透过预测杂波看清一切：采用加权多预测可视化技术传达气候预测分布
Seeing Through the Forecast Clutter: Communicating Climate Forecast Distributions with Weighted Multiple Forecast Visualizations
浏览论文内容
中文总结 AI 辅助
该研究针对气候预测可视化的偏差问题，通过两项实验验证，采用线宽或透明度加权的下采样多预测可视化（MFV），可提升读者对预测分布的认知，减少正态分布误判。
中文摘要 AI 辅助
预测结果常存在差异，原因在于不同模型为应对潜在不确定性会做出不同假设。查看预测结果的读者可能希望考察这些多重预测的形态与范围，以全面了解各类预测结果。可视化多重预测的一种方法是置信区间（CI）图。不过，汇总型CI图虽能传达集合的不确定性，却会掩盖单个预测的属性，而这些属性可能导致对预测分布的认知出现偏差（例如，当实际分布并非正态时，CI图可能暗示其为正态分布）。为应对这一挑战，我们通过两项预先注册的实验，研究采用多预测可视化（MFV）技术，结合气候预测数据来传达细致的预测分布。实验1共有480名参与者，我们对比了MFV与CI图在呈现多重预测分布方面的效果。结果发现，与CI图相比，MFV提升了参与者识别预测潜在分布的能力，并降低了他们假设分布为正态的可能性。基于实验1的结果，我们在实验2（共900名参与者）中检验，展示9个预测结果的下采样MFV能否通过线宽和透明度传达额外的预测属性，且不会对分布感知产生负面影响。研究发现，通过线宽或透明度对预测进行视觉加权，能维持读者对潜在分布的感知。我们讨论了这些发现如何表明，采用下采样且加权的MFV可消除预测杂波，使感知分布与潜在预测分布保持一致，同时为利用加权传达额外预测属性开辟了设计空间。
英文摘要
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.
