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  • 1
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    Lagrangian ocean analysis: fundamentals and practices (2018)
    Elsevier
    In:  Ocean Modelling, 121 . pp. 49-75.
    Details
    Publication Date: 2021-02-08
    Description: Highlights: • Lagrangian ocean analysis is a powerful way to analyse the output of ocean circulation models • We present a review of the Kinematic framework, available tools, and applications of Lagrangian ocean analysis • While there are unresolved questions, the framework is robust enough to be used widely in ocean modelling Abstract: 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 tracking virtual particles. We then showcase some of the innovative applications of trajectory data, and conclude with some open questions and an outlook. The overall goal of this review paper is to reconcile some of the different techniques and methods in Lagrangian ocean analysis, while recognising the rich diversity of codes that have and continue to emerge, and the challenges of the coming age of petascale computing.
    Type: Article , PeerReviewed
    Format: text
    DOI: 10.1016/j.ocemod.2017.11.008
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    OceanRep: Article in a Scientific Journal - peer-reviewed
  • 2
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    Identifying Lagrangian Coherent Vortices in a Mesoscale Ocean Model (2018)
    Elsevier
    In:  Ocean Modelling, 130 . pp. 15-28.
    Details
    Publication Date: 2021-02-08
    Description: Highlights: • Lagrangian vortices are identified in a mesoscale eddy-permitting ocean model. • Rigorous sensitivity analysis of the methods tuning parameters is conducted. • The Coherency Index, a new Lagrangian diagnostic, is introduced to quantify the material coherency of a vortex. • The spectrum of vortex coherency is explored by identifying leaky, moderately coherent, and strictly coherent vortices. We identify Lagrangian coherent vortices in a global mesoscale eddy-permitting ocean model using the rotation-based method of Haller et al. (2016). We present an analysis of the acute sensitivity of the identification results to varying the method’s free parameters, and develop physically justified parameter choices that allow for systematic vortex identification. In contrast to prior vortex studies, we probe the broad spectrum of coherency in the ocean by determining free parameter choices that partition the spectrum into distinct allowing for the identification of strictly coherent, moderately coherent, and leaky vortices. Our tuning methodology is grounded in a combination of sensitivity analysis, convergence tests, and consideration of the ocean model’s physics. To aid in this process, we introduce the a novel Lagrangian diagnostic for mathematically quantifying the degree of material coherency of a Lagrangian vortex. We aim for this manuscript and the accompanying open-access code to serve as a manual and toolset for the oceanographer interested in harnessing a rigorous Lagrangian method to uncover coherent structures in ocean models and observations.
    Type: Article , PeerReviewed
    Format: text
    DOI: 10.1016/j.ocemod.2018.07.001
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    OceanRep: Article in a Scientific Journal - peer-reviewed
  • 3
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    The PANGEO Big Data Ecosystem and its use at CNES (2019)
    Publications Office of the European Union
    In:  In: Proc. of the 2019 conference on Big Data from Space (BiDS’2019), EUR 29660 EN. Publications Office of the European Union, Luxembourg, pp. 49-52. ISBN 978-92-76-00034-1
    Details
    Publication Date: 2019-03-18
    Type: Book chapter , NonPeerReviewed
    Format: text
    Format: text
    DOI: 10.2760/848593
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    OceanRep: Book chapter
  • 4
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    Cloud-based framework for inter-comparing submesoscale-permitting realistic ocean models (2022)
    Copernicus Publications (EGU)
    Details
    Publication Date: 2024-02-07
    Description: With the increase in computational power, ocean models with kilometer-scale resolution have emerged over the last decade. These models have been used for quantifying the energetic exchanges between spatial scales, informing the design of eddy parametrizations, and preparing observing networks. The increase in resolution, however, has drastically increased the size of model outputs, making it difficult to transfer and analyze the data. It remains, nonetheless, of primary importance to assess more systematically the realism of these models. Here, we showcase a cloud-based analysis framework proposed by the Pangeo project that aims to tackle such distribution and analysis challenges. We analyze the output of eight submesoscale-permitting simulations, all on the cloud, for a crossover region of the upcoming Surface Water and Ocean Topography (SWOT) altimeter mission near the Gulf Stream separation. The cloud-based analysis framework (i) minimizes the cost of duplicating and storing ghost copies of data and (ii) allows for seamless sharing of analysis results amongst collaborators. We describe the framework and provide example analyses (e.g., sea-surface height variability, submesoscale vertical buoyancy fluxes, and comparison to predictions from the mixed-layer instability parametrization). Basin- to global-scale, submesoscale-permitting models are still at their early stage of development; their cost and carbon footprints are also rather large. It would, therefore, benefit the community to document the different model configurations for future best practices. We also argue that an emphasis on data analysis strategies would be crucial for improving the models themselves.
