A Case Study of Social and Behavioral Science Integration into a Testbed Environment: Lessons Learned and Ways Forward for Convergence Science in the Weather Enterprise

In a significant milestone, NOAA’s Global Systems Laboratory (GSL) hosted the first in-person evaluation in Boulder, Colorado in June 2024, facilitated facilitated in close partnership with GSL's new Social and Behavioral Sciences Branch. This evaluation tested the effectiveness of new NOAA satellite-based fire detection tools and the NWS Integrated Warning Team for Fire Warnings. NWS forecasters and state forest fire management partners were invited to emulate an Integrated Warning Team’s role in disseminating hotspot notifications and issuing Fire Warnings for rapidly moving wildfires.

This interdisciplinary approach proved critical to the evaluation design, specifically through inclusion of social and behavioral scientists from the beginning to co-inform the evaluation goals and questions, methods used for evaluating these tools and concepts, and analysis of data following the evaluation. Furthermore, the unique challenges posed by fire weather in particular (e.g., communication, number and diversity of partners, and regional contexts) underscore the value of this collaboration. As such, this panel will (i) highlight lessons learned and observed benefits of social and behavioral science integration in evaluating fire weather-related products and services in a testbed environment and (ii) address the unique social, political, and meteorological considerations associated with fire weather and fire management. While wildland fire is the focus, the lessons learned and perspectives shared are broadly applicable to other weather-related hazards beyond the testbed context.

The panel will consist of researchers from NOAA’s Global Systems Laboratory who were involved in the planning and implementation of the Fire Weather Testbed evaluation. Panelists will be asked to provide an overview of their experiences, as well as best practices and ways forward for meaningfully integrating social and behavioral science to inform weather product and process development, and, ultimately, to produce more robust and actionable science.