Improving Cape Canaveral's Next-Day Thunderstorm Forecasting Using a Meso-ETA Model-Based Index

摘要

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 research focused on using the mesoscale eta model to develop an index for improved forecasting of day-2 thunderstorms. Logistic regression techniques were used to regress the occurrence of a thunderstorm at Cape Canaveral against day-2 forecast variables output, or derived, from the mesoscale eta model. Accuracy and bias scores were calculated for the forecasts made by the regression equations, and the forecast results were compared to persistence and to model-based forecasts of the Neumann-Pfeffer Thunderstorm Index (NPTI). For cases where the results were shown to be statistically significant, the forecasts made using the logistic regression equations (called the Eta Thunderstorm Index (ETI)) consistently outperformed both persistence and the NPTI. Due to the small sample size used in this research, further study on this topic is encouraged.