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<br />Numerical model calculations of selected case studies were also <br />processed to show, through automated graphical displays, the key thermo- <br />dynamic processes that control convective development. These MESOCU <br />simulations are discussed in Section 3.4. <br /> <br />Analyses of sounding properties are routinely performed on the EON <br />(Politte et al., 1980) using the ANALYZR program. Stability indexes and <br />profiles of thermodynamic variables were tabulated for each sounding and <br />summarized with MESOCU cloud-model results and radar data in the form <br />shown in Appendix B. These data were also compiled in a statistical <br />file for automatic data processing by the Statistical Package for the <br />Social Sciences (SPSS) (Nie et al., 1975). <br /> <br />3.4 Mesoscale Effects of Lifting on Convective Thermodynamics <br /> <br />Analyses of the effect of lifting on convective thermodynamics that <br />control cloud growth are simulated using the MESOCU cloud model devel- <br />oped by Kreitzberg and Perkey (1976) and modified to provide an analysis <br />of the convective potential (Matthews and Silverman, 1980). The convec- <br />tive potential index (CPI) is defined from a 3 h model simulation of <br />cloud-environment interaction as the total depth of all clouds predicted <br />by the model during that simulation. This arithmetic sum of the depths <br />of all clouds formed by the model is taken as a measure of the thermo- <br />dynamic potential for convective cloud growth of each sounding. The CPI <br />is a simple measure of the relative potential for convective cloud <br />growth which may be realized when synoptic or mesoscale lifting creates <br />potential buoyant energy (PBE) that can be converted into available <br />buoyant energy (ABE). Fritsch and Chappell (1980a) define the PBE as the <br />amount of buoyant energy which a convective environment may have if <br />49 <br />