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REVIEW 5 major objections 6 minor 51 references

A Scalable System for Visual Analysis of Ocean Data

T0 review · 5 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read This paper introduces pyParaOcean, a set of ParaView plugins that provide interactive, scalable 3D visualization for oceanography, including parallel eddy detection and salinity front tracking, supported by a Cinema database that shrinks…

desk verdict A useful, honest systems paper whose scaling evidence proves compute-kernel speedup but not end-to-end interactivity over remote I/O. read the letter →

arxiv 2501.05009 v1 pith:V5CMBJHR submitted 2025-01-09 cs.GR cs.CVcs.DC

Toshit Jain , Upkar Singh , Varun Singh , Vijay Kumar Boda , Ingrid Hotz , Sathish S. Vadhiyar , P. N. Vinayachandran , Vijay Natarajan This is my paper · ORCID
classification cs.GRcs.CVcs.DC
keywords oceanvisualizationParaViewplugineddydetectionsalinityfronttrackingCinemadatabaseparallelscalingBayofBengalscientific
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper introduces pyParaOcean, a set of filters that plug into the ParaView visualization engine to give oceanographers interactive, three-dimensional tools for routine tasks: tracing currents with streamlines and pathlines, extracting and animating eddies, following high-salinity water masses with tracked surface fronts, and probing vertical water columns. The reason to care is scale: ocean model outputs are now too large and too remote to load onto a laptop, so the paper combines ParaView's server-client parallelism with a compact Cinema image database that shrinks a 750 GB Bay of Bengal dataset to 2.6 GB for fast overviews. Together these pieces are meant to let an analyst flip through time steps and depth slices interactively, then jump into full-resolution 3D analysis only where it is needed.

What carries the argument

The system's core is a set of Python filters registered as ParaView plugins, so every module inherits ParaView's distributed data processing, server-client rendering, and user interface. Two mechanisms carry the argument: a depth-first data redistribution that gives each core an equal mix of ocean and land cells and eliminates ghost-cell communication for streamline integration, and a front-tracking pipeline that turns the salinity isovolume boundary into binary grids so neighbor search becomes matrix multiplication and connected-component labeling, parallelized across time steps with MPI. The Cinema database generator is the third pillar: it pre-renders float images for every depth slice and time step of each scalar field, reducing a 750 GB dataset to 2.6 GB so overview analysis no longer pays the growing NetCDF metadata serialization and network bandwidth costs.

What would settle it

Run the same 500-seed, 10-time-step pathline experiment on the same cluster but start from a cold cache and without preloading, measuring the full wall-clock time from read request to completed integration across 2, 16, 64, and 160 cores; if the total time increases with core count or stays flat because NetCDF metadata serialization dominates, then interactivity holds only for the preloaded workflow, not for typical remote-server use.

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Extended reading notes

Core claim

On its own terms, the paper claims that the common workflow of an oceanographer—inspecting 3D salinity and temperature fields, identifying mesoscale eddies, and tracking water masses—can be supported in one scalable, interactive system built on ParaView. The load-bearing evidence is a case study in the Bay of Bengal showing the system following a 500 km anticyclonic eddy through formation and decay, tracking high-salinity water from the Arabian Sea toward the Indian coast, and resolving thin low-salinity filaments in a ~1 km ROMS simulation that vanish at coarser resolution. A parallel implementation of a previously serial front-tracking algorithm, run over MPI across time steps and depth slices, brings the salinity tracking runtime to about ten minutes for the largest volumes tested, and the Cinema database removes the I/O and network transfer bottleneck that otherwise dominates. The paper's conclusion is that this combination makes large remote ocean datasets explorable on an ordinary workstation.

Load-bearing premise

The reported interactive scaling for the pathline filter assumes the needed time steps are already in memory before the clock starts, so the measured speedups exclude the time taken to read NetCDF files over the network from a remote server; that I/O time, which the paper itself shows growing with core count, is the load-bearing premise for the claimed interactivity.

