015). The PAWR captures the rapid development of convective activities every 30 seconds at approximately 100-m resolution.…”