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

Abstract

Visually communicating temperature and precipitation climate outlook graphics is challenging because it requires the viewer to be familiar with probabilities as well as to have the visual literacy to interpret geospatial forecast uncertainty. In addition, the visualization scientific literature has open questions on which visual design choices are the most effective at expressing the multidimensionality of uncertain forecasts, leaving designers with a lack of concrete guidance. Using a two-phase experimental setup, this study shows how recently developed visualization diagnostic guidelines can be used to iteratively diagnose, redesign, and test the understandability the U.S. National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center (CPC) climate outlooks. In the first phase, visualization diagnostic guidelines were used in conjunction with interviews and focus groups to identify understandability challenges of existing visual conventions in temperature and precipitation outlooks. Next, in a randomized control versus experimental treatment setup, several graphic modifications were produced and tested via an online survey of end users and the general public. Results show that, overall, end users exhibit a better understanding of outlooks, but some types of probabilistic color mapping are misunderstood by both end users and the general public, which was predicted by the diagnostic guidelines. Modifications lead to significant gains in end-user and general public understanding of climate outlooks, providing additional evidence for the utility of using control versus treatment testing informed by visualization diagnostics.

Visualizing Uncertainty for Non-Expert End Users: The Challenge of the Deterministic Construal Error

“…A full third of forecasters interpreted the increased color intensity (red in this case) as greater accumulation rather than greater likelihood (Wilson et al, 2019). Another example is a study of colorcoded climate outlook graphics for temperature (Gerst et al, 2020), intended for use by the U.S. National Oceanic and Atmospheric Administration (NOAA). In these visualizations, hue indicated whether the value would be above normal (e.g., orange), near normal (gray) or below normal (e.g., blue).…”

“…We discovered the DCE by accident when asking about the deterministic quantity depicted in the visualization that also contained uncertainty. A more direct approach would be to begin by asking participants an open-ended question about what the visualization means, as was done by Gerst et al (2020) above. This is important to do prior to providing participants additional information that may reveal the fact that uncertainty is the subject.…”

Improving the usability of climate indicator visualizations through diagnostic design principles

“…The problem causes listed in Table 1 can be used to review existing graphics for potential issues that can undermine understandability, and suggest bene cial modi cations. For example, Gerst et al (2020) used the guidelines to identify that a widely distributed weather forecasting map used the same color for two variables. This problem, called visual variable ambiguity, can lead to misinterpretation.…”

“…Based on this result, we selected a small subset of indicator visualizations to use in testing the main question of this study: whether making design modi cations can improve a layperson's understanding of challenging scienti c graphics. Speci cally, the current study utilized a performance assessment of static climate indicator visualizations to test the effectiveness of design changes (similar to Gerst et al, 2020) in increasing the understandability of three indicators: Annual Greenhouse Gas Index (AGGI), Heating and Cooling Degree Days (HCDD), and Spatial Temperature Change (Temp). Our approach was to take existing climate indicator visualizations, diagnose them for design problems, and use the diagnoses to create design modi cations for comparative assessment.…”

National indicators of climate changes, impacts, and vulnerability

“…Such modifications can then be control vs treatment tested to assess whether the modifications improve understanding and subjective reactions among the public. This study provides two key advancements to previous research by Gerst et al (2020): (i) it explicitly examines the role of both simplifications and annotations in improving understandability, and (ii) it explores the impact of respondents' climate attitudes and beliefs, as well as political identities, on the understandability of climate visualizations and efforts to improve both understanding and subjective reactions. Results show that (i) simplifying design modifications helps improve the understandability of the graphics, but understanding is still dependent on general ability (i.e., education and numeracy) and (ii) ideology does not impede improved understanding associated with better design (i.e., motivated reasoning) and such modifications do not measurably counter ideologically based likability and mistrust in climate indicators.…”