Toward dynamic adaptivity: steering the WRF model on the Unidata LEAD test bed

An important aspect of the LEAD project is dynamic adaptivity. In LEAD, dynamic adaptability is the notion of performing meteorological analysis and forecasting on demand in response to the weather. An algorithm for choosing a location of interest based on predicted precipitation from the NAM forecast has been devised at Unidata/UCAR (See Paper in IIPS Cyberinfrastructure session). The center Latitude and Longitude of this location is being utilized to steer both the Workstation Eta and WRF models (the latter running on the Unidata LEAD test bed) with each prediction cycle (4 times daily).

The results of these regional NWP runs are being stored on the test bed, cataloged using THREDDS and distributed via OPeNDAP. We've also generated IDV bundles that allow one to view the most recent regional runs as compared with the most recently received NAM data. This work is intended to enable the LEAD team to work toward developing these and far more sophisticated capabilities within the GRID environment in which it is being developed. This paper will also discuss ongoing work to assimilate real time data for initialization of the WRF forecast.