Using Blue Waters to Assess Tornadic Outbreak Forecast Capability by Lead Time

Volume 11, Issue 2 (April 2020), pp. 23–28

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@article{jocse-11-2-4,
  author={Caroline MacDonald and Andrew Mercer},
  title={Using Blue Waters to Assess Tornadic Outbreak Forecast Capability by Lead Time},
  journal={The Journal of Computational Science Education},
  year=2020,
  month=apr,
  volume=11,
  issue=2,
  pages={23--28},
  doi={https://doi.org/10.22369/issn.2153-4136/11/2/4}
}

Severe weather outbreaks come with many different hazards. One of the most commonly known and identifiable outbreaks are those with tornadoes involved. There has been some prior research on these events with respect to lead time, but shifts in model uncertainty by lead time has yet to be quantified formally. As such, in this study we assess tornado outbreak model uncertainty by lead time by assessing ensemble model precision for outbreak forecasts. This assessment was completed by first identifying five major tornado outbreak events and simulating the events using the Weather Research and Forecasting (WRF) model at 24, 48, 72, 96, and 120-hours lead time. A 10-member stochastically perturbed initial condition ensemble was generated for each lead time to quantify uncertainty associated with initialization errors at the varied lead times. Severe weather diagnostic variables derived from ensemble output were used to quantify ensemble uncertainty by lead time. After comparing moment statistics of several convective indices, the Energy Helicity Index (EHI), Significant Tornado Parameter (STP), and Supercell Composite Parameter (SCP) did the best job of characterizing the tornadic outbreaks at all lead times. There was good consistency between each case utilizing these three indices at all five lead times, suggesting outbreak model forecasting confidence may be able to extend up to 5 days for major outbreak events. These results will be useful for operational use by forecasters in forecast ability of tornadic events.