92nd American Meteorological Society Annual Meeting (January 22-26, 2012)

Thursday, 26 January 2012: 9:00 AM
Statistical Analysis of Mesoscale Convective System Mountain Initiation Locations
Room 238 (New Orleans Convention Center )
Elisabeth F. Callen, University of Kansas, Lawrence, KS; and D. F. Tucker
Manuscript (508.3 kB)

Convective weather systems can be roughly divided into three categories: single cell systems, multicell systems, and mesoscale convective systems (MCSs). This paper will discuss the least numerous group MCSs. MCSs are major precipitation producers in the central United States and being able to improve forecasts of such precipitation producers would be very beneficial. The original data set for this paper was the gridded multi-sensor product used by Tucker and Li (2009). The multi-sensor data were divided into storms based on contiguous precipitation areas. Lifetimes and hourly positions of the systems were part of this data set. The storms were then divided into single cell systems, multicell systems, and MCSs using predetermined characteristics. Initiation points were estimated to be in the same location as the recorded positions for the systems in hour one. The MCS initiation points were further divided depending on where the initiation occurred. The MCSs that are the focus of this analysis are the ones which initiated west of 104 west longitude (mountain initiation) in the years 1996 to 2006 during the warm season in the Arkansas-Red River Basin area. In the Rocky Mountains, convective weather systems appear to have preferred initiation locations (Tucker and Crook 2005). Preliminary results indicate that MCS initiation locations in the Rocky Mountains may have even more focused initiation areas than single cell systems and multicell systems. To isolate the preferred initiation locations, a cluster analysis was performed on the initiation points of all the mountain-initiated MCSs. Seventy-six clusters were created in the cluster analysis. The largest resulting cluster contains 154 members and is the focus of this paper. We assume this cluster represents the most commonly occurring pattern. Variables from upper air data, surface data, and North American Regional Reanalysis (NARR) model data were used as input for a Principal Component Analysis (PCA) and multiple linear regression analysis to determine the synoptic and mesoscale conditions these cases have in common.

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