论文标题
DCPVIZ：一种视觉分析方法，用于缩放气候预测
DCPViz: A Visual Analytics Approach for Downscaled Climate Projections
论文作者
论文摘要
本文介绍了一种新型的视觉分析方法DCPVIZ，以使气候科学家能够交互性地探索大量气候数据，而无需大量数据的前期运动。因此，为气候科学家提供了更有效的方法，以支持气候预测中潜在趋势和模式的识别及其随后的影响。我们设计了DCPVIZ管道，以获取和提取NEX-DCP30数据，并从其公共来源传输最少的数据传输。我们实施了DCPVIZ，以证明其可扩展性和科学价值，并根据不同模型和域专家反馈在三个用例下评估其效用。
This paper introduces a novel visual analytics approach, DCPViz, to enable climate scientists to explore massive climate data interactively without requiring the upfront movement of massive data. Thus, climate scientists are afforded more effective approaches to support the identification of potential trends and patterns in climate projections and their subsequent impacts. We designed the DCPViz pipeline to fetch and extract NEX-DCP30 data with minimal data transfer from their public sources. We implemented DCPViz to demonstrate its scalability and scientific value and to evaluate its utility under three use cases based on different models and through domain expert feedback.
