A Survey on Exploratory Spatiotemporal Visual Analytics Approaches for Climate Science

TL;DR

The paper addresses enabling effective exploratory spatiotemporal visual analytics for climate data, where observations and model outputs yield high-dimensional, distributed datasets. It presents a comprehensive taxonomy and survey of data types, tasks, visualization techniques, interaction methods, systems, and evaluation approaches, identifying key trends and gaps. The work highlights shifts toward integrated and web-based VA systems, the importance of distributed data handling, and the need for progressive visualization and shepherd interactions. These insights aim to guide climate scientists and visualization researchers in building scalable, usable VA tools that support hypothesis generation and climate decision-making.

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 spatiotemporal analyses to enable efficient exploration of massive data. These advances present further opportunities for the visualization community to innovate over the full landscape of challenges and requirements raised by scientists. In this report, we provide a comprehensive review of the challenges, requirements, and current approaches for exploratory spatiotemporal visual analytics solutions for climate data. We categorize the visual analytic techniques, systems, and tools presented in the relevant literature based on task requirements, data sources, statistical techniques, interaction methods, visualization techniques, performance evaluation methods, and application domains. Moreover, our analytic review identifies trends, limitations, and key challenges in visual analysis. This report will advance future research activities in climate visualizations and enables the end-users of climate data to identify effective climate change mitigation strategies.

Figures (9)

• Figure 1: A conceptual illustration of different data types classified in Ben1996. He et al. he2019variable illustrated the concept of the data type classification: (a) Multidimensional: 0D, 1D, 2D, and 3D; (b) Multivariate: scalar, 2-tuple, and n-tuple. (c) Network and (d) Tree data types are illustrated from the view diagram presented by Tominski et al. Tominski2021.
• Figure 2: CrossVis demonstrates interactive axes selection of categorical cells and numerical range in parallel coordinates to explore large-scale multivariate data Steed2020.
• Figure 3: CORNEA castruccio2019visualizing demonstrates an immersive virtual environment to explore simulated climate models using (a) globe and (b) surface display.
• Figure 4: Ensemble of time-varying iso-contours in a weather forecast to produce interactive space-time volume rendering through time-hierarchical clustering Ferstl2017.
• Figure 5: Visual hierarchy of multivariate spatiotemporal data demonstrated by Hadlak et al. Hadlak2010 using glyph-based space-time illustration.