The higher temporal sampling of the PAR data finally provides a more fluid perspective of storm evolution, which was exciting to observe!   This proved particularly beneficial for the fast evolving storm cases, where 1 minute volume scans finally gave warning decision makers a fighting chance in getting some warning lead time, especially for rapidly evolving tornadic storms.  Another benefit of the higher temporal sampling of PAR data was the ability to diagnose feature persistence, particularly with rapidly evolving mesocyclones.  In the 4 to 5 minute volume scans of the current WSR-88D design, rapidly evolving tornadic mesocyclones may be captured by one volume scan after which time the warning decision maker is left to speculate, is this feature going to be persistent or transient?  Should I warn, or should I wait for one more volume scan?  If I wait, will I sacrifice potentially life-saving lead time? Or, if the feature is not persistent, will I reduce my False Alarm Rate (FAR) and potentially increase credibility in the public’s eye from having not warned for a null event?    In cases like these, the environmental cues (how conducive is the environment to producing tornadoes) is relied on more heavily to anticipate storm evolution and tip the scales towards warning, or not warning.  Of course, all storms within a similar environment will not necessarily produce the same result, and hence the weakness of this strategy.  With the PAR data, the higher temporal sampling gave us several more volume scans to assess the persistence of storm features, and gain a better understanding of the storm evolution.  This resulted in greater confidence in the warning decision making process, even if the outcome (would the storm successfully produce a tornado, and if so would it be observed and reported?) was not entirely certain.  I believe the challenge of achieving greater warning lead-time without a higher FAR will not entirely go away in the higher-temporal resolution world of PAR.  But our ability to diagnose storm processes will certainly improve, and through a learning experience, our warning decisions will improve with it.

I found my storm interrogation technique through the duration of the project evolving away from the “All Tilts” perspective of examining each elevation scan as it arrived in the database.    Instead, I found myself gravitating towards watching loops at critical levels within the storm in order to identify key storm scale processes important to my warning decision making (such as intensifying/deepening mesocyclones, RFDs, hail cores aloft ).   Our current AWIPS/WSR-88D techniques of storm analysis through all-tilts would be a daunting task in a PAR environment with 1 minute volume scans.  To analyze and interpret this vast amount of data rapidly, our techniques for radar interrogation will probably need to evolve towards viewing data in 4 dimensions.  I could envision new radar display capabilities for looping 3D isosurfaces of radar parameters such as reflectivity, velocity, shear, and eventually the dual pol parameters. Our computer systems will need to be fast enough to display and loop this larger quantity of data, and the GUI interfaces intuitive and efficient enough to modify display characteristics rapidly without negatively affecting system performance.    Warning operator fatigue with higher temporal resolution PAR radar data is certainly something to be concerned with, especially with widespread events across the entire forecast area.  But with the right tools and interrogation techniques, I believe this could be overcome, and the benefits this data could provide in understanding storm scale evolutions and enhancing NWS warning operations could be far reaching!

Participating in PARISE was such an enjoyable and exciting experience!  My thanks goes to everyone who put this project together and gave field forecasters the opportunity to participate.  I was truly inspired by your attentiveness to our feedback and your desire to understand our experiences with this new data set.  After having experienced how powerful PAR data was for warning decision making, I hope this system can be fielded as soon as possible.

Kathleen Torgerson (Lead Forecaster, NWS Pueblo CO – 2010 Week 3 Evaluator)