Benefits of the Warn on Forecast System in the Watch-to-Warning Period of Severe Events at NWS Blacksburg, VA

The Warn on Forecast System (WoFS), developed by the National Severe Storms Laboratory (NSSL), has demonstrated impressive utility in warning operations within the National Weather Service (NWS) during severe weather events. This experimental forecast system is still under development and is currently run for events when increased spatial and temporal resolution forecasts are particularly impactful. Two events when WoFS was run with a domain across the NWS Blacksburg, Virginia County Warning Area (CWA) occurred on August 7, 2023, and April 11, 2024. This research project analyzes the use of WoFS during both cases and its implementation into warning operations, particularly during the watch-to-warning phase of the severe events, based on forecaster and radar operator experiences and warning statistics. NWS Blacksburg covers 40 counties and 10 independent cities in southwest Virginia, southeast West Virginia, and northwest North Carolina. The CWA is bisected approximately northeast to southwest by the Blue Ridge Mountains of Virginia and North Carolina, which include the highest elevation in Virginia, Mount Rogers, at 5730 feet. To the east, the Piedmont of Virginia and North Carolina extends between the Blue Ridge Mountains and the coastal plains, presenting much flatter topography. The complex terrain within the CWA often adds a significant layer of difficulty and uncertainty to the warning decision process. The main conclusions of this project are that the WoFS runs are extremely valuable and useful in warning operations in areas of complex terrain, owing to its high spatial and temporal resolution. WoFS also provided important mesoscale data that helped radar operators evaluate the immediate downstream environment, resulting in increased confidence in warning decisions. Anticipating changes in the near-storm environment, and probabilities of severe weather and related hazards during the watch-to-warning period can be greatly enhanced by high-resolution ensemble forecasts like WoFS and other machine learning techniques.