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Open AccessArticle

Analysis and Visualization of Coastal Ocean Model Data in the Cloud

Woods Hole Coastal and Marine Science Center, U.S. Geological Survey, Woods Hole, MA 02543, USA
U.S. Army Engineer Research and Development Center, Vicksburg, MS 39180, USA
Author to whom correspondence should be addressed.
J. Mar. Sci. Eng. 2019, 7(4), 110;
Received: 6 March 2019 / Revised: 29 March 2019 / Accepted: 1 April 2019 / Published: 19 April 2019
The traditional flow of coastal ocean model data is from High-Performance Computing (HPC) centers to the local desktop, or to a file server where just the needed data can be extracted via services such as OPeNDAP. Analysis and visualization are then conducted using local hardware and software. This requires moving large amounts of data across the internet as well as acquiring and maintaining local hardware, software, and support personnel. Further, as data sets increase in size, the traditional workflow may not be scalable. Alternatively, recent advances make it possible to move data from HPC to the Cloud and perform interactive, scalable, data-proximate analysis and visualization, with simply a web browser user interface. We use the framework advanced by the NSF-funded Pangeo project, a free, open-source Python system which provides multi-user login via JupyterHub and parallel analysis via Dask, both running in Docker containers orchestrated by Kubernetes. Data are stored in the Zarr format, a Cloud-friendly n-dimensional array format that allows performant extraction of data by anyone without relying on data services like OPeNDAP. Interactive visual exploration of data on complex, large model grids is made possible by new tools in the Python PyViz ecosystem, which can render maps at screen resolution, dynamically updating on pan and zoom operations. Two examples are given: (1) Calculating the maximum water level at each grid cell from a 53-GB, 720-time-step, 9-million-node triangular mesh ADCIRC simulation of Hurricane Ike; (2) Creating a dashboard for visualizing data from a curvilinear orthogonal COAWST/ROMS forecast model. View Full-Text
Keywords: ocean modeling; cloud computing; data analysis; geospatial data visualization ocean modeling; cloud computing; data analysis; geospatial data visualization
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Signell, R.P.; Pothina, D. Analysis and Visualization of Coastal Ocean Model Data in the Cloud. J. Mar. Sci. Eng. 2019, 7, 110.

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