Impacts of Phased-Array Radar Data on Forecaster Performance during Severe Hail and Wind Events

Abstract

The ongoing Phased Array Radar Innovative Sensing Experiment (PARISE) investigates the impacts of higher-temporal-resolution radar data on the warning decision process of NWS forecasters. Twelve NWS forecasters participated in the 2013 PARISE and were assigned to either a control (5-min updates) or an experimental (1-min updates) group. Participants worked two case studies in simulated real time. The first case presented a marginally severe hail event, and the second case presented a severe hail and wind event. While working each event, participants made decisions regarding the detection, identification, and reidentification of severe weather. These three levels compose what has now been termed the compound warning decision process. Decisions were verified with respect to the three levels of the compound warning decision process and the experimental group obtained a lower mean false alarm ratio than the control group throughout both cases. The experimental group also obtained a higher mean probability of detection than the control group throughout the first case and at the detection level in the second case. Statistical significance ( p value 5 0.0252) was established for the difference in median lead times obtained by the experimental (21.5 min) and control (17.3 min) groups. A confidence-based assessment was used to categorize decisions into four types: doubtful, uninformed, misinformed, and mastery. Although mastery (i.e., confident and correct) decisions formed the largest category in both groups, the experimental group had a larger proportion of mastery decisions, possibly because of their enhanced ability to observe and track individual storm characteristics through the use of 1-min updates.

A Primer on Phased Array Radar Technology for the Atmospheric Sciences

“…In addition, understanding the formation of severe hazards, such as hail and microbursts (Heinselman et al 2008a;Newman and Heinselman 2012;Tanamachi and Heinselman 2016;Kuster et al 2019) and capturing the evolution of deep convective updrafts require rapidly collected volume scans through a deep vertical column (Emersic et al 2011;Yoshida et al 2017;Oue et al 2018;Dahl et al 2019;Kuster et al 2019). In a PAR operational testbed, forecasters have noted substantial benefits of using higher temporal resolution data, including improved statistical performance of severe weather warnings issued (Heinselman et al 2012(Heinselman et al , 2015Bowden et al 2015a;Bowden and Heinselman 2016;Wilson et al 2017). Acceptable temporal resolution must be coupled with adequate spatial resolution to mitigate spatial smearing of rapidly evolving features.…”

Exploring the Human Side of Meteorology: A Brief Report on the Psychology of Meteorologists

“…This includes perceptions of forecast uncertainty (Handmer and Proudley 2007;Joslyn and Savelli 2010;Morss, Demuth, and Lazo 2008), perceptions of weather risk and severe weather lead time (Hoekstra et al 2011), the psychological importance of weather (Stewart 2009;Stewart et al 2012), beliefs about and perceptions of climate change (Egan and Mullin 2012;Hamilton and Stampone 2013;Taylor, de Bruin, and Dessai 2014), public health and weather messaging (Williams and Grundstein 2017;, the relationship between perception of color and the communication of weather information (Bryant et al 2014), the role weather itself plays in influencing mental health (Bulbena et al 2005;Coleman et al 2014;Greening and Dollinger 1992;Lonigan et al 1994;Matthey 1988;Westefield et al 2006;Wang 2000), and even meteorologists' use of weather data and cognitive performance in forecasting (Bowden et al 2015;Bowden and Heinselman 2016;Wilson et al 2016;Heinselman et al 2015). Considering past research into the relationship between autism and science, and the multitude of work-related psychological stressors meteorologists often face, it is surprising that more work has not examined the psychology of meteorologists themselves.…”

Towards the Next Generation Operational Meteorological Radar

“…Figure 2 depicts a concept for how such techniques could enhance severe-weather warnings and forecasts 2 . PAR-enabled volume-coverage patterns (VCPs) provide surface-to-storm-top observations approximately once per minute, significantly improving NWS forecasters' cognitive models of severe-storm structure and thereby warning performance (see Heinselman et al (2012Heinselman et al ( , 2015, Bowden et al (2015), and Wilson et al (2017)). These rapid-update VCPs also improve model analysis of storm spin-up (Yussouf and Stensrud 2010), which can improve performance of the Warn-on-Forecast System (WoFS) (Stensrud et al 2009).…”