15C.4 Creation of Animated, Educational Videos with Probabilistic Forecast Information Driven by Survey Data, Narrative, and Real-Life Decision Scenarios

Thursday, 16 January 2025: 2:15 PM
341 (New Orleans Ernest N. Morial Convention Center)
Meghan Collins, DRI, Reno, NV; and M. Mahmoud, A. Heggli, K. Lambrecht, B. J. Hatchett, L. Olman, and Z. Tolby

As weather forecasts incorporate more probabilistic forecast information (PFI), public audiences can benefit from demonstrating how this new type of information can be applied to daily decisions involving weather and risk. This presentation covers a research-to-operations (R2O) project focused on end-user engagement in which we created animated, educational videos that incorporate mixed-methods research findings to support both forecasters and public audiences in the shift to probabilistic forecast information.

Specifically, we teamed up with a motion graphic artist to produce a series of five animated videos that demonstrate decision-making across a range of winter and summer weather scenarios that incorporate probabilistic forecast information. Video content integrates four elements: effective practices from science communication (narrative and relatability), our survey findings, key concepts from probabilistic forecasting, and real-life decision scenarios that demonstrate how PFI can be used. Surveys were distributed to public audiences and NWS partners in five County Warning Areas (n=1,428) in the Western Region. Surveys identified community commonplaces (shared beliefs and values amongst a community) and risk thresholds that were then incorporated into the video storylines to shape characters’ decision-making in relatable settings. This presentation will share the process of producing the videos, including how we developed the scripts and how we used our survey results to shape the dialog. Throughout, we will highlight how the elements of narrative and storytelling create an opportunity to educate the public on complex scientific topics to support decision-making with probabilistic forecast information.
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