Visualizing Uncertainty

OneNOAA Science Seminar Series

Presenter: Max Schneider, PhD student in Statistics at the University of Washington, Seattle. 

Sponsor: NEDTalk NOAA Environmental Data Talks – Speaker Series (https://sites.google.com/a/noaa.gov/nesdis-datafest/nedtalks). POC: tiffany.small@noaa.gov

NOAA DataFest is a month-long effort throughout September to educate the public and inspire our colleagues at NOAA to learn more about NOAA data while celebrating its value, reliability, and accessibility. NOAA data covers a robust collection of scientific earth and environmental observations available to the scientific community and the public. 

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Abstract: Every day, thousands of forecast maps are produced by NOAA and related agencies. All forecasts come with error but how can we best visualize uncertainty on a forecast map? Max Schneider, PhD student in Statistics and NCEP intern, says the effectiveness of different approaches towards visualizing this uncertainty can be carefully studied in user experiments. In this talk, he presents a human subjects experiment where three common visualization techniques go head-to-head, to see which one enables effective map-reading and judgments using the forecasts.

Bio
Max Schneider is a PhD student in Statistics at the University of Washington, Seattle. This summer, he is interning at the Environmental Modeling Center within NOAA's National Centers for Environmental Prediction in College Park, MD. In his dissertation work, Max builds spatiotemporal models to forecast earthquakes in the Pacific Northwest. He focuses on quantifying various sources of error in these models and how to visualize them to diverse audiences. He collaborates with cognitive psychologists to directly study the effect visualizations have on how people use forecasts. In his work at NOAA, Max quantifies the uncertainty within coupled numerical models of hurricane impacts, for the US COASTAL Act. His motivation is to improve operational forecasting with a statistical approach to uncertainty quantification and visualization.

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{Max Schneider, PhD student in Statistics at the University of Washington, Seattle}