DCPViz: A Visual Analytics Approach for Downscaled Climate Projections

TL;DR

DCPViz is a visual analytics tool that allows climate scientists to interactively explore large-scale climate projection data efficiently, supporting trend identification and impact analysis without extensive data transfer.

Contribution

The paper presents DCPViz, a novel visual analytics pipeline that enables scalable, interactive exploration of massive climate datasets with minimal data movement.

Findings

Effective data fetching and extraction from public sources.

Demonstrated scalability and scientific utility.

Positive domain expert feedback on usability.

Abstract

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.