P12 Development of a nowcast modeling system to support Army applications

Dumais, Robert, Stephen Kirby, David Knapp, Jeff Passner, Brian Reen, United States Army Research Laboratory

To support the U.S. Army’s missions, rapid-update cycling Nowcast numerical weather predictions are required for operations in dynamic modeling domains (time and space) with rich to limited observation data available for model initialization. The WRF-ARW model has been adapted for the Army to provide gridded forecast output for use in various weather tools. The WRF-ARW’s Four Dimensional Data Assimilation method has been implemented to integrate and apply observations from various conventional and unconventional battlefield sources into the model’s initialization processes. This tailored version of the WRF-ARW, which takes advantage of available local and regional observation data sources, is called the Weather Running Estimate-Nowcast (WRE-N) modeling system. WRE-N is being designed to cycle frequently (such as hourly) in order to produce updated short-range forecasts (i.e; nowcasts) out to a few hours forward with output generated at 30- 60 minute increments and at finest horizontal grid point resolutions of 1-2 km or finer. Various nesting and cycling configurations are be tested for different Army applications, such as Artillery.