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    The state of the art in visual analysis approaches for ocean and atmospheric datasets

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    STARs2019_paper.pdf
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    PDF
    Description:
    Accepted manuscript
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    Type
    Article
    Authors
    Afzal, Shehzad
    Hittawe, M. M.
    Ghani, Sohaib
    Jamil, Tahira
    Knio, Omar cc
    Hadwiger, Markus cc
    Hoteit, Ibrahim cc
    KAUST Department
    Applied Mathematics and Computational Science Program
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Earth Fluid Modeling and Prediction Group
    Earth Science and Engineering Program
    Physical Science and Engineering (PSE) Division
    Visual Computing Center (VCC)
    Visualization
    KAUST Grant Number
    REP/1/3268-01-01
    Date
    2019-07-10
    Online Publication Date
    2019-07-10
    Print Publication Date
    2019-06
    Embargo End Date
    2020-01-01
    Permanent link to this record
    http://hdl.handle.net/10754/656777
    
    Metadata
    Show full item record
    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).
    Citation
    Afzal, S., Hittawe, M. M., Ghani, S., Jamil, T., Knio, O., Hadwiger, M., & Hoteit, I. (2019). The State of the Art in Visual Analysis Approaches for Ocean and Atmospheric Datasets. Computer Graphics Forum, 38(3), 881–907. doi:10.1111/cgf.13731
    Sponsors
    This study was supported by the Office of Sponsored Research (OSR) at King Abdullah University of Science and Technology (KAUST) under the “Virtual Red Sea Initiative” (award #REP/1/3268-01-01). We want to acknowledge Dr. Hari Prasad Dasari for his help and contribution in organizing the interviews with domain experts. We also like to thank KAUST Visualization Core Lab for their help and support.
    Publisher
    Blackwell Publishing Ltd
    Journal
    Computer Graphics Forum
    DOI
    10.1111/cgf.13731
    Additional Links
    https://onlinelibrary.wiley.com/doi/abs/10.1111/cgf.13731
    ae974a485f413a2113503eed53cd6c53
    10.1111/cgf.13731
    Scopus Count
    Collections
    Articles; Applied Mathematics and Computational Science Program; Physical Science and Engineering (PSE) Division; Computer Science Program; Earth Science and Engineering Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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