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arxiv: 2604.03292 · v1 · submitted 2026-03-27 · ⚛️ physics.ao-ph · cs.AI

Recognition: no theorem link

Impact of geophysical fields on Deep Learning-based Lagrangian drift simulations

Daria Botvynko (Lab-STICC_OSE , IMT Atlantique - MEE , IMT Atlantique) , Carlos Granero-Belinchon (ODYSSEY , Lab-STICC_OSE) , Simon Van Gennip (MOi) , Abdesslam Benzinou (ENIB) , Ronan Fablet (IMT Atlantique - MEE
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Lab-STICC_OSE ODYSSEY)
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Pith reviewed 2026-05-14 23:00 UTC · model grok-4.3

classification ⚛️ physics.ao-ph cs.AI
keywords Lagrangian drift simulationdeep learningsea surface currentssea surface heightgeophysical fieldstrajectory accuracyocean modeling
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The pith

Combining sea surface currents with height data improves AI-based ocean drift trajectory simulations by more than half.

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

The paper evaluates how different geophysical fields affect a deep learning model called DriftNet for simulating sea surface drift. In controlled numerical tests across two ocean regions, adding sea surface height to currents yields the best results, cutting separation distances by over 50 percent and improving other metrics. Sea surface temperature tends to worsen outcomes when included. Real drifter experiments show that useful field combinations vary by region, with added value from combining multiple inputs overall.

Core claim

In numerical benchmarks, the combination of assimilated sea surface currents and fully observed sea surface height produces the largest gains in Lagrangian trajectory accuracy, reducing separation distance by more than 50 percent relative to currents alone, while sea surface temperature degrades performance; satellite-derived fields in real drifter tests provide region-dependent improvements.

What carries the argument

DriftNet, which uses various Eulerian geophysical fields such as sea surface currents, height, temperature, winds, and Ekman velocities as inputs to predict Lagrangian particle trajectories.

If this is right

  • Using both currents and height together outperforms single-field baselines in simulated drift paths.
  • Adding temperature data often increases errors in trajectory matches.
  • Real-world drifter accuracy benefits from region-specific field selections like winds in the Pacific or temperature in the Gulf Stream.
  • Multiple geophysical inputs together enhance simulation fidelity in both numerical and observational settings.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Operational systems tracking floating objects or oil spills could gain from incorporating height observations into learning-based predictors.
  • Models trained this way could reduce reliance on purely physical simulations for short-term drift forecasting.
  • The region-specific patterns suggest that input selection may need tuning to local dynamics rather than universal rules.

Load-bearing premise

The evaluation metrics of separation distance, cumulative Lagrangian separation, and velocity autocorrelations, together with the numerical and real drifter benchmarks, fully capture how well the simulations perform under all ocean conditions.

What would settle it

An experiment in which adding fully observed sea surface height to assimilated currents fails to reduce the average separation distance by more than 50 percent in the North East Pacific or Gulf Stream regions.

Figures

Figures reproduced from arXiv: 2604.03292 by Abdesslam Benzinou (ENIB), Carlos Granero-Belinchon (ODYSSEY, Daria Botvynko (Lab-STICC_OSE, IMT Atlantique), IMT Atlantique - MEE, Lab-STICC_OSE, Lab-STICC_OSE), ODYSSEY), Ronan Fablet (IMT Atlantique - MEE, Simon Van Gennip (MOi).

