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Object-based verification of a prototype Warn-on-Forecast system.
Patrick S Skinner1,2, Dustan M Wheatley3, Kent H Knopfmeier1,2
1Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, Norman, Oklahoma.
Weather and Forecasting
|April 19, 2021
Summary
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.
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.
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