Abstract
This study examines the degree to which the downscale cascade of information from synoptic-scale motions constrains error growth in simulations of a particular type of heavy-rain-producing mesoscale convective system known as training lines. A total of 21 cases of training convection over a 7-yr period from 2000 to 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. The NCEP/Department of Energy (DOE)-II and Interim ECMWF Re-Analysis (ERA-Interim), representing lower- and higher-resolution datasets, respectively, were used for this purpose. In most cases the model simulations were able to reproduce qualitative aspects of observed storm structure, including subjectively classified mesoscale convective system archetype and training characteristics, despite the absence of mesoscale features in the reanalysis datasets used to provide initial conditions and lateral boundary conditions to the simulations. Furthermore, models were capable of predicting that a heavy-precipitation event would occur in nearly every case. Increasing the horizontal resolution of the reanalysis dataset used for initial conditions and lateral boundary conditions did not result in measurable improvement in simulated precipitation placement skill relative to observations. A quantitative relationship between a measure of synoptic-scale uncertainty in the atmospheric state and error in the model forecast accumulated precipitation was established, with larger synoptic-scale uncertainty tending to be associated with larger model error. This result suggests that synoptic-scale uncertainty in numerical weather prediction model simulations partially controls error in the placement of heavy convective precipitation.
Current affiliation: Department of Atmospheric Sciences, Colorado State University, Fort Collins, Colorado.