Editorial extensions

If this is right

  • If the scaling claims hold, oceanographers can routinely run eddy detection and salinity tracking on ~1 km regional simulations from a laptop client connected to a cluster.
  • The Cinema database makes quick overviews of a large remote dataset a local, interactive operation instead of a multi-terabyte transfer.
  • The parallel front-tracking implementation puts a previously serial salinity-propagation method within reach of full multi-year, multi-gigabyte model outputs.
  • Depth-sliced partitioning removes ghost-cell overhead for flow visualization, so speedups appear even when ParaView's default partition would distribute land-heavy blocks unevenly.
  • Higher-resolution ROMS data visibly preserves filaments and eddy shapes that a coarser grid blurs or breaks.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • An implication the paper leaves implicit: the same plugin design and Cinema workflow should transfer to other geoscience fields, since atmospheric and meteorological models use the same time-varying NetCDF fields, making the contribution a template rather than a domain-specific tool.
  • The reported pathline timings preload time steps into memory; extending the system to overlap I/O with computation or to read only the time range of interest would be a natural next step the paper does not demonstrate.
  • The Cinema database stores a fixed set of scalars, so deep analysis of derived quantities still requires the full dataset; a testable extension would be to make the generator field-configurable and to generate derived-field images, such as vorticity or Okubo-Weiss criteria, on demand.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 6 minor

Summary. The paper introduces pyParaOcean, a plugin-based visual analysis system built on ParaView for oceanographic data, with modules for seed placement and fieldlines, isovolumes, depth profiles, surface front tracking, eddy identification, and Cinema database generation. The authors report scaling studies on the GLORYS12V1 and ROMS datasets over the Bay of Bengal and present a qualitative case study conducted with an oceanographer. The central claim is that pyParaOcean is a scalable and interactive system that supports the full range of oceanographic visualization tasks from quick overview to detailed filter-based exploration.

Significance. If the central claim holds, pyParaOcean is a timely and useful systems contribution: it reduces the barrier to scalable ocean data analysis by packaging specialized functionality as ParaView filters, reuses the distributed architecture of ParaView, discloses prior work on front tracking [SDVN22], and makes the code publicly available. The Cinema database generator achieves a striking data reduction (approximately 750 GB to 2.6 GB) and addresses a real operational bottleneck. At present, however, the evidence does not fully establish end-to-end scalability for the intended remote server-client deployment, and several inconsistencies in data provenance and image encoding need to be resolved before the system's claims are fully supported.

major comments (5)
  1. [§6.2, Fig. 6c; §6.5] The pathline scaling experiment preloads 10 time steps into memory and explicitly excludes file I/O and network transfer from the reported runtimes. Given that Sections 1 and 4.6 state that ocean data are typically stored on remote servers, and Section 6.5 (Fig. 11) shows that NetCDF load time grows with core count before flattening, the reported speedups support only compute-kernel scaling under memory-resident data. They do not support the end-to-end 'scalable and interactive' claim for the intended server-client deployment, where every new time step fetched from remote storage incurs the excluded I/O. Please either measure end-to-end timings that include remote data fetch, or qualify the interactive scaling claim to the compute kernel only.
  2. [§7, Figs. 13–16] The case study uses dates in 2020 (Fig. 13 'August 2020'; Fig. 14 'July 1, 2020', 'July 15, 2020', 'July 31, 2020'), but Section 2 describes only GLORYS12V1 (June–September 2016) and ROMS (integrated 2012–2013). No 2020 dataset is specified in the data section, so the provenance of the case-study figures is unclear and the study is not reproducible as written. Please correct the figure dates to match the described datasets or add the missing dataset specification.
  3. [§4.7] The text states that pyParaOcean generates 'high-resolution float images' stored in 'the standard PNG format, where each pixel contains the corresponding value of the scalar data,' and that this strategy 'preserves the submesoscale features.' Standard PNG does not support floating-point pixel values. Please clarify the actual encoding (e.g., 16-bit integer scaling), quantify the resulting precision loss, and justify the preservation claim for submesoscale features.
  4. [§6.2, Figs. 6, 8, 9] Scaling timings are reported without repeated trials or error bars, and the caption of Fig. 6a describes the curve as 'linear' even though the plot clearly saturates to a smaller slope. Without error estimates or multiple runs, the saturation points and the 'close to linear' complexity claims in Figs. 8 and 9 are not rigorously established. Please provide repeated measurements with variability indicators, or state explicitly that the curves are single-run illustrations.
  5. [§6.3 vs. §4.4] The scaling studies for front tracking (Figs. 7–9) appear to be executed with the standalone MPI script described in Section 4.4, while the pyParaOcean front-tracking filter itself is stated to run only on the local workstation's cores. The contribution list and Section 6 title imply that these results characterize pyParaOcean modules. Please clarify this distinction in Section 6 so that the 'pyParaOcean modules scale' claim is not overstated, and specify whether the reported front-tracking timings include the plugin filter or only the standalone script.
minor comments (6)
  1. [§2] ROMS is described as a reanalysis dataset, but the text later states that ROMS is a regional ocean model integrated without salinity relaxation and forced by HYCOM reanalysis data; it is a model simulation, not a reanalysis product. Please correct the classification.
  2. [Fig. 10 caption] The caption refers to 'Figure 3' when comparing partitioning schemes for larger numbers of blocks; the intended reference is likely Figures 5(e,f).
  3. [§6.3] The sentence 'On average, it takes 9.6 and 7.1 minutes to process the largest volumes of ROMS and GLORYS' does not specify the number of cores used for those runs. Please include the core count.
  4. [§8] The user experience section is based on anecdotal comments from two oceanographers, one of whom is a coauthor; this should be presented as informal feedback rather than as a systematic user evaluation.
  5. [§4.4] The sentence 'Two fronts correspond from consecutive time steps correspond to each other' contains a duplicated word; please revise.
  6. [§6.5, Fig. 11] Figure 11 shows no error bars and does not state how many repetitions were averaged. Please indicate the measurement variability for the I/O time results.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: pyParaOcean's scaling claims rest on direct measurements, with only minor disclosed self-citations that are not load-bearing.

