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    Analysis and visualization of coastal ocean model data in the cloud. (2019)
    MDPI
    Details
    Publication Date: 2022-10-27
    Description: © The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Signell, R. P., & Pothina, D. Analysis and visualization of coastal ocean model data in the cloud. Journal of Marine Science and Engineering, 7(4), (2019);110, doi:10.3390/jmse7040110.
    Description: 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.
    Description: This research benefited from National Science Foundation grant number 1740648, and EarthSim project was funded by ERDC projects PETTT BY17-094SP and PETTT BY16-091SP. This project also benefited from research credits granted by Amazon.
    Keywords: Ocean modeling ; Cloud computing ; Data analysis ; Geospatial data visualization
    Repository Name: Woods Hole Open Access Server
    Type: Article
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  • 2
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    Estimating connectivity of hard clam (Mercenaria mercenaria) and eastern oyster (Crassostrea virginica) larvae in Barnegat Bay (2019)
    MDPI
    Details
    Publication Date: 2022-10-27
    Description: © The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Goodwin, J. D., Munroe, D. M., Defne, Z., Ganju, N. K., & Vasslides, J. Estimating connectivity of hard clam (Mercenaria mercenaria) and eastern oyster (Crassostrea virginica) larvae in Barnegat Bay. Journal of Marine Science and Engineering, 7(6), (2019): 167, doi:10.3390/jmse7060167.
    Description: Many marine organisms have a well-known adult sessile stage. Unfortunately, our lack of knowledge regarding their larval transient stage hinders our understanding of their basic ecology and connectivity. Larvae can have swimming behavior that influences their transport within the marine environment. Understanding the larval stage provides insight into population connectivity that can help strategically identify areas for restoration. Current techniques for understanding the larval stage include modeling that combines particle attributes (e.g., larval behavior) with physical processes of water movement to contribute to our understanding of connectivity trends. This study builds on those methods by using a previously developed retention clock matrix (RCM) to illustrate time dependent connectivity of two species of shellfish between areas and over a range of larval durations. The RCM was previously used on physical parameters but we expand the concept by applying it to biology. A new metric, difference RCM (DRCM), is introduced to quantify changes in connectivity under different scenarios. Broad spatial trends were similar for all behavior types with a general south to north progression of particles. The DRCMs illustrate differences between neutral particles and those with behavior in northern regions where stratification was higher, indicating that larval behavior influenced transport. Based on these findings, particle behavior led to small differences (north to south movement) in transport patterns in areas with higher salinity gradients (the northern part of the system) compared to neutral particles. Overall, the dominant direction for particle movement was from south to north, which at times was enhanced by winds from the south. Clam and oyster restoration in the southern portion of Barnegat Bay could serve as a larval supply for populations in the north. These model results show that coupled hydrodynamic and particle tracking models have implications for fisheries management and restoration activities.
    Description: This work is supported by the Barnegat Bay Partnership EPA grants CE98212311, CE98212312. We extend our deep thanks to anonymous reviewers and Lisa Lucas who provided thoughtful input that improved the manuscript. We thank Matthew Kozak and Ian Mitchell for technical advice and Elizabeth North for LTRANS guidance. Joe Caracapa and Jennifer Gius provided help running remote simulations. COAST model source code is available at https://code.usgs.gov/coawstmodel/COAWST [50]. The hydrodynamic model outoput is available at: http://geoport.whoi.edu/thredds/catalog/clay/usgs/users/zdefne/GRL/catalog.html [21] and particle tracking model outputs are available from the corresponding author upon request.
    Keywords: Bivalve connectivity ; Larval transport ; Modeling ; Retention clock ; RCM ; ROMS ; LTRANS ; Barnegat Bay ; Hard clam ; Eastern oyster
    Repository Name: Woods Hole Open Access Server
    Type: Article
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