An Automatic precipitation estimation technique (called the Auto-Estimator) has been developed for GOES-8/9 data. This algorithm has being used experimentally in NESDIS for estimating rainfall from convective systems during flash flood situations for 3 years. Characteristics of the Auto-Estimator include a rain rate curve based on the 10.7 micron temperature, and adjustments for cloud growth, cloud gradients, and moisture. Currently, the Auto-Estimator uses half hour data in the computation of the rainfall estimates. The objective of this paper is to determine the impact of rapid scan GOES-10 data on the quality of the half-hour derived rainfall estimates from the Auto-Estimator. A special rapid scan GOES-10 data set was collected from the middle of March to the middle of April. Digital satellite data was archived at 5 minute intervals for the following channels: visible, 3.9, 6.7 10.7 and 12 micron data. In order to obtain some ground truth information on rainfall, corresponding precipitation data sets have been collected from the stage 2 and 3 products (WSR-88D alone and WSR-88D adjusted by gauges, respectively) and from selected convective events that occurred over the Oklahoma Meso-Net (rain gauge information). Several comparisons are underway: 1-3-6-12 hour rainfall accumulations (from Stage 2 or 3 or the Meso-Net) compared against Auto-Estimator derived rainfall using half hour, 15 minute, and 5 minute interval data. Graphics of 5 and 15 minute derived rainfall estimates against Stage 2/3 and Meso-Net rainfall will also be presented. It is hoped that the rapid scan imagery will improve the detection of cirrus and delineation of precipitation cores. Such improvements will help rectify one of the greatest weaknesses of infrared derived precipitation estimation estimates - large overestimation of rainfall amounts and coverage due to cold-topped anvil debris