full rationale

The paper is a systems paper, not a derivation, and its central claim is that pyParaOcean is a scalable and interactive visualization system. That claim is supported by direct experiments: filter runtimes versus core count in Figures 6 and 10, front-tracking weak and strong scaling in Figures 7-9, I/O measurements in Figure 11, and a qualitative case study in Section 7. There are no fitted parameters that are later presented as predictions, no uniqueness theorems, and no ansatz smuggled in through citation. The authors do cite their own prior work [SDVN22, SVN24, JSB*23] as the source of the front-tracking method and a preliminary system version, but that is disclosed and is not load-bearing for the scaling evidence: the parallel front-tracking timings are new measurements of an MPI implementation, and the citation does not substitute for those measurements. One methodological limitation is visible in Section 6.2, where the pathline timing 'preloads 10 time steps into the memory' to report computation times 'not inflated due to I/O and the network bandwidth constraints,' while Section 6.5 shows that I/O time increases with core count. This means the end-to-end interactive claim is stronger than the evidence directly supports for remote server-client deployments, and the Cinema database is offered as a mitigation. That is an evidence gap or correctness risk, not circularity: the measured scaling is still an independent empirical fact about the compute kernel. Accordingly, the circularity score is 2, reflecting minor non-load-bearing self-citations without any circular reduction of the central claim.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The paper introduces no physical or mathematical entities. It relies on standard computational infrastructure and on prior work by the same group for the front tracking method. The main unstated parameters are the user-facing algorithmic parameters that are not given concrete values.

free parameters (2)
  • distance parameter n in front tracking = not specified
    User-specified neighborhood distance for surface front extraction and correspondence; no default value or used value is reported in the paper, affecting reproducibility of the front tracking results.
  • persistence threshold for eddy simplification = not specified
    Eddy filter applies topological persistence-driven simplification [TFL*17] to remove noise; the threshold is not reported, so the eddy detection results cannot be exactly reproduced.
assumptions (4)
  • domain assumption ParaView/VTK parallel partitioning and ghost cell handling work as documented.
    The system relies on ParaView's server-client architecture and ghost cell mechanism to produce correct filter outputs in parallel (Section 3.2).
  • domain assumption The front tracking algorithm of Singh et al. [SDVN22] correctly identifies high salinity water movement.
    The paper builds on this prior algorithm without re-validating its oceanographic correctness, citing [SDVN22, SVN24].
  • domain assumption The GLORYS12V1 and ROMS reanalysis datasets accurately represent ocean state.
    The case study and scaling experiments use these datasets as ground truth for oceanographic features without independent validation (Section 2).
  • standard math Standard computational topology tools (persistence, connected components) are correct.
    Used for eddy simplification and front tracking grids; no formal proof or verification is included.

