A Survey on Exploratory Spatiotemporal Visual Analytics Approaches for Climate Science
Abdullah-Al-Raihan Nayeem, Dongyun Han, Huikyo Lee, Donghoon Kim,, Daniel Feldman, William J. Tolone, Daniel Crichton, and Isaac Cho

TL;DR
This survey reviews current exploratory spatiotemporal visual analytics approaches in climate science, highlighting challenges, techniques, and future directions to enhance climate data analysis and decision-making.
Contribution
It provides a comprehensive categorization and analysis of existing visual analytics methods, identifying trends, limitations, and key challenges in climate data visualization.
Findings
Categorizes visual analytic techniques based on multiple criteria.
Identifies current limitations and challenges in climate visual analytics.
Highlights opportunities for innovation in visualization tools for climate science.
Abstract
Climate science produces a wealth of complex, high-dimensional, multivariate data from observations and numerical models. These data are critical for understanding climate changes and their socioeconomic impacts. Climate scientists are continuously evaluating output from numerical models against observations. This model evaluation process provides useful guidance to improve the numerical models and subsequent climate projections. Exploratory visual analytics systems possess the potential to significantly reduce the burden on scientists for traditional spatiotemporal analyses. In addition, technology and infrastructure advancements are further facilitating broader access to climate data. Climate scientists today can access climate data in distributed analytic environments and render exploratory visualizations for analyses. Efforts are ongoing to optimize the computational efficiency of…
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Taxonomy
TopicsData Visualization and Analytics
