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Description

Reliable thunderstorm forecasts are essential to safety and resource protection at Cape Canaveral. Current methods of forecasting day-2 thunderstorms provide little improvement over forecasting by persistence alone and are therefore in need of replacement. This thesis focuses on using the mesoscale eta model to develop an index for improved forecasting of day-2 thunderstorms. Surface observations from the shuttle landing facility and the coincident output of the mesoscale eta forecast model were collected for the period of 1 May to 14 Sep 1998. Variables extracted from the eta forecast model output, as well as derived variables that incorporate the eta output variables, were divided into three data sets. A univariate logistic regression with the occurrence of a thunderstorm in the surface observation (the "truth") as the dependent variable, and the output/derived values from the eta model as the independent variable, discarded all but 94 of over 250 predictors that were considered important to thunderstorm occurrence. The data sets were further divided into model-building and validation sets for the purpose of building a logistic regression model. Regression coefficients were developed using the model-building sets and then applied to the validation sets for the purpose of forecasting a thunderstorm.