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Pith. "Pith review of A Scalable System for Visual Analysis of Ocean Data." pith.science (2026). https://pith.science/paper/V5CMBJHR

@misc{pith2026250105009,
  author       = {Pith},
  title        = {Pith review of: A Scalable System for Visual Analysis of Ocean Data},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/V5CMBJHR}},
  note         = {Machine review of arXiv:2501.05009}
}
read the original abstract

Oceanographers rely on visual analysis to interpret model simulations, identify events and phenomena, and track dynamic ocean processes. The ever increasing resolution and complexity of ocean data due to its dynamic nature and multivariate relationships demands a scalable and adaptable visualization tool for interactive exploration. We introduce pyParaOcean, a scalable and interactive visualization system designed specifically for ocean data analysis. pyParaOcean offers specialized modules for common oceanographic analysis tasks, including eddy identification and salinity movement tracking. These modules seamlessly integrate with ParaView as filters, ensuring a user-friendly and easy-to-use system while leveraging the parallelization capabilities of ParaView and a plethora of inbuilt general-purpose visualization functionalities. The creation of an auxiliary dataset stored as a Cinema database helps address I/O and network bandwidth bottlenecks while supporting the generation of quick overview visualizations. We present a case study on the Bay of Bengal (BoB) to demonstrate the utility of the system and scaling studies to evaluate the efficiency of the system.

Figures

Figures reproduced from arXiv: 2501.05009 by the authors.

Figure 1
Figure 1. pyParaOcean functionality and user interface. (A) All pyParaOcean modules are implemented as ParaView filters. (B) ParaView pipeline browser shows the different datasets under study and the filters applied on them. (C) The seeding filter from pyParaOcean provides multiple options for tracing fieldlines. The figure illustrates the usage of various filters showcasing (D) salinity visualization using volume rendering, … view at source ↗
Figure 2
Figure 2. shows the architecture of pyParaOcean, which com￾prises of data parallel filters specifically designed to offer visu￾alization capabilities for the interactive exploration of the three￾dimensional ocean data. This design supports easy incorporation of new functionalities as filters in ParaView, the parallel execution of the data server and the render server, and interactive exploration through the client. 3.1. Paral… view at source ↗
Figure 3
Figure 3. (a) An example 2D mesh. (b) The mesh is partitioned for parallel processing. Four processes are created and each chunk is sent to a different process. Each process is represented by a unique color ( ). The vtkDataSetSurfaceFilter filter is applied to the dataset to compute its boundary. (c) Output of the filter when there are no ghost cells. The filter incorrectly reports edges from the interior of the data as bound… view at source ↗
Figures from the paper (15 more)
Figure 4
Figure 4. Figure 4: Cinema view with sliding toggles to scroll through the depth slices, time steps, and different scalar fields specification that describe the structure and content of the data. It offers a hierarchical structure for complex data, is platform￾independent, supports large …
Figure 5
Figure 5. Figure 5: Partitioning the spatial domain of the ROMS dataset for efficient visualization. Each block in the partition, represented using a unique color, is sent to a unique core that processes the data within the block independently. (a,c) partitioning into 2 blocks. (b,d) part…
Figure 6
Figure 6. Figure 6: Scaling behavior of pyParaOcean filters. (a) The seed placement filter applied on the ROMS dataset. The seeds are weighted according to vorticity. The scaling is linear, with a steeper drop in runtime up to 16 cores followed by a smaller slope. (b) The streamlines filt…
Figure 7
Figure 7. Figure 7: Weak scaling study. (a) ROMS data with a resolution of 96th of a degree. (b) GLORYS data with a resolution of 120th of a degree. In both datasets, we observe an increase in runtime as the number of cores and time steps increase. The curve appears to flatten towards the…
Figure 8
Figure 8. Figure 8: Runtime complexity. (a) Processing 240 time steps of increasing resolutions of the ROMS data using multiple cores. Let V denote the data size at a resolution of 12th of a degree, which corresponds to 265×229×200 voxels at each time step or 265×229×200×240 voxels in tot…
Figure 9
Figure 9. Figure 9: Strong scaling study. (a) Processing 240 time-steps of a fixed resolution ROMS data using an increasing the number of cores. (b) Processing 120 time steps of a fixed resolution GLORYS data. In both cases, runtime drop is steep initially followed by a eventual flattenin…
Figure 10
Figure 10. Figure 10: Effect of data distribution on scaling behavior. (a) A larger number of ghost cells are generated after data redistribution. (b) Streamlines are integrated with 500 seeds in the ROMS dataset. The filter is significantly faster for a smaller number of cores after redis…
Figure 11
Figure 11. Figure 11: I/O time increases with number of cores. Time taken to load a single time step in the NetCDF format from the ROMS dataset. The time to load a file into memory increases with number of cores and flattens after 32 cores [PITH_FULL_IMAGE:figures/full_fig_p012_11.png]
Figure 12
Figure 12. Figure 12: Currents and eddies in the BoB during the monsoon season, including the Summer Monsoon Current (SMC), the Sri Lanka Dome (SLD), and an anticyclonic eddy (AE). Downwelling [PITH_FULL_IMAGE:figures/full_fig_p012_12.png]
Figure 13
Figure 13. Figure 13: Dissipation of a large anticyclonic eddy in the BoB during August 2020. Streamlines with seeds near detected vortex cores are computed to show the evolution of eddy profiles in 3D. (a) (b) (c) [PITH_FULL_IMAGE:figures/full_fig_p013_13.png]
Figure 14
Figure 14. Figure 14: The BoB between July 1, 2020 and July 31, 2020. Visualization of the flow using streamlines with uniform seeding and the ≥ 35 psu salinity isovolume. (a) July 1, 2020: The AE is forming around 8°N and 90°E with the SMC streamlines visible from 78°E to 86°E. (b) July 1…
Figure 15
Figure 15. Figure 15: Visualizing movement of high salinity water via computation and tracking of surface fronts of high salinity isovolumes. (left) Sur￾face fronts computed at one time step. (middle, right) One of the components of the surface front moves towards the east coast of India, …
Figure 16
Figure 16. Figure 16: The depression of the 27◦ C isotherm (yellow) by the anticyclonic eddy in the BoB. A needle is dropped at 7◦ N, 84◦ E and the depth profile shows the temperature drop. The interactive parallel coordinates plot is used to brush-select 10 m intervals at depths of 25 m a…
Figure 17
Figure 17. Figure 17: The need for higher resolution data to extract certain ocean structures. (a) A low salinity isovolume (green colormap) and a higher salinity isovolume (blue-red colormap) extracted from the ROMS data at a resolution of 96th of a degree clearly depicts filaments and so…
Figure 18
Figure 18. Figure 18: Visualizing the temporal behavior of a low salinity filament using the depth profile filter on April 13 and 15, 2012. (a,b) Needle placed at 17.5◦ N, 88.5◦ E. Plot of salinity vs. depth and analysis of the filament isovolume may help in the study of barrier layers. (c…

