Assimilation of UAS-Supercell Datasets in an OSSE Framework

Unmanned Aircraft Systems (UAS) have the potential to characterize the thermodynamic and kinematic state in the vicinity of supercell thunderstorms. The impact of UAS observations on simulated supercells is being assessed through Observing System Simulation Experiments (OSSEs), wherein synthetic observations are “collected” by a simulated UAS within a high-resolution idealized simulation (termed the nature run) that represents the true atmospheric state. The nature run was developed using the First-Generation Pennsylvania State University/National Center for Atmospheric Research Cloud Model (CM1), and was initialized in an environment consisting of open-cell boundary layer convection and deep-layer vertical wind shear sufficient to support a supercell. Realistic boundary layer structures were seeded using random thermal perturbations in the lowest 1 km in the nature run initial conditions and were maintained through inclusion of radiative parameterization, thermal and moisture surface fluxes, and a semi-slip surface. An aircraft model was developed to sample synthetic data from the storm environment in a manner consistent with recent fixed-wing UAS operations in supercell environments. The impact on forecasts achieved through assimilation of these synthetic data into coarse-resolution idealized simulations will be discussed. This comparison of the nature run predictions (truth) to the coarse-run predictions allow for evaluation of the impact UAS data could have on real time convection allowing models (CAMs).