On the Predictability of Mesoscale Convective Systems: Three-Dimensional Simulations

Abstract

Mesoscale convective systems (MCSs) are a dominant climatological feature of the central United States and are responsible for a substantial fraction of warm-season rainfall. Yet very little is known about the predictability of MCSs. To help address this situation, a previous paper by the authors examined a series of ensemble MCS simulations using a two-dimensional version of a storm-scale (Dx 5 1 km) model. Ensemble member perturbations in the preconvective environment, namely, wind speed, relative humidity, and convective instability, are based on current 24-h forecast errors from the North American Model (NAM). That work is now extended using a full three-dimensional model.Results from the three-dimensional simulations of the present study resemble those found in two dimensions. The model successfully produces an MCS within 100 km of the location of the control run in around 70% of the ensemble runs using perturbations to the preconvective environment consistent with 24-h forecast errors, while reducing the preconvective environment uncertainty to the level of current analysis errors improves the success rate to nearly 85%. This magnitude of improvement in forecasts of environmental conditions would represent a radical advance in numerical weather prediction. The maximum updraft and surface wind forecast uncertainties are of similar magnitude to their two-dimensional counterparts. However, unlike the two-dimensional simulations, in three dimensions, the improvement in the forecast uncertainty of storm features requires the reduction of preconvective environmental uncertainty for all perturbed variables. The MCSs in many of the runs resemble bow echoes, but surface winds associated with these solutions, and the perturbation profiles that produce them, are nearly indistinguishable from the nonbowing solutions, making any conclusions about the bowlike systems difficult.

Convection-Allowing and Convection-Parameterizing Ensemble Forecasts of a Mesoscale Convective Vortex and Associated Severe Weather Environment

“…These results are consistent with more recent studies documenting rapid error growth at convective scales in convection-allowing models (e.g., Kong et al 2006Kong et al , 2007aZhang et al 2006;Hohenegger and Schä r 2007), and relatively poor warm season quantitative precipitation forecasting (QPF) over much of the United States (e.g., Fritsch and Carbone 2004), when the majority of rainfall is contributed by convective systems (e.g., Fritsch et al 1986;Schumacher and Johnson 2006). For a more thorough review of predictability at convective scales, see Lilly (1990) and Wandishin et al (2008).…”

Downstream influence of mesoscale convective systems. Part 1: influence on forecast evolution

“…Numerical weather prediction (NWP) models have difficulties representing MCSs due to their evolution from individual thunderstorms (Wandishin et al . ; ). For grid boxes of about 10 km or larger, convection needs to be parametrized.…”

Sensitivity of summer ensembles of fledgling superparameterized U.S. mesoscale convective systems to cloud resolving model microphysics and grid configuration

“…Given the dynamic limitations of the two‐dimensional CRM configuration used in SP‐CAM, which can distort MCS physics relative to three‐dimensional configurations in stand‐alone CRM simulations [ Schlesinger , ; Weisman et al ., ; Nicholls and Weissbluth , ; Wandishin et al ., ; Wandishin et al ., ], it is somewhat surprising that SP‐CAM is able to produce these propagating systems. While it may be tempting to imagine the individual CRM arrays in SP‐CAM as self‐contained models responding to an external large‐scale forcing, as is the case for limited‐domains CRM studies, the interactions across the CRM‐GCM interface are in fact more complicated than that.…”