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Reference graph

Works this paper leans on

51 extracted references · 44 canonical work pages

  1. [1]

    write newline

    " write newline "" before.all 'output.state := FUNCTION fin.entry add.period write newline FUNCTION new.block output.state before.all = 'skip after.block 'output.state := if FUNCTION new.sentence output.state after.block = 'skip output.state before.all = 'skip after.sentence 'output.state := if if FUNCTION not #0 #1 if FUNCTION and 'skip pop #0 if FUNCTIO...

  2. [2]

    write newline

    " write newline "" before.all 'output.state := FUNCTION fin.entry.original add.period write newline FUNCTION new.block output.state before.all = 'skip after.block 'output.state := if FUNCTION new.sentence output.state after.block = 'skip output.state before.all = 'skip after.sentence 'output.state := if if FUNCTION not #0 #1 if FUNCTION and 'skip pop #0 i...

  3. [3]

    : Paraview: An end-user tool for large data visualization

    Ahrens J., Geveci B., Law C. : Paraview: An end-user tool for large data visualization . The visualization handbook 717 (2005)

    work page 2005

  4. [4]

    P., Raitsos D

    Afzal S., Ghani S., Tissington G., Langodan S., Dasari H. P., Raitsos D. E., Gittings J. A., Jamil T., Srinivasan M., Hoteit I. : RedSeaAtlas : A visual analytics tool for spatio-temporal multivariate data of the red sea. In EnvirVis: Workshop on Visualization in Environmental Sciences (EnvirVis2019) (2019), pp. 25--32

    work page 2019

  5. [5]

    M., Ghani S., Jamil T., Knio O., Hadwiger M., Hoteit I

    Afzal S., Hittawe M. M., Ghani S., Jamil T., Knio O., Hadwiger M., Hoteit I. : The state of the art in visual analysis approaches for ocean and atmospheric datasets. Computer Graphics Forum 38, 3 (2019), 881--907

    work page 2019

  6. [6]

    H., Petersen M

    Ahrens J., Jourdain S., O'Leary P., Patchett J., Rogers D. H., Petersen M. : An image-based approach to extreme scale in situ visualization and analysis. In SC'14: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (2014), IEEE, pp. 424--434

    work page 2014

  7. [7]

    : Coherent mesoscale eddies in the North Atlantic subtropical gyre: 3-D structure and transport with application to the salinity maximum

    Amores A., Melnichenko O., Maximenko N. : Coherent mesoscale eddies in the North Atlantic subtropical gyre: 3-D structure and transport with application to the salinity maximum . Journal of Geophysical Research: Oceans 122, 1 (2017), 23--41

    work page 2017

  8. [8]

