On the Collective Importance of Model Physics and Data Assimilation on Mesoscale Convective System and Precipitation Forecasts over Complex Terrain

Abstract

Forecasting mesoscale convective systems (MCSs) and precipitation over complex terrain is an ongoing challenge even for convective permitting numerical models. Here, we show the value of combining mesoscale constraints to improve short-term MCS forecasts for two events during the North American monsoon season in 2013, including: 1) the initial specification of moisture, via GPS-precipitable water vapor (PWV) data assimilation (DA), 2) kinematics via modification of cumulus parameterization, and 3) microphysics via modification of cloud microphysics parameterization. A total of five convective-permitting Weather Research Forecasting (WRF) model experiments is conducted for each event to elucidate the impact of these constraints. Results show that combining GPS-PWV DA with a modified Kain-Fritsch scheme and double moment microphysics provides relatively the best forecast of both North American monsoon MCSs and convective precipitation in terms of timing, location, and intensity relative to available precipitation and cloud-top temperature observations. Additional examination on the associated reflectivity, vertical wind field, equivalent potential temperature, and hydrometeor distribution of MCS events show the added value of each individual constraint to forecast performance.