Using the Short-Range Weather App and the Grell-Freitas Convection Scheme to Analyze the Mesoscale Convective Complex of June 24-26, 2023

Atmospheric convection affects mesoscale dynamics by changing vertical stability and redistributing heat and moisture. In atmospheric forecast models, such convective processes are often not completely resolved due to limits on model resolution (which is on the order of the grid cell size). Thus, one of the great challenges in numerical weather prediction is the accounting of the effects of unresolved convective clouds on resolved scales. This is accomplished through a technique known as convective parameterization. NOAA’s Rapid Refresh (RAP) model uses the Scale-Aware Grell-Freitas (SAGF) convection parameterization, which parameterizes convection in three modes: shallow, congestus, and deep. The SAGF executes in that order, with the convective tendencies generated from one mode applied as forcing terms to the next. The SAGF characterizes the vertical mass flux profiles, which incorporate the effects of updrafts, downdrafts, environmental mixing, and cloud microphysical processes, by using probability density functions to derive the entrainment and detrainment rates occurring in the three convection modes.

This research will evaluate the impact of the SAGF parameterization on forecasts from NOAA’s Unified Forecast System (UFS) Short-Range Weather (SRW) Application through comparison of runs using two different physics suites: the Rapid Refresh (RAP) physics suite, which includes the SAGF, and the HRRR (High Resolution Rapid Refresh) physics suite, which does not include it. Three SRW forecasts were initiated on June 24, 23 at 12z, one using the default RAP physics suite, one using the HRRR suite, and one with SAGF shallow convection turned off in the RAP suite. This case study was chosen because a decaying Mesoscale Convective System (MCS) in Iowa was forecast to redevelop along a Mixed-Layer CAPE (MLCAPE) boundary, a “pseudo-dryline”, risking production of damaging winds, large hail and tornadoes across much of the southeast United States.

This research will analyze how the SAGF affects categorical precipitation and reflectivity forecasts for this MCS case study by comparing forecast bias and critical success index scores between the three forecasts. In addition, since convective parameterizations provide important source terms to the evolution equations of heat, humidity, momentum and cloud microphysics species, temperature and specific humidity tendencies will be qualitatively analyzed to see how the SAGF with/without shallow convection affected the forecast in each experiment. Convection scheme-based tendencies have been found to be a principal component in model bias differences, and in these experiments, the presence/absence/form of the convective parameterization is the independent variable that is being tested. This research aims to use physics tendency information to help diagnose model error biases in order to contribute to improved forecast accuracy of community modeling systems and accelerate research to operations of the UFS SRW model.