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An Image-Based Framework for Ocean Feature Detection and Analysis

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Abstract

Today’s supercomputing capabilities allow ocean scientists to generate simulation data at increasingly higher spatial and temporal resolutions. However, I/O bandwidth and data storage costs limit the amount of data saved to disk. In situ methods are one solution to generate reduced data extracts, with the potential to reduce disk storage requirement even for high spatial and temporal resolutions, a major advantage to storing raw output. Image proxies have become an efficient and accepted in situ reduced data extract. These extracts require innovative automated techniques to identify and analyze features. We present a framework of computer vision and image processing techniques to detect and analyze important features from in situ image proxies of large ocean simulations. We constrain the analysis framework in support of techniques that emulate ocean-specific tasks as accurately as possible. The framework maximizes feature analysis capabilities while minimizing computational requirements. We demonstrate its use for image proxies extracted from the ocean component of Model for Prediction Across Scales (MPAS) simulations to analyze ocean-specific features such as eddies and western boundary currents. The results obtained for specific data sets are compared to those of traditional methods, documenting the efficacy and advantages of our framework.

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Funding

The authors thank David Rogers for his support and input. D. Banesh and T.L. Turton were supported by the Cinema project as part of the Exascale Computing Project. M. Petersen was supported as part of the Energy Exascale Earth System Model (E3SM) project, funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research. This research used resources provided by the Los Alamos National Laboratory Institutional Computing Program, which is supported by the U.S. Department of Energy National Nuclear Security Administration under Contract No. 89233218CNA000001. This research was supported by the Exascale Computing Project (17-SC-20-SC), a collaborative effort of the U.S. Department of Energy Office of Science and the National Nuclear Security Administration.

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Authors and Affiliations

  1. University of California, Davis, Davis, CA, USA

    Divya Banesh & Bernd Hamann

  2. Los Alamos National Laboratory, Los Alamos, NM, USA

    Divya Banesh, Mark R. Petersen, James Ahrens, Terece L. Turton & Francesca Samsel

  3. NOAA, Boulder, CO, USA

    Joseph Schoonover

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  1. Divya Banesh
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Correspondence to Divya Banesh.

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Banesh, D., Petersen, M.R., Ahrens, J. et al. An Image-Based Framework for Ocean Feature Detection and Analysis. J geovis spat anal 5, 17 (2021). https://doi.org/10.1007/s41651-021-00085-8

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