    L., Sprintall J., Rainville L., Lee C

    Anutaliya A., Send U., McClean J. L., Sprintall J., Rainville L., Lee C. M., Jinadasa S. U. P., Wallcraft A. J., Metzger E. J. : An undercurrent off the east coast of sri lanka. Ocean Science 13, 6 (2017), 1035--1044. URL: https://os.copernicus.org/articles/13/1035/2017/, https://doi.org/10.5194/os-13-1035-2017 doi:10.5194/os-13-1035-2017

    work page doi:10.5194/os-13-1035-2017 2017

Show all 51 references
  1. [9]

    : An oceanic general circulation model framed in hybrid isopycnic-cartesian coordinates

    Bleck R. : An oceanic general circulation model framed in hybrid isopycnic-cartesian coordinates. Ocean modelling 4, 1 (2002), 55--88

    2002

  2. [10]

    R., Bidigare R

    Benitez-Nelson C. R., Bidigare R. R., Dickey T. D., Landry M. R., Leonard C. L., Brown S. L., Nencioli F., Rii Y. M., Maiti K., Becker J. W., et al. : Mesoscale eddies drive increased silica export in the subtropical pacific ocean. Science 316, 5827 (2007), 1017--1021

    2007

  3. [11]

    P., Hurlburt H

    Chassignet E. P., Hurlburt H. E., Smedstad O. M., Halliwell G. R., Hogan P. J., Wallcraft A. J., Baraille R., Bleck R. : The hycom (hybrid coordinate ocean model) data assimilative system. Journal of Marine Systems 65, 1-4 (2007), 60--83

    2007

  4. [12]

    Real-time global forecasting CMEMS system, 2012

    Copernicus : GLORYS12V1 : Global Ocean Physics Reanalysis. Real-time global forecasting CMEMS system, 2012. URL: https://doi.org/10.48670/moi-00021, https://doi.org/10.48670/moi-00021 doi:10.48670/moi-00021

    2012 doi

  5. [13]

    : Uncertainty visualization using HDR volume rendering

    Dinesha V., Adabala N., Natarajan V. : Uncertainty visualization using HDR volume rendering. The Visual Computer 28 (2012), 265--278

    2012

  6. [14]

    : Data mining geoscientific data sets using self organizing maps

    Fraser S., Dickson B. : Data mining geoscientific data sets using self organizing maps. Mastering the Data Explosion in the Earth and Environmental Sciences, Extended Abstracts (2006), 5--7

    2006

  7. [15]

    https://ferret.pmel.noaa.gov/Ferret/, 2023

    Ferret. https://ferret.pmel.noaa.gov/Ferret/, 2023. [Online; accessed 28-April-2023]

    2023

  8. [16]

    : A winding angle framework for tracking and exploring eddy transport in oceanic ensemble simulations

    Friederici A., Falk M., Hotz I. : A winding angle framework for tracking and exploring eddy transport in oceanic ensemble simulations. In EnvirVis: Workshop on Visualization in Environmental Sciences (EnvirVis2021) (2021)

    2021

  9. [17]

    : Flow feature extraction in oceanographic visualization

    Guo D., Evangelinos C., Patrikalakis N. : Flow feature extraction in oceanographic visualization. In Proceedings of Computer Graphics International Conference (07 2004), pp. 162--173. https://doi.org/10.1109/CGI.2004.1309207 doi:10.1109/CGI.2004.1309207

    2004 arXiv

  10. [18]

    : COVE : A visual environment for ocean observatory design

    Grochow K., Stoermer M., Kelley D., Delaney J., Lazowska E. : COVE : A visual environment for ocean observatory design. Journal of Physics: Conference Series 125, 1 (2008), 012092

    2008

  11. [19]

    Halliwell G. R. : Evaluation of vertical coordinate and vertical mixing algorithms in the hybrid-coordinate ocean model (hycom). Ocean Modelling 7, 3-4 (2004), 285--322

    2004

  12. [20]

    : A hybrid 3d eddy detection technique based on sea surface height and velocity field

    Hua W., Bemis K., Kang D., Ozer S., Silver D. : A hybrid 3d eddy detection technique based on sea surface height and velocity field. arXiv preprint arXiv:2305.08229 (2023)

    2023 arXiv

  13. [21]

    R., Millman K

    Harris C. R., Millman K. J., van der Walt S. J., Gommers R., Virtanen P., Cournapeau D., Wieser E., Taylor J., Berg S., Smith N. J., Kern R., Picus M., Hoyer S., van Kerkwijk M. H., Brett M., Haldane A., del R \' i o J. F., Wiebe M., Peterson P., G \' e rard-Marchant P., Shepp...

