Insights from a Decade of Decision Support Research-to-Operationalized Recommendations to Improve Forecast Understandability and Service Equity

Weather, climate, and ecological forecast products that are intended for broad audiences, including the public, can sometimes suffer from challenges of understandability, interpretation, and usefulness. Tackling these problems requires integrating social science methods such as visualization science, decision science, and design theory. The combination of these fields allows us to better understand user needs, test whether current designs are meeting them, and develop improved visualizations using best practice design principles.

In this presentation, I will share a synthesis of a decade of research focused on processes of engagement with forecast producers and users and novel methods we have developed to identify and test original versus modified data products and decision support tools. This work will feature both results and best practices from the development of the original National Climate Assessment (NCA) indicators (part of a sustained assessment in the 3rd NCA and continued and expanded in the 4th and 5th NCA); redevelopment of the NOAA CPC temperature and precipitation forecasts; recommendations for the USGS Water Watch, Groundwater Watch, and Water Quality Watch products; NOAA OWP National Water Prediction Service (NWPS); and long-term predictions of Mississippi River high/low flows as part of one of the NOAA Climate Equity pilot projects. I will also highlight insights from current research on the NOAA CPC and NIDIS drought outlooks, NOAA OWP flood inundation maps (FIMs), NOAA Atlas, and the USDA-funded Dashboard for Agricultural Water and Nutrient management (DAWN) decision support system. I will connect these examples to implications for advancing critical decision support systems in the next decades.

Through sharing best practices from a diversity of decision support and data product applications, I hope to inspire conversations about how we can better shape data products to be more understandable, usable, accessible, and equitable for all who need such forecasts to improve individual or organizational decisions.