Future nowcasting systems must detect and retain extreme variations in the atmosphere, incorporate large volumes of high-resolution asynoptic data, and also be computationally very efficient. This will require 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 nowcasting is presented that uses LaGrangian techniques 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.
Real data tests are underway at CIMSS with the goals of identifying atmospheric details associated with the onset of significant weather events several hours in advance. 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) important to the development of convective instability 3-6 hours in advance, even after IR observations are no longer available due to cloud development. Results form case studies of hard-to-forecast isolated convective events show substantial skill in being able to define areas of developing convective instability 3-6 hours in advance using combinations of product images similar to those currently available for GOES derived product observations. Although these tests provide prototype examples of nowcast products that will be available at higher resolution using GOES-R ABI data, additional examples of the added impact of hyperspectral GEOS-R HES instrument will also be discussed using AIRS data. Plans will also be discussed for performing assessments of these products within selected NWS WFOs.