The advent of the geosynchronous satellite with 1-km resolution has presented meteorologistis with the opportunity to routinely track and forecast smaller scale weather phenomena than hitherto was possible. The enormous data rate, however, precludes a completely manual system of data interpretation and forecasting, so to fully utilize this opportunity we must use computers. Following examples set by forecasters using digitized radar data, we are developing techniques to make guidance forecasts through automatic extrapolation of satellite imagery. Candidate techniques for extracting motion vectors from satellite imagery have been selected and programmed. This report describes the techniques and presents results of a preliminary test using simulated motions of selected satellite video images.

This report describes the development and testing of an objective technique to forecast cloudiness and precipitation through extrapolation of satellite imagery. By utilizing on objectively determined cloud-motion vector, the technique makes local forecasts of satellite parameters (brightness and IR temperature), with high temporal resolution, using simple linear extrapolation. Algorithms are then used to convert the satellite parameters to total cloud cover, probability of 1-hour precipitation, and presence of low, middle, and high clouds. The test program computed motion vectors and made forecasts out to 7 hours, in half-hour steps, at 30 locations. The program was tested on 12 spring and fall cases, using half-hourly GOES imagery. For periods beyond 2 hours, forecasts of cloud cover and precipitation were markedly better than persistence, which deficiencies in specification hindered short-period performance. Forecasts of cloud layers were worse than persistence due to inadequate specification algorithms. The net results were quite encouraging, and further refinements and developments are planned.