Cloud-to-ground lightning data from the National Lightning Data Network (NLDN), satellite visible and radar-derived products are used to train a lightning prediction algorithm. The radar reflectivity values are clustered to identify storm and real-time geometric, lagrangian and scalar attributes of those storms are computed. A lightning density field is "precast" to form the target decision field to be predicted using the computed attributes. Several days of data from the continental United States were chosen to obtain a seasonally and geographically diverse dataset for training. The trained system is used to predict lightning density and the predicted lightning density field is advected to produce a 30-minute nowcast field. The skill of the resulting algorithm is evaluated against both a persistence forecast and a steady-state prediction with motion correction.-->