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Impacts of Phased Array Radar Data on Forecaster Performance during Severe Hail and Wind Events (Weather and Forecasting)

Early online release 1/13/15

Katie A. Bowden,1 Pamela L. Heinselman,2 Darrel M. Kingfield,1,2 and Rick P. Thomas3

Summary: Twelve National Weather Service (NWS) forecasters participated in the Phased Array Innovative Sensing Experiment (PARISE) 2013 and were assigned to either a control (5-min radar data updates) or experimental (1-min radar data updates) group. Each group worked a marginally severe hail event and a severe hail and wind event in simulated real-time. While working each event, participants made warning decisions regarding the detection, identification, and re-indentification of severe weather, now known as “the compound warning decision process.”

Important conclusions: The experimental group's performance exceeded that of the control group's, as demonstrated through their significantly longer median warning lead time, as well as superior probability of detection and false alarm ratio scores. The experimental group also had a larger proportion of mastery decisions (i.e., confident and correct) than the control group, possibly because of their enhanced ability to observe and track individual storm characteristics through the use of 1-min updates.

Significance: This work furthers efforts that have already been made to understand the impact of higher-temporal resolution radar data, as provided by PAR, on the warning decision process of NWS forecasters. The research questions, methodology, and analysis presented in this paper build upon the findings presented from earlier PARISE work, while also sharing findings that are of a new nature.