Synoptic Control of Heavy-Rain-Producing Convective Training Episodes

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 higherresolution 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 synopticscale uncertainty in numerical weather prediction model simulations partially controls error in the placement of heavy convective precipitation.

Using dynamical downscaling to examine mechanisms contributing to the intensification of Central U.S. heavy rainfall events

“…However, higher rainfall totals in WRF compared with Stage IV might be related to the identification of more light events in the Stage IV data set. Rainfall totals also had little dependence on the downscaled model even with large differences in their native resolutions (0.75° × 0.75° for CMCC‐CM and 1.5° × 1.5° for CNRM‐CM5), similar to results from Peters and Roebber [] and Harding et al [], which suggest that the parent model resolution has little impact on the simulation of heavy rainfall events in WRF.…”

Initial Results From the Super‐Parameterized E3SM

“…This is logical to expect given that it has been known for over a decade that models begin to resolve important mesoscale circulations at grid resolutions below 30 km (Moncrieff & Liu, ). Furthermore, MCSs can be significantly influenced by synoptic frontal systems (Peters et al, ; Schumacher et al, ) and orographic features (Houze, ), so the ability to accurately reproduce MCS precipitation statistics will be negatively affected if these synoptic scales remain underresolved. This is especially important in the central eastern United States, where GCMs continue to disagree on future projections (Maloney et al, ).…”

A numerical study of back-building process in a quasistationary rainband with extreme rainfall over northern Taiwan during 11–12 June 2012

“…As a common type of mesoscale convective systems (MCSs) with a lifespan around 3-12 h, organized rainbands such as squall lines are capable of producing persistent precipitation at high intensity, compared to ordinary, isolated, or scattered convection (e.g., Carbone, 1982;Bluestein and Jain, 1985;stratiform (TL/AS) type often forms along (or north of) an east-west (E-W) aligned, pre-existing slow-moving surface boundary (such as a front or a convergence line), and a series of embedded "training" cells move eastward (also Stevenson and Schumacher, 2014;Peters and Roebber, 2014;Peters and Schumacher, 2015). The second type is quasistationary back-building (BB) systems, which depend more on meso-and storm-scale forcing and processes.…”