This study evaluated the ability of the High Resolution Rapid Refresh (HRRR) modeling system to forecast the characteristics of mesoscale atmospheric boundaries arising from thunderstorm outflows, gust fronts, and downburst winds (referred collectively as convective outflows) within the contiguous United State and Alaska. Such convective outflows in the vicinity of wildfires can lead to rapid changes in fire behavior and growth that increase risks to firefighters. Our objective was to develop and evaluate diagnostic tools based on HRRR model output that could improve situational awareness within the operational fire weather community of the ability of the HRRR model to nowcast and forecast convective outflows at lead times of 18 hours or less. 

Key findings:

• The HRRR model can facilitate nowcasting at lead teams less than 6 hours and does improve situational awareness for the potential for convection at lead times less than 18 hours, particularly in synoptic-mesoscale situations for which the model is well initialized as part of the mode’s data assimilation procedures. 
• The GOES-Lightning Mapper sensors onboard GOES-16 and GOES-17 satellites were shown to be useful for evaluating the HRRR forecasts of lightning potential as a proxy for forecasts of intense convection.

Future work:
The utility of model output requires well-designed wildfire metrics and trained personnel to diagnose local trends and thresholds applied to model-derived products in order to provide accurate guidance. Such automated alerts, and the criteria they are derived from, will require extensive verification, calibration, and validation. Criteria based on objective model guidance are useful for situational awareness, but not of sufficient accuracy for actionable decisions.