P6.9 AN OBJECTIVE NOWCASTING TOOL THAT OPTIMIZES THE IMPACT OF GOES DERIVE PRODUCT IMAGERY IN VERY-SHORT-RANGE FORECASTS AND NOWCASTS

Many future instruments (e.g., Wind Profilers networks, automated aircraft reports and the Hyperspectal Environmental Sounder planned for GOES-R) have the capability of resolving atmospheric features beyond today’s capabilities in both time and space. Although these data are expected to generate improvements in numerical forecast guidance out to 48 hours and beyond, a greater benefit from these high-time-frequency and detailed data sources may come from their use in real time objective nowcasting systems designed to assist forecasters with identifying rapidly developing, small-scale extreme weather events. These nowcasting systems will need to detect and retain extreme variations in the atmosphere, incorporate large volumes of high-resolution asynoptic data from satellites and other high-resolution systems, and be very computationally efficient. Accomplishing this will require numerical approaches and techniques that are notably different from those used in numerical weather prediction where the forecast objectives cover longer time periods. The nowcasting systems will need to place an emphasis on retaining the accuracy of individual observations and preserving the large gradients seen in these data through time. Speed, however, will be of the essence, since in many cases the detailed information provided in the observations is extremely perishable. The basis for a new approach to objective nowcasting is presented that uses LaGrangian techniques to optimize the impact and retention of information provided by multiple observing systems. Real data tests are shown in whic