Session 8A.5 Verification of Supercell Cold Pools in High-Resolution WRF Simulations using StickNet In Situ Data

Tuesday, 12 October 2010: 2:30 PM
Grand Mesa Ballroom F (Hyatt Regency Tech Center)
Anthony E. Reinhart, Texas Tech University, Lubbock, TX; and C. C. Weiss and D. C. Dowell

Presentation PDF (2.5 MB)

Numerical model errors in supercellular cold pools at high resolution are being investigated in the Weather Research and Forecasting (WRF) model, initialized with conventional Doppler radar data using ensemble Kalman filter (EnKF) assimilation techniques. High-resolution simulations are necessary to resolve individual updrafts. However, as spatial resolution increases, the simulation becomes prone to systematic errors due to assumptions within each parameterization scheme. For instance, the default microphysical parameterization used in most numerical simulations are bulk single-moment, which tend to overestimate the strength of the cold pool. This study will investigate the biases present within supercellular cold pools generated by common microphysical parameterizations (single-moment and double-moment schemes) used in the WRF model.

The second Verification of the Origins of Rotation in Tornadoes Experiment (VORTEX2) has collected a unique dataset of high-resolution in situ data on supercell thunderstorms. These observations pertain to all portions of the supercell thunderstorm including the supercell cold pool. The cold pool is an agent in the generation of horizontal baroclinic vorticity, which has been shown in some studies to affect tornado potential. Inaccuracies in the simulated cold pool will bias results of updraft intensity and the amount of horizontal baroclinic vorticity generated. For this study several cases from the VORTEX2 dataset will be simulated.

Verification of the numerically simulated supercell cold pool will use data gathered during VORTEX2, especially from the StickNet platforms, which spanned the entirety of the precipitation core in numerous cases. Using these high-resolution data for verification allows for a more precise error calculation both spatially and temporally. This verification will allow for determining if certain parts of the model-developed cold pool are more susceptible to error.

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