Climatological characteristics of mesoscale convective vortices (MCVs) occurring in the state of Oklahoma during the late spring and summer of four years are investigated. The MCV cases are selected based on vortex detection by an objective algorithm operating on analyses from the Rapid Update Cycle (RUC) model. Consistent with a previous study, true MCVs represent only about 20% of the mesoscale relative vorticity maxima detected by the algorithm. The MCVs have a broad range of radii and intensities, and their longevities range between 1 and 54 h. Their median radius is about 200 km, and their median midlevel relative vorticity is 1.2 x 10(-4) s(-1). There appears to be no significant relationship between MCV longevity and intensity. Similar to past estimates, approximately 40% of the MCVs generate secondary convection within their circulations. The mean synoptic-scale MCV environment is determined by the use of a RUC-based composite analysis at four different stages in the MCV life cycle, defined based on vortex detection by the objective algorithm. MCV initiation is closely tied to the diurnal cycle of convection over the Great Plains, with MCVs typically forming in the early morning, near the time of maximum extent of nocturnal mesoscale convective systems (MCSs). Features related to the parent MCSs, including upper-level divergent outflow, midlevel convergence, and a low-level jet, are prominent in the initiating MCV composite. The most significant feature later in the MCV life cycle is a persistent mesoscale trough in the midlevel height field. The potential vorticity (PV) structure of the composite MCV consists of a midlevel maximum and an upper-level minimum, with some extension of elevated PV into the lower troposphere as the vortex matures. The environment immediately downshear of the MCV is more conducive to secondary convection than the environment upshear of the MCV. This midlatitude MCV climatology represents an extension of past individual case studies by providing mean characteristics of a large MCV population; these statistics are suitable for the verification of MCV simulations. Also presented is the first high-resolution composite analysis of the MCV environment at different stages of the MCV life cycle, which will aid in identifying and forecasting these systems.
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