Assimilating Near-Surface Observations from a Portable Mesoscale Network of StickNet Platforms During VORTEX-SE with the High Resolution Rapid Refresh Ensemble

The physical science component of the Verification of the Origins of Rotation in Tornadoes EXperiment – SouthEast (VORTEX-SE) broadly aims to diagnose how environmental factors unique to the southeast U.S. contribute to the formation, intensification, structure, and climatology of tornadoes. Heterogeneities in the near-surface state (e.g., due to advection, differential insolation, land-surface characteristics, terrain elevation) are an area of focus for many of these VORTEX-SE objectives. In order to properly sample the near-surface environment, a mobile network of observing platforms has been needed. Texas Tech University contributed 24 StickNet (Weiss and Schroeder 2008) observing platforms during the 2016, 2017, and Meso18-19 field phases. The platforms were positioned in a fixed “StesoNet” mesoscale array centered over Hunstville, AL with approximately 40-km spacing between stations; Meso18-19 featured reduced station spacing within the innermost portions of the observing domain. The platforms collected data at 10 Hz, observing u and v wind components, temperature, dewpoint, and pressure.

This unique and high-resolution dataset, positioned in a relatively data-sparse region near the surface, can be further exploited through data assimilation. Experimental versions of the High Resolution Rapid Refresh Ensemble (HRRRE) run at the Global Systems Division of NOAA ESRL are being utilized to investigate the utility of StickNet observations in surface data assimilation during four intensive observing periods of VORTEX-SE. Retrospective simulations are completed that both include and withhold hourly 1-min averaged bias-corrected StickNet observations of temperature, specific , and pressure to evaluate their influence on ensemble analyses and forecasts. Generally, StickNet observations are seen to induce coherent analysis increments of near-surface thermodynamics in early assimilation cycles, which quickly evolve nonlinearly as cycling continues. Forecasts of updraft helicity and reflectivity show improvements when StickNet observations are included for some IOPs, and degradation for other events. These discrepancies in forecast improvement, along with details regarding assimilation and forecast increments, will be discussed in the broader context of mesoscale data assimilation in the southeast United States towards improved forecasts of deep convection.