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Convective Initiation Forecasts Through the Use of Machine Learning Methods

Detection and forecasting of convective initiation (CI) is an important problem, not only for aviation related purposes, but across all forms of business and general recreation. For aviation in particular, hazards related to thunderstorms, such as lightning, hail, strong winds and wind shear, are very costly to airlines through delays and wasted fuel in the events of holding or diversions. Current nowcasting systems, such as MIT Lincoln Laboratory’s Corridor Integrated Weather System (CIWS) (Wolfson et al. 2006) rely heavily on the tracking and trending of existing radar signatures of precipitation intensity and storm heights to produce forecasts, and as a result, often struggle with predicting new storm growth. Improving this capability is the subject of this work.