This document discusses applications of data mining techniques to predict mesoscale weather events like tornadoes and cloudbursts. It summarizes previous research that applied data mining methods like neural networks, support vector machines, and clustering to weather prediction. For tornado prediction, studies developed spatiotemporal models to identify relationships between storm variables. Other research used mesocyclone detection algorithms and neural networks to predict tornadoes. For cloudburst prediction, clustering relative humidity and divergence from numerical models provided early formation indications. The document also briefly explains ensemble forecasting, which runs multiple forecasts from slightly different initial conditions to sample forecast uncertainty.