A primary goal of PARISE is to understand the impacts of higher-temporal resolution radar data on forecasters' warning decision processes and performance. While findings from 2010, 2012, and 2013 PARISE are positive, the sample sizes are limited. In the 2015 PARISE, sample size is increased in number of participants and cases worked to improve the generalizability of the results. Furthermore, the experiment design is expanded to advance understanding of impacts of temporal resolution on forecaster's cognitive processes and workload. To accomplish this goal, the 2015 PARISE consists of three parts, including a 1) traditional experiment, 2) eye-tracking experiment, and 3) focus group; each part is described in this paper.
The 2015 PARISE began 3 August 2015 and ran for six weeks. During the traditional experiment, 30 NWS forecasters from across the Great Plains worked nine archived cases in simulated real time using 1-, 2-, or 5-min PAR updates. After working each case, forecasters produced detailed accounts of their warning decision process and cognitive workload. Preliminary findings, such as forecasters' performance, will be presented. New to PARISE is the use of eye-tracking technology to deepen understanding of forecasters' cognitive processes. Eye-tracking technology has been used for similar purposes in domains such as healthcare, air traffic control, and human-computer interactions. Eye gaze data were collected from the 30 forecasters as they worked an archived PAR case in simulated real time. Half of the forecasters used 1-min PAR data, whereas the other half used 5-min PAR data. Based on results from a similar pilot eye-tracking study conducted in spring 2015, we expect that the analysis of these data will provide new insights regarding the impacts of update time on forecasters' cognitive processes. Preliminary results will be reported in a companion paper by Bowden. On the last day of the experiment, forecasters participated in a focus group aimed at generating insightful feedback and ideas important to the development of a future PAR network such as: 1) impacts of update time on data interrogation techniques and ability to apply their conceptual models, 2) how rapid-scan data could be integrated into operations, and 3) training that would be useful in transitioning rapid-update data to operations.