    Type: Article , PeerReviewed , info:eu-repo/semantics/article
    Format: text
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    OceanRep: Article in a Scientific Journal - peer-reviewed
  • 5
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    Diffusion-Based smoothers for spatial filtering of gridded geophysical data (2021)
    American Geophysical Union
    Details
    Publication Date: 2022-10-26
    Description: © The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Grooms, I., Loose, N., Abernathey, R., Steinberg, J. M., Bachman, S. D., Marques, G., Guillaumin, A. P., & Yankovsky, E. Diffusion-Based smoothers for spatial filtering of gridded geophysical data. Journal of Advances in Modeling Earth Systems, 13(9), (2021): e2021MS002552, https://doi.org/10.1029/2021MS002552.
    Description: We describe a new way to apply a spatial filter to gridded data from models or observations, focusing on low-pass filters. The new method is analogous to smoothing via diffusion, and its implementation requires only a discrete Laplacian operator appropriate to the data. The new method can approximate arbitrary filter shapes, including Gaussian filters, and can be extended to spatially varying and anisotropic filters. The new diffusion-based smoother's properties are illustrated with examples from ocean model data and ocean observational products. An open-source Python package implementing this algorithm, called gcm-filters, is currently under development.
    Description: I.G. and N.L. are supported by NSF OCE 1912332. R.A. is supported by NSF OCE 1912325. J.S. is supported by NSF OCE 1912302. S.B. and G.M. are supported by NSF OCE 1912420. A.G. and E.Y. are supported by NSF GEO 1912357 and NOAA CVP NA19OAR4310364.
    Keywords: Spatial filtering ; Coarse graining ; Data analysis
    Repository Name: Woods Hole Open Access Server
    Type: Article
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    PAPER (OA)
  • 6
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    Cloud-based framework for inter-comparing submesoscale-permitting realistic ocean models (2022)
    In:  EPIC3Geoscientific Model Development, 15(14), pp. 5829-5856, ISSN: 1991-9603
    Details
    Publication Date: 2022-08-16
    Description: With the increase in computational power, ocean models with kilometer-scale resolution have emerged over the last decade. These models have been used for quantifying the energetic exchanges between spatial scales, informing the design of eddy parametrizations, and preparing observing networks. The increase in resolution, however, has drastically increased the size of model outputs, making it difficult to transfer and analyze the data. It remains, nonetheless, of primary importance to assess more systematically the realism of these models. Here, we showcase a cloud-based analysis framework proposed by the Pangeo project that aims to tackle such distribution and analysis challenges. We analyze the output of eight submesoscale-permitting simulations, all on the cloud, for a crossover region of the upcoming Surface Water and Ocean Topography (SWOT) altimeter mission near the Gulf Stream separation. The cloud-based analysis framework (i) minimizes the cost of duplicating and storing ghost copies of data and (ii) allows for seamless sharing of analysis results amongst collaborators. We describe the framework and provide example analyses (e.g., sea-surface height variability, submesoscale vertical buoyancy fluxes, and comparison to predictions from the mixed-layer instability parametrization). Basin- to global-scale, submesoscale-permitting models are still at their early stage of development; their cost and carbon footprints are also rather large. It would, therefore, benefit the community to document the different model configurations for future best practices. We also argue that an emphasis on data analysis strategies would be crucial for improving the models themselves.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , NonPeerReviewed
    Format: application/pdf
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    PAPER (OA)
  • 7
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    Seasonality of the mesoscale inverse cascade as inferred from global scale-dependent eddy energy observations (2022)
    American Meteorological Society
    Details
    Publication Date: 2022-09-01
    Description: Author Posting. © American Meteorological Society, 2022. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Physical Oceanography 52(8), (2022): 1677-1691, https://doi.org/10.1175/jpo-d-21-0269.1.
