8B.3 Developing a Quantitative Measure of Convective Forcing to Evaluate High Resolution Rapid Refresh Ensemble (HRRRE) Variance

Tuesday, 8 November 2016: 5:00 PM
Pavilion Ballroom West (Hilton Portland )
Amber J. Liggett, Millersville Univ., Beaver, PA; and T. T. Ladwig, D. C. Dowell, and C. R. Alexander

Hazardous weather events have the greatest impact when they are not accurately forecasted, yielding costly repairs and leaving people injured. Longer lead times of accurate forecasts will enhance weather situational awareness for a variety of severe weather watch and warning applications, including aviation to prevent traffic delays, hospitals and nursing homes which require 30 minutes or more to relocate patients to safety, and residents of low-lying areas threatened by flash flooding. Past studies suggest that utilizing Convective Allowing Models (CAM) and ensemble forecasting is important for issuing severe weather watches and warnings. Stemming from successful hourly updated forecasting by the High Resolution Rapid Refresh (HRRR) model, the High Resolution Rapid Refresh Ensemble (HRRRE) utilizes ensemble data assimilation for improved convective scale forecasts. The HRRRE was run in real-time experimentally during the Spring 2016 Hazardous Weather Testbed Experimental Forecast Program (HWT-EFP).

HRRRE and other CAM guidance skill can vary widely in different weather regimes. Convective forcing is hypothesized to influence forecast skill and CAM ensemble variance. Understanding the correlation between forcing and ensemble skill/variance has the potential to enhance the future HRRRE ensemble design. To study this relationship, an objective measure of convective forcing is required.

This study developed the Reflectivity Convective Forcing Categorization (RCFC), a quantitative method to categorize convective forcing using Multi-Radar Multi-Sensor (MRMS) composite reflectivity observations. Both reflectivity coverage and rate of change of reflectivity were examined for the months of May and June 2016 utilizing RCFC. Measuring forcing based on MRMS radar observations allowed the classification to be independent from model analyses and forecasts. Several events exemplifying strong and weak forcing regimes were qualitatively analyzed using Storm Prediction Center (SPC) mesoscale analyses, upper air maps, and surface analyses, to verify the RCFC method. With verification, the RCFC method will be utilized as the primary HRRRE measure for convective forcing.

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