Supercell Storm Evolution Observed by Forecasters Using PAR Data

How do forecasters decide to issue or not to issue a tornado warning? What are their radar-based conceptual models? Does rapid-scan radar data enhance the application of those conceptual models and aid forecaster warning decisions? These are the questions we sought to answer via the Phased Array Radar Innovative Sensing Experiment (PARISE). Twelve National Weather Service forecasters participated in PARISE, which ran for six weeks during June – August 2012 at the NOAA Hazardous Weather Testbed in Norman, Oklahoma, USA. Two forecasters participated each week. The experiment's overarching goal was to test whether rapid, adaptive sampling with the phased array radar increased NWS forecasters' ability to effectively cope with tough tornado warning cases.

During the experiment each forecaster worked four cases, two tornadic and two non-tornadic, in displaced real time. For each case, the forecasters' goal was to decide whether a tornado warning was warranted. The recordMyDesktop software recorded forecaster interaction with the radar data. After each case, the forecasters reviewed the video replay of their desktop activity and retrospected aloud about their reasoning and observations. The researcher prompted the forecaster to describe his or her actions and thought processes, and typed out a timeline of these. After completing the timeline, forecasters were asked to identify key judgments during the case and the information used to make them.

Early data analysis has focused on quantitative measures of forecaster performance, such as tornado lead time (TLT), polygon probability of detection (PPOD), and probability of false alarm (POFA). The results indicated strong performance: the mean TLT (N=48) was 21 min, PPODs ranged from 0.75 to 1.0, and the POFD values ranged from 0 to 0.5. Also of interest, though, is the radar-based situational awareness that forecasters attained from the rapid-scan PAR data. In particular, the temporal-scale of storm evolution discerned and the relative importance of this information to warning decisions. The timelines collected on the two tornadic cases are being analyzed to understand the radar-based situational awareness that preceded their key judgments and warning decisions.