    Description: Oceanic mesoscale motions including eddies, meanders, fronts, and filaments comprise a dominant fraction of oceanic kinetic energy and contribute to the redistribution of tracers in the ocean such as heat, salt, and nutrients. This reservoir of mesoscale energy is regulated by the conversion of potential energy and transfers of kinetic energy across spatial scales. Whether and under what circumstances mesoscale turbulence precipitates forward or inverse cascades, and the rates of these cascades, remain difficult to directly observe and quantify despite their impacts on physical and biological processes. Here we use global observations to investigate the seasonality of surface kinetic energy and upper-ocean potential energy. We apply spatial filters to along-track satellite measurements of sea surface height to diagnose surface eddy kinetic energy across 60–300-km scales. A geographic and scale-dependent seasonal cycle appears throughout much of the midlatitudes, with eddy kinetic energy at scales less than 60 km peaking 1–4 months before that at 60–300-km scales. Spatial patterns in this lag align with geographic regions where an Argo-derived estimate of the conversion of potential to kinetic energy is seasonally varying. In midlatitudes, the conversion rate peaks 0–2 months prior to kinetic energy at scales less than 60 km. The consistent geographic patterns between the seasonality of potential energy conversion and kinetic energy across spatial scale provide observational evidence for the inverse cascade and demonstrate that some component of it is seasonally modulated. Implications for mesoscale parameterizations and numerical modeling are discussed.
    Description: This work was generously funded by NSF Grants OCE-1912302, OCE-1912125 (Drushka), and OCE-1912325 (Abernathey) as part of the Ocean Energy and Eddy Transport Climate Process Team.
    Keywords: Eddies ; Energy transport ; Mesoscale processes ; Turbulence ; Oceanic mixed layer ; Altimetry ; Seasonal cycle
    Repository Name: Woods Hole Open Access Server
    Type: Article
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    PAPER (OA)
  • 8
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    Lagrangian ocean analysis : fundamentals and practices (2018)
    Elsevier
    Details
    Publication Date: 2022-05-26
    Description: © The Author(s), 2017. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Ocean Modelling 121 (2018): 49-75, doi:10.1016/j.ocemod.2017.11.008.
    Description: 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 tracking virtual particles. We then showcase some of the innovative applications of trajectory data, and conclude with some open questions and an outlook. The overall goal of this review paper is to reconcile some of the different techniques and methods in Lagrangian ocean analysis, while recognising the rich diversity of codes that have and continue to emerge, and the challenges of the coming age of petascale computing.
    Description: EvS has received funding from the European Research Council (ERC) under the European Unions Horizon 2020 research and innovation programme (grant agreement No 715386). This research for PJW 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. Funding for HFD was provided by Grant No. DE-SC0012457 from the US Department of Energy. PB acknowledges support for this work from NERC grant NE/R011567/1. SFG is supported by NERC National Capability funding through the Extended Ellett Line Programme.
    Keywords: Ocean circulation ; Lagrangian analysis ; Connectivity ; Particle tracking ; Future modelling
    Repository Name: Woods Hole Open Access Server
    Type: Article
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    PAPER (OA)
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