3.5 A Next-Generation Warning Concept of Threats-In-Motion (TIM): An Operational Warning Decision Making Collaborative Exercise

Thursday, 27 June 2013: 2:30 PM
Two Rivers (Sheraton Music City Hotel)
Peter Wolf, NOAA/NWS, Jacksonville, FL; and G. J. Stumpf, E. E. Carpenter, C. Entremont, A. P. Enyedi, G. R. Garrett, M. L. Grantham, K. B. Laws, and S. E. Nelson

Threats-In-Motion (TIM) is an experimental severe weather warning paradigm in which storm-based warning polygons continuously follow along with the moving threats. This style of issuing warnings is hypothesized to, 1) increase average warning lead times for site-specific locations by 50-100%, 2) provide continuously-updated threat information (the polygon always illustrates up-to-date area of threat), 3) decrease forecaster workload by at least 50% compared to present-day warning operations, and 4) offers a more robust solution to the issues of very large polygons, gaps between polygons leading to partially warned events, polygon overlap for the same storm leading to over-alerting (e.g. NWR), polygons for storms that cross CWA boundaries, merging storms, splitting storms, and non-linear storm motions.

To test these hypotheses, four National Weather Service (NWS) Weather Forecast Offices (WFOs) participated in a collaborative exercise to test the concepts of TIM in a simulated real-time warning environment. The data used will be from the 27 April 2011 tornado outbreak event as the storms affected the Jackson, MS, Birmingham, AL, Huntsville, AL, and Atlanta, GA weather forecast offices (The Jacksonville, FL, WFO will act as the “stand-in” for the Huntsville, AL, WFO). While we will assume that any future TIM system will be based on new technology that has yet to be fully-developed, for this study, warnings will be issued by employing innovative techniques to exploit the current capabilities of the Advanced Weather Information Processing System (AWIPS). The main objectives of the study are to evaluate the impacts of the TIM methodology on the flow of information to customers, the accuracy of that information, and forecaster workload.

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