Using simulations to explore how assimilating observations from a boundary layer profiling network impacts WoFS ensemble forecasts of significant severe weather events

Rapid advancements to computational capabilities, and the resulting proliferation of high-resolution convective allowing models (CAMs), have recently revolutionized severe weather forecasting. However, CAMs are still far from perfect, and are heavily limited by the availability of observational data. Operational observations above the surface are severely limited in space and time, leaving much of the three-dimensional structure of the atmosphere poorly resolved. This is especially true for the atmospheric boundary layer (ABL), where small-scale heterogeneities driven by turbulence are merely parameterized, even in the most sophisticated operational models. Numerous features and processes, some of which play an important role in the evolution of convective storms, also exist in the ABL. As such, a boundary layer profiling network could provide the observations necessary to fill data gaps that are likely a major limiting factor to accuracy of CAM forecasts. Results from experiments using simulated observations from two significant tornado events in central OK are analyzed to provide insight into the potential impact of a boundary layer profiling network on convective forecasts, as well as how such a network could be designed to optimize its utility while minimizing cost.