Description

The Grell-Freitas (GF) scheme, as described in Grell and Freitas (2014) [73], Freitas et al. (2018) [61], Freitas et al. (2021) [62], and Lin et al. (2022) (under review) follows the mass flux approach published by Grell (1993) [74]. Further developments by Grell and \(D\acute{e}v\acute{e}nyi\) (2002) [72] included implementing stochastics through allowing parameter perturbations. In GF1 scale awareness, and the aerosol dependence through rain generation (following Berry (1968) [26] and evaporation formulations (following Jiang et al. (2010) [99] ), depending on the cloud concentration nuclei at cloud base were added. FG included mixed phase physics impact, momentum transport (as in ECMWF), a diurnal cycle closure (Bechtold et al. (2014) [13] ), and a trimodal spectral size to simulate the interaction and transition from shallow, congestus and deep convection regimes. In order for this trimodal size spectrum to be accurately represented, GF's deep and shallow convective schemes must be run together. The vertical massflux distribution of shallow, congestus and deep convection regimes is characterized by Probability Density Functions (PDF's). The three PDF's are meant to represent the average statistical mass flux characteristic of deep, congestus, and shallow (respectively) plumes in the grid area. Each PDF therefore represents a spectrum of plumes within the grid box. Forcing is different for each characteristic type. Entrainment and detrainment are derived from the PDF's. The deep convection considers scale awareness (Arakawa et al. (2011) [8] ), the congestus type convection as well as the shallow convection are not scale-aware. Aerosol dependence is implemented through dependence of rain generation and evaporation formulations depending on the cloud concentration nuclei at cloud base (Berry 1968 [26], Jiang et al.(2010) [99], and Lee and Feingold (2010) [111] ). Aerosol dependence is considered experimental and is turned off at this point. GF is able to transport tracers.

A paper describing the latest changes and modifications is in process and will be submitted to GMD.

CCPP Physics Updates

Version: CCPP v6.0.0

GPU capabilities have been added
Cap suppressing (do_cap_suppress) based on radar data assimilation has been added. This is used only for the RAP suite
Some fixed parameters have been made scale-aware
Updated coupling between radiation and convection has been implemented

Operational Impacts in RAP/HRRR

Uses mass-flux schemes, which are more physically realistic than (sounding) adjustment schemes
Takes parameterization uncertainty into account by allowing parameters from multiple convective schemes which can be perturbed internally or with temporal and spatial correlation patterns
For higher resolutions (less than 10 km), in addition to scale awareness as in Arakawa et al. (2011) [8] GF can transition as grid spacing decreases into a shallow convection scheme
Coupled to the grid scale precipitation and radiation schemes through passing of diagnosed cloud liquid and ice from simulated precipitating convective cloud and shallow convective clouds

Intraphysics Communication

The GF scheme passes cloud hydrometeors to the grid-scale microphysics scheme (Thompson Aerosol-Aware Cloud Microphysics Scheme ) through detrainment from each convective cloud layer containing convective cloud. The detrained condensate interacts with short- and longwave radiation by contributing to the "opaqueness" to radiation of each grid layer. Additionally, detrained condensate is added to any existing condensate, to be treated by the complex grid-scale microphysics scheme. This allows for a crude emulation of stratiform precipitation regions in the RAP.

Additionally, the shallow convection and PBL schemes pass cloud information to the radiation scheme, which improved cloud/radiation interaction and retention of the inversion typically found above mixed layers.

Argument Table

General Algorithm

Grell-Freitas Cumulus Scheme Driver General Algorithm