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Lagrangian ocean analysis: fundamentals and practices ArchiMer
Van Sebille, Erik; Griffies, Stephen M.; Abernathey, Ryan; Adams, Thomas P.; Berloff, Pavel; Biastoch, Arne; Blanke, Bruno; Chassignet, Eric P.; Cheng, Yu; Cotter, Colin J.; Deleersnijder, Eric; Doos, Kristofer; Drake, Henri F.; Drijfhout, Sybren; Gary, Stefan F.; Heemink, Arnold W.; Kjellsson, Joakim; Koszalka, Inga Monika; Lange, Michael; Lique, Camille; Macgilchrist, Graeme A.; Marsh, Robert; Adame, C. Gabriela Mayorga; Mcadam, Ronan; Nencioli, Francesco; Paris, Claire B.; Piggott, Matthew D.; Polton, Jeff A.; Ruehs, Siren; Shah, Syed H. A. M.; Thomas, Matthew; Wang, Jinbo; Wolfram, Phillip J.; Zanna, Laure; Zika, Jan D..
Lagrangian analysis is a powerful way to analyse the output of ocean circulation models and other ocean velocity data such as from altimetry. In the Lagrangian approach, large sets of virtual particles are integrated within the three-dimensional, time-evolving velocity fields. Over several decades, a variety of tools and methods for this purpose have emerged. Here, we review the state of the art in the field of Lagrangian analysis of ocean velocity data, starting from a fundamental kinematic framework and with a focus on large-scale open ocean applications. Beyond the use of explicit velocity fields, we consider the influence of unresolved physics and dynamics on particle trajectories. We comprehensively list and discuss the tools currently available for...
Tipo: Text Palavras-chave: Ocean circulation; Lagrangian analysis; Connectivity; Particle tracking; Future modelling.
Ano: 2018 URL: http://archimer.ifremer.fr/doc/00412/52324/53099.pdf
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Surface Kinetic Energy Distributions in the Global Oceans From a High‐Resolution Numerical Model and Surface Drifter Observations ArchiMer
Yu, Xiaolong; Ponte, Aurelien; Elipot, Shane; Menemenlis, Dimitris; Zaron, Edward D.; Abernathey, Ryan.
The surface kinetic energy of a 1/48° global ocean simulation and its distribution as a function of frequency and location are compared with the one estimated from 15,329 globally distributed surface drifter observations at hourly resolution. These distributions follow similar patterns with a dominant low‐frequency component and well‐defined tidal and near‐inertial peaks globally. Quantitative differences are identified with deficits of low‐frequency energy near the equator (factor 2) and at near‐inertial frequencies (factor 3) and an excess of energy at semidiurnal frequencies (factor 4) for the model. Owing to its hourly resolution and its near‐global spatial coverage, the array of surface drifters is an invaluable tool to evaluate the realism of...
Tipo: Text Palavras-chave: LLC4320; Surface drifter; Rotary spectrum; SWOT.
Ano: 2019 URL: https://archimer.ifremer.fr/doc/00514/62517/66817.pdf
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The Pangeo Big Data Ecosystem and its use at CNES ArchiMer
Eynard-bontemps, Guillaume; Abernathey, Ryan; Hamman, Joseph; Ponte, Aurelien; Rath, Willi.
Pangeo[1] is a community-driven effort for open-source big data initially focused on the Earth System Sciences. One of its primary goals is to enable scientists in analyzing petascale datasets both on classical high-performance computing (HPC) and on public cloud infrastructure. In only a few years, Pangeo has grown into a very productive community collaborating on the development of open-source analysis tools for science. It provides a set of example deployments based on open-source Scientific Python packages like Jupyter[2], Dask[3], and Xarray[4] that bring together scientists and developer with their actual use-cases. In this paper, we first describe Pangeo ecosystem and community. We then present its impact on the work of scientists from CNES on the...
Tipo: Text Palavras-chave: Pangeo; Dask; Jupyter; HPC; Cloud; Big Data; Analysis; Open Source.
Ano: 2019 URL: https://archimer.ifremer.fr/doc/00503/61441/65160.pdf
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The Pangeo Ecosystem: Interactive Computing Tools for the Geosciences: Benchmarking on HPC ArchiMer
Odaka, Tina; Banihirwe, Anderson; Eynard-bontemps, Guillaume; Ponte, Aurelien; Maze, Guillaume; Paul, Kevin; Baker, Jared; Abernathey, Ryan.
The Pangeo ecosystem is an interactive computing software stack for HPC and public cloud infrastructures. In this paper, we show benchmarking results of the Pangeo platform on two di erent HPC sys- tems. Four di erent geoscience operations were considered in this bench- marking study with varying chunk sizes and chunking schemes. Both strong and weak scaling analyses were performed. Chunk sizes between 64MB to 512MB were considered, with the best scalability obtained for 512MB. Compared to certain manual chunking schemes, the auto chunk- ing scheme scaled well.
Tipo: Text Palavras-chave: Pangeo; Interactive computing; HPC; Cloud; Benchmarking; Dask; Xarray.
Ano: 2019 URL: https://archimer.ifremer.fr/doc/00597/70946/69187.pdf
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