A Mass-Flux Cumulus Parameterization Scheme across Gray-Zone Resolutions

Abstract

A method that enables a mass-flux cumulus parameterization scheme (CPS) to work seamlessly in various model grids across CPS gray-zone resolutions is proposed. The convective cloud-base mass flux, convective inhibition, and convective detrainment in the simplified Arakawa–Schubert (SAS) scheme are modified to be functions of the convective updraft fraction. The combination of two updraft fractions is used to modulate the cloud-base mass flux; the first one depends on the horizontal grid space and the other is a function of the grid-scale and convective vertical velocity. The convective inhibition and detrainment of hydrometeors are also modified to be a function of the grid-size-dependent convective updraft fraction. A set of sensitivity experiments with the Weather Research and Forecasting (WRF) Model is conducted for a heavy rainfall case over South Korea. The results show that the revised SAS CPS outperforms the original SAS. At 3 and 1 km, the precipitation core over South Korea is well reproduced by the experiments with the revised SAS scheme. On the contrary, the simulated precipitation is widespread in the case of the original SAS experiment and there are multiple spurious cores when the CPS is removed at those resolutions. The modified mass flux at the cloud base is found to play a major role in organizing the grid-scale precipitation at the convective core. A 1-month simulation at 3 km confirms that the revised scheme produces slightly better summer monsoonal precipitation results as compared to the typical model setup without CPS.

The coupling of deep convection with the resolved flow via the divergence of mass flux in the IFS

“…Currently, f is an empirical function decreasing logarithmically with the horizontal grid spacing. This function has been tuned using model runs over Germany with resolutions from 10 to 1 km, with f ≈0.65 for δx = 5 km and f ≈0.25 for δx = 2 km to ensure vanishing subgrid fluxes in the limit of σ u = 0 (Arakawa and Wu, ; Kwon and Hong, ).…”

“…Numerous pathways have been pursued in recent years to adapt the traditional mass flux (MF) parametrization schemes for high resolutions by either including convective memory (Gérard and Geleyn, ; Piriou et al . ; Park, ) and/or rescaling of the convective fluxes to assure conservation of the total flux and vanishing subgrid convective fluxes in the limit of infinite horizontal resolution (Arakawa and Wu, ; Grell and Freitas, ; Gerard, ; Kwon and Hong, ).…”

“…Numerous pathways have been pursued in recent years to adapt the traditional mass flux (MF) parametrization schemes for high resolutions by either including convective memory (Gérard and Geleyn, 2005;Piriou et al, 2007;Park, 2014) and/or rescaling of the convective fluxes to assure conservation of the total flux and vanishing subgrid convective fluxes in the limit of infinite horizontal resolution (Arakawa and Wu, 2013;Grell and Freitas, 2014;Gerard, 2015;Kwon and Hong, 2017). Furthermore, it was early recognised (e.g Bretherton and Smolarkiewicz 1989) that the exchange of mass and momentum between the convective up/downdraughts and their environment occurs on multiple spatial and temporal scales via local mixing and gravity waves.…”

A Parameterization of Turbulent‐Scale and Mesoscale Orographic Drag in a Global Atmospheric Model

“…The physics package used here is similar to C2017 but with the advancement of computational cost and physical process. For example, the Rapid Radiative Transfer Model for GCMs scheme for shortwave and longwave radiation was improved in terms of computational cost (Baek, ), the Noah land surface model was upgraded from version 3.0 to the latest (version 3.4.1) with new surface input parameters (Koo et al, ), and the updated version of the simplified Arakawa‐Schubert convection scheme (Han et al, ; Kwon & Hong, ), which can be used in various model grids across gray‐zone resolutions, was used. In addition, a simple sea‐ice model was introduced with a newly created climatological data for ice thickness (Koo et al, ).…”

Impact of the Sea Surface Salinity on Simulated Precipitation in a Global Numerical Weather Prediction Model

“…The simulations for the medium-range forecasts in this study are conducted using the global NWP model in the GRIMs, which is not coupled with the three-dimensional ocean general circulation model. Spherical harmonics is selected as the dynamic core, and the physics parameterization schemes include the revised Rapid Radiative Transfer Model (RRTMG) for the general circulation models (Iacono et al, 2008, RRTMK;Baek, 2017); the Weather Research and Forecasting (WRF) single-moment 5-class (WSM5) microphysics scheme (Hong et al, 2004), with the effective hydrometeor radius for radiation (Bae et al, 2016); the simplified Arakawa-Schubert (SAS) cumulus convection scheme (Han et al, 2016;Hong & Pan, 1998;Kwon & Hong, 2017;Lim et al, 2014;Pan & Wu, 1995); the GRIMs shallow convection scheme (Hong & Jang, 2018); the prognostic cloudiness scheme (Park et al, 2016); the scale-aware Yonsei University (YSU) PBL scheme (Hong et al, 2006;Shin & Hong, 2015), with top-down turbulent mixing by the stratocumulus-topped boundary layer (Lee et al, 2018); the Noah land surface model (Chen & Dudhia, 2001;Ek et al, 2003;Koo et al, 2017); and the orographic, convective, and frontal spectral gravity wave drag parameterization schemes (Choi & Chun, 2011;Choi & Hong, 2015;Hong et al, 2008;Richter et al, 2010). The simple slab ocean mixed-layer model, based on the schemes that include wind-driven mixing, introduced by Pollard (1973), and the sea surface skin temperature, suggested by Zeng and Beljaars (2005), is included to estimate the diurnal SST variations in response to the net heat flux and wind stress at the surface (Kim & Hong, 2010).…”