Combining Satellite and Radar Data for the Short-Range Forecasting of Precipitation

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Abstract
An algorithm yielding probability of rain from GOES visible-infrared imagery and simultaneous radar data is applied over a satellite image the size of eastern Canada. It is then mapped by means of a conic projection an a constant resolution Cartesian grid to facilitate overlay with synoptic charts. A pattern recognition technique is applied to 16 subareas of the entire map and has proved successful in tracking the displacement of the probability-of-rain contours. The potential of the system for making short-range precipitation forecasts is discussed briefly. Abstract An algorithm yielding probability of rain from GOES visible-infrared imagery and simultaneous radar data is applied over a satellite image the size of eastern Canada. It is then mapped by means of a conic projection an a constant resolution Cartesian grid to facilitate overlay with synoptic charts. A pattern recognition technique is applied to 16 subareas of the entire map and has proved successful in tracking the displacement of the probability-of-rain contours. The potential of the system for making short-range precipitation forecasts is discussed briefly.

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