Tuesday, 9 January 2018: 1:45 PM
615 AB (Hilton) (Austin, Texas)
The Forecasting a Continuum of Environmental Threats (FACETs) research project contains eleven sub projects aimed at developing short-range, probabilistic guidance for many atmospheric hazards. One of our subproject objectives is to develop short-range calibrated probabilistic guidance for tornado intensity, especially for significant tornadoes defined as EF2 or greater on the Enhanced Fujita scale. The forecast guidance runs hourly out to 15 hours and is valid at 2-h intervals over the prior 4-h. This forecast guidance utilizes the research results led by Storm Prediction Center (SPC) forecasters Bryan Smith and Rich Thompson (Thompson et al. 2017). They manually examined a multi-year archive of radar-observed tornadic supercells to determine the statistical relationship between the significant tornado parameter (STP) in the near-supercell environment and the tornado EF-scale rating. Calibration tables were then created for EF0+, EF2+, and EF4+ tornadoes based on value ranges of STP.
The calibrated probability of tornado intensity guidance uses information from the last four hourly runs of the High-Resolution Rapid Refresh (HRRR) numerical model to create a time-lagged ensemble. The guidance also uses the last hourly run of the Rapid Refresh (RAP) numerical model. These are used to provide environment (RAP) and storm attribute (HRRR) forecast information. The maximum value of STP from the RAP over the 4-h period is taken at each grid point, and the statistical relationship between STP and observed tornado EF-scale distribution is used to determine conditional exceedance probabilities for various EF-scale ratings. This probability is then multiplied by the probability of 4-h maximum updraft helicity neighborhood probability ≥ 100 m^2/s^2 (representing the probability of a supercell) to create an “unconditional” probability of tornado intensity. The guidance was utilized and evaluated in the 2017 Hazardous Weather Testbed Spring Forecasting Experiment. Subjective evaluation and statistical verification show that the product can provide useful guidance to severe weather forecasters.
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