Posted May 14th, 2014 in Uncategorized
The National Weather Service’s Weather-Ready Nation initiative was begun to help communities throughout the country better prepare for and respond to severe weather events. Much of that preparedness has to do with increasing the speed, accuracy, and effectiveness of weather monitoring and warning mechanisms on the local level. And finding the strongest ways to communicate weather messages to residents is key.
That is why, as part of the Weather-Ready Nation project, the Great Lakes Social Science Network conducted extensive research into the most effective impact-based warnings. Their report, “Evaluation of the National Weather Service Impact-based Warning Tool,” utilized interviews, focus groups, and surveys to determine the most and least effective ways for broadcast meteorologists and emergency managers to communicate these warnings to the public.
National Weather Service piloted an impact-based warning system in 2012 in five select offices, and expanded it to the central region’s 38 offices in 2013. The report offers a sort of mid-term evaluation of the system’s effectiveness and stakeholders’ perceptions of it, while also providing recommendations for further training and implementation improvements.
This research was a team effort between representatives from five Great Lakes Sea Grant programs. Caitie McCoy and Leslie Dorworth from Illinois-Indiana Sea Grant were involved, as well as Dr. Jane Harrison (Wisconsin Sea Grant), Dr. Kathy Bunting-Howarth (New York Sea Grant), Hilarie Sorensen (Minnesota Sea Grant), Katie Williams (University of Wisconsin-Milwaukee), and Dr. Chris Ellis (NOAA Coastal Services Center). The report was presented earlier this year to the Social Coast Forum in Charleston, SC, sparking a number of other groups and agencies to inquire about the report and possible opportunities to expand on it with further research.
For further information about the Great Lakes Social Science Network, as well as training and future research projects, visit the link above.