Abstract

An investigation of several hundred mesoscale convective systems (MCSs) during the warm seasons (April–August) of 1996–98 is presented. Circular and elongated MCSs on both the large and small scales were classified and analyzed in this study using satellite and radar data. The satellite classification scheme used for this study includes two previously defined categories and two new categories: mesoscale convective complexes (MCCs), persistent elongated convective systems (PECSs), meso-β circular convective systems (MβCCSs), and meso-β elongated convective systems (MβECSs). Around two-thirds of the MCSs in the study fell into the larger satellite-defined categories (MCCs and PECSs). These larger systems produced more severe weather, generated much more precipitation, and reached a peak frequency earlier in the convective season than the smaller, meso-β systems. Overall, PECSs were found to be the dominant satellite-defined MCS, as they were the largest, most common, most severe, and most prolific precipitation-producing systems. In addition, 2-km national composite radar reflectivity data were used to analyze the development of each of the systems. A three-level radar classification scheme describing MCS development is introduced. The classification scheme is based on the following elements: presence of stratiform precipitation, arrangement of convective cells, and interaction of convective clusters. Considerable differences were found among the systems when categorized by these features. Grouping systems by the interaction of their convective clusters revealed that more than 70% of the MCSs evolved from the merger of multiple convective clusters, which resulted in larger systems than those that developed from a single cluster. The most significant difference occurred when classifying systems by their arrangement of convective cells. In particular, if the initial convection were linearly arranged, the mature MCSs were larger, longer-lived, more severe, and more effective at producing precipitation than MCSs that developed from areally arranged convection.

Highlights

  • Mesoscale convective systems (MCSs) provide a significant portion of the precipitation that falls over the central United States during the critical agricultural growing seasons of spring and summer

  • MCSs produce a broad range of severe convective weather events (Maddox et al 1982 and Houze et al 1990) that can be potentially damaging to crops and society in general

  • Due to its global coverage, infrared satellite imagery has been an important means of studying MCSs, mesoscale convective complexes (MCCs) (e.g. Maddox 1980, Maddox et al 1982, and Maddox 1983)

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Summary

Scatter plot of maximum area against duration

6.2 Scatter plot of maximum area against total totals index. 6.3 Scatter plot of maximum area against precipitable water. 7.1 Plot of MCS tracks for all systems. 7.7 Plot of MCS tracks for systems that were not embedded. 7.8 Plot of MCS tracks for areal systems. 7.9 Plot of MCS tracks for line systems. 7.10 Plot of MCS tracks for combination systems. 7.11 Plot of MCS tracks for merger systems. 7.12 Plot of MCS tracks for growth systems. 7.13 Plot of MCS tracks for isolated systems. 7.20 Infrared satellite lifecycle composite for systems that were n01; embedded. 7.21 Infrared satellite lifecycle composite for areal systems. 7.22 Infrared satellite lifecycle composite for line systems. 7.23 Infrared satellite lifecycle composite for combination systems. 7.24 Infrared satellite lifecycle composite for merger systems. 7.25 Infrared satellite lifecycle composite for growth systems. 7.26 Infrared satellite lifecycle composite for isolated systems.

MCS definitions based upon analysis of infrared satellite data
Distribution of MCS development by satellite classification
Number of severe weather reports for each radar classification
Introduction
Background
Satellite classification of MeSs
Radar classification of MeSs
Radar classification by organization
Radar classification by development
Study area and period
Satellite data
Dataset information
Satellite analysis
Radar data
Radar analysis
Sounding data and severe weather reports
Definition of classes
Basic characteristics
Examples of classes
Findings
Classification by organization
Full Text
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