1. Eye-tracking during the Forecaster Warning Decision Process: A Pilot Experiment Katie Bowden OU CIMMS/School of Meteorology, Ph.D. Student Pam Heinselman NOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma Ziho Kang OU School of Industrial and Systems Engineering Monday 19 October 2015

2. The Phased Array Radar Innovative Sensing Experiment (PARISE) How will rapidly-updating phased array radar data impact forecasters during their warning decision processes? Current technology: WSR-88D (Weather Surveillance Radar – 1988 Doppler) Radar update time: 4-6 min Potential Future technology: PAR (Phased Array Radar) Radar update time: 1 min

3. The Phased Array Radar Innovative Sensing Experiment (PARISE) How will rapidly-updating phased array radar data impact forecasters during their warning decision processes? Performance Measures -Accuracy (e.g., POD, FAR) -Lead time Cognitive Process - How temporal resolution affects conceptual models, ability to discern radar signatures, mental effort in interpreting radar data Our focus today

4. 1) View weather briefing and work the case using AWIPS-2 2) Review recorded simulation, produce timeline of decision process (Hoffman 2005) and answer questions on:  Warning decisions  Noticeable impacts of temporal resolution  Cognitive workload Simulated Warning Operations and Retrospective Recall  From the most recent experiment, we collected ~2000 pages of qualitative data from forecasters. But, is there a way to quantify forecasters’ cognitive processes?

5. Eye-Tracking Experiment Incorporate eye-tracking technology to further develop our understanding of forecasters’ cognitive processes “There is no appreciable lag between what is fixated and what is processed.” (Just and Carpenter 1980)

6. Pilot Study Goal: Run a short study with one forecaster to see what their eye-tracking data looks like. Does the data make sense?

7. Eye Tracker Warning Decision Support System Integrated Information Display Reflectivity Velocity Control Panel

8. Example of Eye Tracking Data Output

9. Fixation Heatmaps and Distributions Count Duration

10. As the weather event evolved, how did the forecaster’s cognitive processes change, and was a response to these changes observed in the eye-tracking data?

11. Comparing Retrospective Recall and Trends

13. 2015 PARISE: Eye-tracking Experiment Experimental Group n=15 Control Group n=15 Work One Case (1 hour duration) 1-min updates5-min updates All participants completed a retrospective recall using a playback video Successful data collections: 12 experimental and 12 control cases

14. Summary Eye-tracking research is a brand new avenue for PARISE, bringing together the fields of meteorology and human factors. Our pilot study demonstrated capability to obtain eye-tracking data and that such data was useful for understanding forecaster cognitive activity. First presentation of results from our recent experiment will be shared at the AMS Annual Meeting in January 2016. Thank you for your time. Any questions? katie.bowden@noaa.gov