Seeing Through the Forecast Clutter: Communicating Climate Forecast Distributions with Weighted Multiple Forecast Visualizations

2026-08-01

AI Summary

Traditional confidence interval plots in multi-model climate prediction visualization often obscure individual model characteristics, leading users to misinterpret the underlying distribution—frequently assuming normality where none exists. To address this, this work proposes a Weighted Multi-Forecast Visualization (MFV) approach that leverages visual variables such as line width and opacity, combined with a downsampling strategy, to preserve accurate perception of the true predictive distribution while effectively conveying additional forecast attributes. Through a preregistered experimental design and large-scale user study, results demonstrate that MFV significantly improves users’ accuracy in identifying predictive distributions and reduces erroneous assumptions of normality. Moreover, the weighted MFV variant successfully overcomes the expressive limitations of conventional summary-based visualizations without compromising distributional fidelity.

Abstract

Forecasts often diverge because different models make varying assumptions to account for underlying uncertainty. Readers who consume forecasts may wish to survey the shape and spread of these multiple forecasts to get a full account of the different predictions. One approach to visualizing multiple forecasts is through Confidence Interval (CI) plots. However, while the summative CI plots can communicate uncertainty of an ensemble, they obscure attributes of individual forecasts that can lead to inaccurate perceptions of the distribution of these forecasts (e.g., implying a normal distribution when non-existent). To address this challenge, we investigate the use of multiple forecast visualization (MFV) in communicating nuanced forecast distributions through two preregistered experiments using climate forecast data. In Experiment 1 (480 participants), we compared how well MFV and CI plots can represent the distribution of multiple forecasts. We found that, compared to CI plots, MFV improved participants' ability to identify the underlying distribution of forecasts and reduced the likelihood of assuming normality. Building on Experiment 1, we examined in Experiment 2 (900 participants) whether a downsampled MFV showing 9 forecasts might be able to communicate additional forecast properties using linewidth and opacity without negatively impacting distribution perception. We found that visually weighting forecasts by linewidth or opacity preserves readers' perception of the underlying distribution. We discuss how these findings suggest the use of downsampled and weighted MFV to cut through forecast clutter by aligning perceived distribution with the underlying forecast distribution, while opening up design opportunities to use weighting to communicate additional forecast attributes.

Problem

• climate forecast
• forecast distribution
• visualization
• uncertainty communication
• forecast clutter

Innovation

• Multiple Forecast Visualization
• Weighted Visualization
• Climate Forecast Uncertainty
• Distribution Perception
• Confidence Interval Alternatives