Using OSSEs to Assess the Impact of Assimilating Observations from a Boundary Layer Profiling Network on the Modeling of Severe Convective Environments

While numerical weather prediction (NWP) has continued to become increasingly sophisticated in the midst of continued advancement of computational capabilities, there remains much to improve, particularly on the convective scale. The very sparse global resolution of observations above the surface apply a rigid ceiling to the potential accuracy of NWP. Due to their small spatial extent and relatively short durations, the predictability of mesoscale and convective scale features is especially impacted by this observational data gap. These features largely evolve in response to changes in atmospheric structure that occur quickly and vary substantially over small spatial regimes, and are thereby mostly unobserved above the surface. Current operational NWP merely parameterizes these small scale variations resulting in inaccurate representations of their impacts on atmospheric phenomena. This talk discusses the results from observing system simulation experiments (OSSEs) that explore how assimilating observations from a variety of boundary layer profiling network configurations into the Warn-on-Forecast System (WoFS) can potentially improve ensemble representation of severe convective environments.