The two primary goals of the CR ForecastBuilder experiment included (1) explore the operational benefits of using a common starting point at Weather Forecast Offices (WFO) to produce a scientifically sound, accurate, and consistent forecast, and (2) examine the operational benefits of using a standard tool within GFE (ForecastBuilder) to increase overall efficiency and aid in the development of forecasts at WFOs, potentially reducing the time spent collaborating and editing forecast grids within GFE.
The experiment demonstrated that ForecastBuilder successfully facilitated a fundamental shift in how forecast grids were produced. Leveraging the latest technology and science, such as a top-down approach to derive precipitation type probabilities (Baumgardt et al. 2017) and the freezing rain accumulation model (Sanders et al. 2016), ForecastBuilder improved overall WFO to WFO forecast consistency, resulted in a noticeable improvement in verification scores, and saved time for forecasters. As forecasters became more efficient using ForecastBuilder and began applying a “target of opportunity” approach to grid editing, they found they had additional time to analyze the weather situation, provide enhanced impact-based decision support services, and engage in training and professional development activities.