Future instruments (e.g., multi-channel geostationary imagers, Wind Profilers, automated aircraft reports, etc.) will resolve atmospheric features with resolutions far beyond today's capabilities in both time and space. Although these data are expected to improve NWP guidance at 6-12 hours and beyond, a greater benefit from these detailed time/space-frequency data (i.e., GOES) may come from objective nearcasting systems that assist forecasters in identifying rapidly developing, extreme weather events by helping to fill the 1-6 hour information gap which exists between nowcasts (based primarily on extrapolation of radar data) and longer-range NWP guidance.

Nearcasting systems must detect and retain extreme variations in the atmosphere (especially moisture fields) and incorporate large volumes of high-resolution asynoptic data, while also be extremely computationally efficient. This requires numerical approaches that are notably different from those used in numerical weather prediction, where the forecast objectives cover longer time periods.

A new approach to objective nearcasting is presented that uses Lagrangian techniques (instead of Eulerean methods used in conventional NWP) to optimize the impact and retention of information provided by satellites. It is designed to detect and preserve intense vertical and horizontal variations observed in the various data fields observed over time. Analytical tests have confirmed this, as well as the computational advantages of this approach.

Real data tests have been conducted with the goals of detecting the development of atmospheric details several hours prior the onset of significant weather events. Tests using full resolution (10 km) moisture products from current GOES sounders to update and enhance current operational RUC forecasts show that the Lagrangian system captures and retains details (maxima, minima and extreme gradients) critical to the development of convective instability several hours in advance, even after subsequent satellite observations are no longer available due to cloud development. Results from case studies of hard-to-forecast isolated convective events show substantial skill in being able to define areas of convective destabilization 3-6 hours in advance using combinations of product images similar to those currently available for GOES derived product observations. These tests provide prototype examples of nearcast products that will be available at higher resolution using GOES-R ABI data and possibly with Meteosat products as well. Plans will also be discussed for assessing of these products within selected NWS WFOs.