    2020 doi

  14. [22]

    K., Singh U., Hotz I., Vinayachandran P., Natarajan V

    Jain T., Singh V., Boda V. K., Singh U., Hotz I., Vinayachandran P., Natarajan V. : pyParaOcean: A System for Visual Analysis of Ocean Data . In Workshop on Visualisation in Environmental Sciences (EnvirVis) (2023), Dutta S., Feige K., Rink K., Zeckzer D., (Eds.), The Eurograp...

    2023 doi

  15. [23]

    P., Nuncio M., Ramaiah N., Sardesai S., Narvekar J., Fernandes V., Paul J

    Kumar S. P., Nuncio M., Ramaiah N., Sardesai S., Narvekar J., Fernandes V., Paul J. T. : Eddy-mediated biological productivity in the Bay of Bengal during fall and spring intermonsoons. Deep Sea Research Part I: Oceanographic Research Papers 54, 9 (2007), 1619--1640

    2007

  16. [24]

    : Vapor: A visualization package tailored to analyze simulation data in earth system science

    Li S., Jaroszynski S., Pearse S., Orf L., Clyne J. : Vapor: A visualization package tailored to analyze simulation data in earth system science. Atmosphere 10, 9 (2019), 488

    2019

  17. [25]

    : Parallel netcdf: A high-performance scientific i/o interface

    Latham R., Zingale M., Thakur R., Gropp W., Gallagher B., Liao W., Siegel A., Ross R., Choudhary A., Li J. : Parallel netcdf: A high-performance scientific i/o interface. In SC Conference (Los Alamitos, CA, USA, nov 2003), IEEE Computer Society, p. 39. URL: https://doi.ieeecom...

    2003 arXiv

  18. [26]

    : A new approach to ocean eddy detection, tracking, and event visualization--application to the northwest pacific ocean

    Matsuoka D., Araki F., Inoue Y., Sasaki H. : A new approach to ocean eddy detection, tracking, and event visualization--application to the northwest pacific ocean. Procedia Computer Science 80 (2016), 1601--1611

    2016

  19. [27]

    McWilliams J. C. : The vortices of two-dimensional turbulence. Journal of Fluid mechanics 219 (1990), 361--385

    1990

  20. [28]

    McWilliams J. C. : The nature and consequences of oceanic eddies. Ocean modeling in an eddying regime 177 (2008), 5--15

    2008

  21. [29]

    : New chemical, bio-optical and physical observations of upper ocean response to the passage of a mesoscale eddy off bermuda

    McNeil J., Jannasch H., Dickey T., McGillicuddy D., Brzezinski M., Sakamoto C. : New chemical, bio-optical and physical observations of upper ocean response to the passage of a mesoscale eddy off bermuda. Journal of Geophysical Research: Oceans 104, C7 (1999), 15537--15548

    1999

  22. [30]

    https://marine.copernicus.eu/access-data/ocean-visualisation-tools, 2023

    Copernicus myOcean . https://marine.copernicus.eu/access-data/ocean-visualisation-tools, 2023. [Online; accessed 28-April-2023]

    2023

  23. [31]

    : OceanPaths : Visualizing multivariate oceanography data

    Nobre C., Lex A. : OceanPaths : Visualizing multivariate oceanography data. In Proceedings of the Eurographics Conference on Visualization (EuroVis 2015) - Short Papers (2015), The Eurographics Association. https://doi.org/10.2312/eurovisshort.20151124 doi:10.2312/eurovisshort...

    2015 doi

  24. [32]

    : Horizontal dispersion of floatable particles in the vicinity of velocity singularities such as convergences

    Okubo A. : Horizontal dispersion of floatable particles in the vicinity of velocity singularities such as convergences. Deep sea research and oceanographic abstracts 17, 3 (1970), 445--454

    1970

  25. [33]

    : Visualization of very large oceanography time-varying volume datasets

    Park S., Bajaj C., Ihm I. : Visualization of very large oceanography time-varying volume datasets. In Computational Science-ICCS 2004: 4th International Conference, Krak \'o w, Poland, June 6-9, 2004, Proceedings, Part II 4 (2004), Springer, pp. 419--426

    2004

  26. [34]

    https://ferret.pmel.noaa.gov/Ferret/, 2023

    pyferret. https://ferret.pmel.noaa.gov/Ferret/, 2023. [Online; accessed 28-April-2023]

