Airspace utilization and safety could benefit significantly from accurate, real-time, short-term predictions of hazardous weather regions (e.g., 5-30 minutes). For some hazards, such as heavy turbulence, the detection process itself is in an immature stage. No universally accepted algorithm exists for indicating the regions of current turbulence - let alone predicting it. For other hazards, such as hail and more particularly for heavy rain, the detection process is in a more mature state. In fact heavy rain may be unambiguously associated with high dBZ (reflectivity), if no ice phases are present. Hail is also associated with high reflectivities. We have therefore chosen to place our initial emphasis on the prediction of reflectivity contours in the context of ATC (air traffic control) operations. For all or our prediction techniques, we begin by collecting fixed dBZ-level contours on a fixed-elevation scan by fixed-elevation scan basis, and then combining these elevation cell slices into volume cells as is done in the algorithm of Bjerkaas and Forsyth (1980). To these volume cells we attach translations vectors to make the desired prediction: at this time no provision is made for the growth or decay of reflectivity cells. We generate our translation vectors using each of several algorithms which have already been described elsewhere. Firstly, we use the centroid-tracking approach of Bjerkaas and Forsyth (1980). This is the current tracker of choice in the NEXRAD (Next Generation Weather Radar) program. Secondly, we use tracking vectors of clusters of volume cells, as described ny Crane (1979): much of this work was performed under the sponsorship of the Federal Aviation Administration (FAA). Thirdly, we generate translation vectors by cross-correlating low-altitude (0-4 cm) CAPPIs (constant-altitude plan position indicators): this correlation is done either for the entire storm, or for 30 km by 30 km segments of the storm. This approach has been motivated by the work of Rinehart and Garvey (1978), although we generally use a CAPPI of liquid water content. Fourthly, we use as a prediction the current, composite reflectivity map - our so-called status-quo prediction.