Synoptic Control of Heavy-Rain-Producing Convective Training Episodes

We examine the degree to which the downscale cascade of information from synoptic scale motions constrains error growth in simulations of a particular organization of heavy-rain producing mesoscale convective systems (MCSs) known as training lines. Twenty-four cases of training convection over a 7-year period from 2000-2006 that produced extreme rainfall were dynamically downscaled from reanalysis data using a high-resolution convection-permitting configuration of the Weather Research and Forecasting model (WRF).

In most cases the model simulations were able to reproduce qualitative aspects of observed storm structure, including subjectively classified MCS archetype and training convection, despite the absence of mesoscale features in the reanalysis data sets used to provide initial conditions (ICs) and lateral boundary conditions (LBCs) to the simulations. Furthermore, model simulations were capable of predicting that a heavy precipitation event would occur in nearly every case. The horizontal resolution of the reanalysis data set used for ICs and LBCs did not result in measurable improvement in simulated precipitation placement skill relative to observations. We also establish a quantitative relationship between a measure of synoptic-scale uncertainty in the atmospheric state and errors between model forecasted and observed accumulated precipitation, wherein model errors tend to be larger when synoptic scale uncertainty is larger. This suggests that synoptic scale uncertainty in numerical weather prediction model simulations partially controls errors in the placement of heavy convective precipitation. The implications of these results are then discussed in the context of operational weather forecasting.