The State of the Art in Visual Analysis Approaches for Ocean and Atmospheric Datasets

Abstract

The analysis of ocean and atmospheric datasets offers a unique set of challenges to scientists working in different application areas. These challenges include dealing with extremely large volumes of multidimensional data, supporting interactive visual analysis, ensembles exploration and visualization, exploring model sensitivities to inputs, mesoscale ocean features analysis, predictive analytics, heterogeneity and complexity of observational data, representing uncertainty, and many more. Researchers across disciplines collaborate to address such challenges, which led to significant research and development advances in ocean and atmospheric sciences, and also in several relevant areas such as visualization and visual analytics, big data analytics, machine learning and statistics. In this report, we perform an extensive survey of research advances in the visual analysis of ocean and atmospheric datasets. First, we survey the task requirements by conducting interviews with researchers, domain experts, and end users working with these datasets on a spectrum of analytics problems in the domain of ocean and atmospheric sciences. We then discuss existing models and frameworks related to data analysis, sense‐making, and knowledge discovery for visual analytics applications. We categorize the techniques, systems, and tools presented in the literature based on the taxonomies of task requirements, interaction methods, visualization techniques, machine learning and statistical methods, evaluation methods, data types, data dimensions and size, spatial scale and application areas. We then evaluate the task requirements identified based on our interviews with domain experts in the context of categorized research based on our taxonomies, and existing models and frameworks of visual analytics to determine the extent to which they fulfill these task requirements, and identify the gaps in current research. In the last part of this report, we summarize the trends, challenges, and opportunities for future research in this area. (see http://www.acm.org/about/class/class/2012)

Hornero: Thunderstorms Characterization using Visual Analytics

“…Some of the lessons learned during our project reinforce previous research [DPD ∗ 15,DPD ∗ 17,AHG ∗ 19] and discussions during the recent IEEE VIS 2019 “Application Spotlight” session on “Visualization in Meteorology & Climate Sciences” [IEE19]. Visual design for quick and high‐impact decision making requires user‐centered, easy, expressive, and effective visualizations.…”

“…A recent survey from Afzal et al [AHG ∗ 19] provides a comprehensive overview of the state of the art covering the most popular visual approaches for ocean and atmospheric data sets. The authors present several taxonomies with respect to application area, visualization technique, interaction, and data type.…”

“…Several efforts have been undertaken in both the meteorology community and the visualization community [RBS ∗ 18, AHG ∗ 19] to visualize meteorological data. Most of them concern analytical tasks, but not operational weather forecasting.…”

Towards an End-to-End Analysis and Prediction System for Weather, Climate, and Marine Applications in the Red Sea

“…RedSeaAtlas (Afzal et al 2019a), considering the tasks, computational and operational requirements of the variety of simulation models and observational datasets of the Red Sea region.…”

Visual analysis of model parameter sensitivities along warm conveyor belt trajectories using Met.3D (1.6.0-multivar0)

“…Visualization approaches for meteorological analysis have been discussed widely in the literature. Comprehensive overviews have been provided by Rautenhaus et al (2018); Afzal et al (2019); Yoshizumi et al (2020). Our workflow builds upon and extends approaches to perform interactive statistical data analysis (Love et al, 2005;Potter et al, 2010;Orf et al, 2016;Meyer et al, 2021), and touches on aspects of 3-D feature-based visualization (Rautenhaus et al, 2015a;Kern et al, 2018Kern et al, , 2019Bader et al, 2019;Kappe et al, 2022;Bösiger et al, 2022).…”