Impact of Assimilating High-Density GOES-Derived Wind Vectors on Numerical Analyses of Severe Storm Environments

The forecast accuracy of severe storms partly is limited by uncertainties of mesoscale shear variability in near-storm environments. Ongoing research by the coauthors and collaborators explores potential improvement in mesoscale details of shear surrounding severe convection by assimilating dense GOES-derived atmospheric motion vectors (AMVs; i.e., cloud drift winds) into convection-permitting WRF ensembles using the Ensemble Kalman filter. The primary goal of this is research is to understand the impact of AMVs on the numerical analysis of convective environments over the U.S. plains relative to the next most abundant conventional sources of upper air observations (e.g., routine operational radiosondes and commercial aircraft data). Prior to the operational status of the new GOES-16, which has the potential to make the highest quality AMV data sets of all previous GOES, our current work seeks to understand the impact of the rapid-scan GOES-11-derived AMVs collected on 5 June 2009. This date was selected because GOES-11 super-rapid-scan operations were performed during the period of severe storms that formed that afternoon-evening, including the highly-documented VORTEX2 Goshen County, Wyoming, tornadic supercell event. Several valuable VORTEX2 data sets were available for verification (e.g., proximity environment soundings).

Comparisons of shear analyses among mesoscale (4-km grid spacing) ensembles that assimilate different combinations of AMVs and operational wind data using a variety parameters (e.g., varying assumed observation error, vertical and horizontal localizations, observation density) suggest that the GOES winds often degraded observed wind shear profiles (verified with operational and VORTEX2 soundings). Degradation of the analyses, was generally maximized when AMV data were permitted their largest impact on the ensemble (e.g., largest prescribed localization, smallest assumed error, etc.). Although certain AMV-assimilating experiments minimize horizontal wind biases aloft, degradation still occurred below mid-levels of the troposphere, the shear layer most typically evaluated for supercell and tornado potential. Perturbations added via assimilation of AMV data increased ensemble error growth during periods of full disk scans, when AMVs were not available to constrain the analyses (albeit, to a somewhat degraded level).

Nested storm-scale (1.33 km grid-spacing) ensemble forecast experiments using initial conditions from the parent mesoscale domains with or without GOES AMVs assimilated will demonstrate the impact of the AMVs on storm-scale structure and severe weather metrics.