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Webinar: Using Visualization Science to Improve Expert and Public Understanding of Probabilistic Temperature and Precipitation Outlooks

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Tuesday, 11 January 2022, 1:00

Tuesday, January 11, 2022. 1:00 PM. Webinar: Using Visualization Science to Improve Expert and Public Understanding of Probabilistic Temperature and Precipitation Outlooks. Melissa Kenney, University of Minnesota; Michal Gerst, UMD College Park; Jon Gottschalk, NOAA. Sponsored by NOAA Climate Prediction Center. More information here. Register here.


Embedding science in decision support tools and representing it in public communications has long been a challenge. This is partly because most scientific information is infused with multiple trends or patterns. Moreover, how to simplify visualizations is often unclear because lack of stakeholder engagement makes it difficult to know which trend or pattern should be highlighted as the key message. As a result, multiple trends are often shown, leading to complicated scientific graphics being reproduced for public use. The existence of uncertainty further complicates use of scientific information because it adds at least one extra variable to be considered and displayed, and decision-makers or the public are less accustomed to reasoning with scientific uncertainty.

Over the past few years, we have been investigating these problems for global change, climate, and water information provided by, respectively, the (1) US Global Research Change Program (USGCRP) indicator suite and 3rd National Climate Assessment graphics, (2) temperature and precipitation outlooks produced by the NOAA Climate Prediction Center (CPC), and (3) water watch, water quality watch, groundwater watch produced by US Geological Survey (USGS). Tackling these problems requires the integration of visualization science, decision science, and design theory. Using focus groups and control/treatment testing, the combination of these scientific fields leads to the ability to better understand user needs, test whether current designs are meeting them, and compare current products against visualizations modified by best practice design principles.

Our results show that this three-step process can identify problems with current visualizations that are fixable within the typical constraints of legacy scientific products, such as a large engaged user base and being embedded within established institutional workflows. Furthermore, we outline how the process is scalable and customizable to the needs of organizations and their users and to the specifics of visualization products. This research is funded by NOAA Climate Prediction Center, NOAA Climate Program Office, and USGS Water Mission Area.


Dr. Melissa Kenney: Dr. Kenney is Director of Research and Knowledge Initiatives at the University of Minnesota's Institute on the Environment where she leads the institute's impact goal initiatives on carbon neutrality, sustainable land use, and safe drinking water. Dr. Kenney is also an environmental decision scientist with expertise in multidisciplinary, team-based science approaches to solving sustainability challenges. Her research team conducts multidisciplinary social science research to increase the use of evidence in climate adaptation and mitigation, ecosystem resilience, interdependent infrastructure decisions, and water quality management. The goal of her program is to understand and improve the processes and tools that aid these decisions, both in the public and private sectors. Notably she has worked to improve the understandability of high-profile indicators and data products produced by the Federal government for the public. She earned a Ph.D. from Duke University, focusing on water quality modeling and decision analysis.

Dr. Michael D. Gerst is an Associate Research Professor at the Earth System Science Interdisciplinary Center at the University of Maryland (ESSIC). His scientific work is motivated by helping stakeholders identify problems and solutions at the intersection of the environment, technology, and society. As a result of the complexity of these issues, his approach is rooted in systems, data, and decision science, which together allow for a holistic understanding of current knowledge so that stakeholders may assess trade-offs and risks of potential solutions. Dr. Gerst's research portfolio has ranged from participatory development of global change indicators, cost-benefit analysis under uncertainty, life-cycle analysis, and scenario planning to designing and testing the efficacy of visualizations. His application areas have spanned climate change mitigation, food-energy-water nexus, critical materials, healthcare systems, and corporate sustainability. He received his Ph.D. from Yale University, focusing on industrial ecology and techno-economic systems analysis.

Mr. Jon Gottschalck currently works at the Climate Prediction Center (CPC, since 2004) within NOAA's National Weather Service. He is the Chief of the Operational Prediction Branch within CPC and is responsible for outlining the overall direction of operational forecast-related activities. Prior to this, Mr. Gottschalck served as CPC Head of Forecast Operations where he was responsible for overseeing day-to-day routine production and dissemination of CPC's operational forecast products. Mr. Gottschalck also served as the CPC Madden-Julian Oscillation (MJO) operational team lead that was responsible for managing MJO monitoring, assessment, and outlook activities and their associated impacts both globally and for the U.S. while coordinating the weekly production of the CPC Global Tropics Hazards Outlook. Mr. Gottschalck earned both a B.S. and M.S. degree in meteorology from the Pennsylvania State University in 1994 and 1996 respectively. Prior to CPC, Mr. Gottschalck worked at the Rosenstiel School of Marine and Atmospheric Science at the University of Miami from 1997-2001 and at NASA's Goddard Space Flight Center from 2001-2004.

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