Since 2017, the Warn-on-Forecast System (WoFS) has been tested and evaluated during the Hazardous Weather Testbed Spring Forecasting Experiment (SFE) and summer convective seasons. The system has shown promise in predicting high temporal and spatial specificity of individual evolving thunderstorms. However, this baseline version of the WoFS has a 3-km horizontal grid spacing and cannot resolve some convective processes. Efforts are under way to develop a WoFS prototype at a 1-km grid spacing (WoFS-1km) with the hope to improve forecast accuracy. This requires extensive changes to data assimilation specifications and observation processing parameters. A preliminary version of WoFS-1km nested within WoFS at 3 km (WoFS-3km) was developed, tested, and run during the 2021 SFE in pseudo–real time. Ten case studies were successfully completed and provided simulations of a variety of convective modes. The reflectivity and rotation storm objects from WoFS-1km are verified against both WoFS-3km and 1-km forecasts initialized from downscaled WoFS-3km analyses using both neighborhood- and object-based techniques. Neighborhood-based verification suggests WoFS-1km improves reflectivity bias but not spatial placement. The WoFS-1km object-based reflectivity forecast accuracy is higher in most cases, leading to a net improvement. Both the WoFS-1km and downscaled forecasts have ideal reflectivity object frequency biases while the WoFS-3km overpredicts the number of reflectivity objects. The rotation object verification is ambiguous as many cases are negatively impacted by 1-km data assimilation. This initial evaluation of a WoFS-1km prototype is a solid foundation for further development and future testing.
This study investigates the impacts of performing data assimilation directly on a 1-km WoFS model grid. Most previous studies have only initialized 1-km WoFS forecasts from coarser analyses. The results demonstrate some improvements to reflectivity forecasts through data assimilation on a 1-km model grid although finer resolution data assimilation did not improve rotation forecasts.
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