    2023

  27. [35]

    https://bitbucket.org/vgl_iisc/pyparaocean/, 2024

    pyParaOcean . https://bitbucket.org/vgl_iisc/pyparaocean/, 2024. [Online; accessed 19-June-2024]

    2024

  28. [36]

    : Netcdf: an interface for scientific data access

    Rew R., Davis G. : Netcdf: an interface for scientific data access. IEEE Computer Graphics and Applications 10, 4 (1990), 76--82. https://doi.org/10.1109/38.56302 doi:10.1109/38.56302

    1990 doi

  29. [37]

    Rosenblum L. J. : Visualizing oceanographic data. IEEE computer graphics and applications 9, 3 (1989), 14--19

    1989

  30. [38]

    S., Robinson I

    Robinson I. S., Robinson I. S. : Mesoscale ocean features: Eddies. Discovering the Ocean from Space: The unique applications of satellite oceanography (2010), 69--114

    2010

  31. [39]

    : Dynamics of summer monsoon current around sri lanka

    Rath S., Vinayachandran P., Behara A., Neema C. : Dynamics of summer monsoon current around sri lanka. Ocean Dynamics 69 (2019), 1133--1154

    2019

  32. [40]

    Sarmiento J. L. : Ocean biogeochemical dynamics. In Ocean Biogeochemical Dynamics. Princeton university press, 2013

    2013

  33. [41]

    : CDO user guide, Oct

    Schulzweida U. : CDO user guide, Oct. 2019. URL: https://doi.org/10.5281/zenodo.3539275, https://doi.org/10.5281/zenodo.3539275 doi:10.5281/zenodo.3539275

    2019 doi

  34. [42]

    M., Vinayachandran P

    Singh U., Dhipu T. M., Vinayachandran P. N., Natarajan V. : Front and skeleton features based methods for tracking salinity propagation in the ocean. Computers & Geosciences 159 (2022), 104993. https://doi.org/https://doi.org/10.1016/j.cageo.2021.104993 doi:https://doi.org/10....

    2022

  35. [43]

    : A semi-implicit ocean circulation model using a generalized topography-following coordinate system

    Song Y., Haidvogel D. : A semi-implicit ocean circulation model using a generalized topography-following coordinate system. Journal of Computational Physics 115, 1 (1994), 228--244

    1994

  36. [44]

    F., McWilliams J

    Shchepetkin A. F., McWilliams J. C. : The regional oceanic modeling system (roms): a split-explicit, free-surface, topography-following-coordinate oceanic model. Ocean modelling 9, 4 (2005), 347--404

    2005

  37. [45]

    : The Visualization Toolkit (4th ed.)

    Schroeder W., Martin K., Lorensen B. : The Visualization Toolkit (4th ed.). Kitware, 2006

    2006

  38. [46]

    : Advection-based tracking and analysis of salinity movement in the indian ocean

    Singh U., Vinayachandran P., Natarajan V. : Advection-based tracking and analysis of salinity movement in the indian ocean. Computers & Geosciences 182 (2024), 105493

    2024

  39. [47]

    A., Gueunet C., Michaux M

    Tierny J., Favelier G., Levine J. A., Gueunet C., Michaux M. : The Topology Toolkit . IEEE Transactions on Visualization and Computer Graphics 24 , 1 (2017), 832--842

    2017

  40. [48]

    N., Chauhan P., Mohan M., Nayak S

    Vinayachandran P. N., Chauhan P., Mohan M., Nayak S. : Biological response of the sea around Sri Lanka to summer monsoon. Geophysical Research Letters 31, 1 (2004)

    2004

  41. [49]

    : Observations of barrier layer formation in the bay of bengal during summer monsoon

    Vinayachandran P., Murty V., Ramesh Babu V. : Observations of barrier layer formation in the bay of bengal during summer monsoon. Journal of Geophysical Research: Oceans 107, C12 (2002), SRF--19

    2002

  42. [50]

    N., Yamagata T

    Vinayachandran P. N., Yamagata T. : Monsoon response of the sea around Sri Lanka : generation of thermal domes and anticyclonic vortices. Journal of Physical Oceanography 28, 10 (1998), 1946--1960

    1998

  43. [51]

    : A survey on visual analysis of ocean data

    Xie C., Li M., Wang H., Dong J. : A survey on visual analysis of ocean data. Visual Informatics 3, 3 (2019), 113--128

    2019

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.