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Object-based verification of a prototype Warn-on-Forecast system.

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An object-based verification method for the NSSL Experimental Warn-on-Forecast System for ensembles (NEWS-e) shows decreasing skill over 3 hours but retains value. Forecast skill improves with larger, more intense storm objects and in environments with higher severe thunderstorm risk.

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Area of Science:

  • Meteorology
  • Atmospheric Science
  • Weather Forecasting

Background:

  • The NSSL Experimental Warn-on-Forecast System for ensembles (NEWS-e) aims to improve severe weather prediction.
  • Object-based verification is crucial for evaluating ensemble forecast systems like NEWS-e.

Purpose of the Study:

  • To develop and apply an object-based verification methodology for the NEWS-e system.
  • To establish baseline performance metrics for NEWS-e thunderstorm and mesocyclone forecasts.

Main Methods:

  • Developed an object-based verification methodology for NEWS-e.
  • Matched NEWS-e forecast objects (composite reflectivity, updraft helicity tracks) to Multi-Radar Multi-Sensor data.
  • Utilized contingency table-based verification statistics.

Main Results:

  • NEWS-e critical Success Index (CSI) for reflectivity and updraft helicity forecasts decreased over 3 hours but retained skill.
  • Rotation track forecasts showed lower scores due to high frequency bias.
  • 2017 reflectivity forecasts showed increased skill, attributed to improved initial conditions.

Conclusions:

  • The object-based verification methodology provides valuable performance metrics for NEWS-e.
  • NEWS-e demonstrates retained skill in forecasts up to 3 hours.
  • Forecast skill is influenced by object characteristics and environmental conditions, with improvements noted for larger, more intense storms.