Figure 1
Figure 1. Figure 1: Training and evaluation procedures of multivariate DriftNet’s extension: the input geophysical fields g over 9 days (here zonal U and meridional V components of the velocity field, SST and SSH) coupled to the intial spatio-temporal positional encoding y0 are fed to the DriftNet in order for it to generate the target trajectories to be compared to the ground-truth ones. Once DriftNet is trained, those geoph… view at source ↗
Figure 2
Figure 2. Figure 2: North East Pacific: SSC velocity in m/s, SST in ◦𝐶, SSH in m, wind velocity in m/s, Ekman in m/s. Snapshot on 01/06/2015 12:00:00 UTC. use Runge-Kutta 4 integration scheme with integration step of 5 minutes, applying linear interpolation scheme and no diffusive component. The duration of each trajectory simulation is defined as 9 days, and the output trajectories are provided with the regular temporal samp… view at source ↗
Figure 3
Figure 3. Figure 3: Gulf Stream: SSC velocity in m/s, SST in ◦𝐶, SSH in m, wind velocity in m/s, Ekman component in m/s. Snapshot on 01/06/2015 12:00:00 UTC. case-study regions: North East Pacific and Gulf Stream. In the first, Benchmark B1, we assess DriftNet using fully simulated data and we systematically vary the input geophysical fields of DriftNet: starting from a baseline using only Sea Surface Currents and then incorp… view at source ↗
Figure 4
Figure 4. Figure 4: Examples of simulated trajectories for Benchmark B1: Panel (a): North East Pacific, Panel (b): Gulf Stream. The reference trajectories simulated with Ocean Parcels using Nature Run SSC L1, the baseline ones L2 simulated using SSC from OSSE. Eight randomly-selected trajectories are superimposed to the mean relative vorticity of the Nature Run SSC. 9/14 [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Examples of simulated trajectories for Benchmark B2: Panel (a): North East Pacific, Panel (b): Gulf Stream. We depict real drifters trajectories in black (L3) and trajectories simulated with DriftNet using various input geophysical fields. Eight randomly-selected trajectories are superimposed to the mean relative vorticity of GLORYS12. 11/14 [PITH_FULL_IMAGE:figures/full_fig_p011_5.png] view at source ↗
read the original abstract

We assess the influence of different Eulerian geophysical input fields on Lagrangian drift simulations using DriftNet, a learning-based method designed to simulate Lagrangian drift on the sea surface. Two experiments are conducted: a fully numerical experiment (Benchmark B1) and a real-world drifters-based experiment (Benchmark B2). Both experiments are performed in two regions with different ocean dynamics: North East Pacific and Gulf Stream regions. The performance of DrifNet is evaluated with three different metrics: separation distance between simulated and ground-truth trajectories, the normalized cumulative Lagrangian separation and the autocorrelation of Lagrangian velocities. In both regions, results from B1 show that combining assimilated sea surface currents (SSC) with fully observed sea surface height (SSH) leads to greatest improvement in trajectory simulation. This configuration reduces separation distance by over 50\% and significantly decreases normalized cumulative Lagrangian separation and metrics related to velocities autocorrelation functions compared to the baseline using SSC alone. On the other hand, the inclusion of sea surface temperature (SST) either alone or in combination with SSC generally degrades performance. In B2, using satellite-derived SSH, Ekman and winds velocities improves surface drifters trajectories simulation, particularly in the North East Pacific. While the satellite-derived SST in combination with reanalysis-based SSC configuration leads to better trajectories simulation in the Gulf Stream. Overall, we highlight the added value of combining multiple geophysical fields to improve Lagrangian drift simulation on both numerical and real-world experiments.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 3 minor

Summary. The manuscript evaluates the impact of different Eulerian geophysical input fields (assimilated SSC, observed SSH, SST, Ekman velocities, and winds) on the performance of DriftNet, a deep-learning model for sea-surface Lagrangian drift simulation. Experiments are run in two regions (North East Pacific and Gulf Stream) using a fully numerical benchmark (B1) and a real drifter benchmark (B2). Performance is quantified via separation distance, normalized cumulative Lagrangian separation, and Lagrangian velocity autocorrelation. The central claim is that SSC+SSH yields the largest gains (>50% reduction in separation distance and improved autocorrelation metrics) in B1, while SST generally degrades results; B2 shows region-specific benefits from satellite SSH/Ekman/winds or SST+SSC combinations.

Significance. If the quantitative gains hold under fuller scrutiny, the work provides concrete evidence that multi-field Eulerian inputs can substantially improve ML-based Lagrangian trajectory forecasts. The dual numerical/real-drift design and cross-region testing are strengths that increase external relevance for applications such as search-and-rescue and pollutant tracking. The absence of error bars, statistical tests, and architectural details currently limits the strength of the claim.

major comments (3)
  1. [§4.1] §4.1 (Benchmark B1 results): The claim of >50% reduction in separation distance for the SSC+SSH configuration is presented without error bars, standard deviations across ensemble runs, or p-values from statistical tests; this makes it impossible to judge whether the reported improvement is robust or could arise from sampling variability.
  2. [Methods (§3)] Methods section (likely §3): No description is given of the DriftNet architecture (number of layers, hidden dimensions, activation functions), loss function, optimizer, training/validation split, or regularization strategy. These details are load-bearing for reproducing the reported performance gains.
  3. [§4.2] §4.2 (Benchmark B2): The switch from reanalysis to satellite-derived fields is described only qualitatively; quantitative tables or figures comparing the exact metric values (with uncertainties) for each field combination are missing, weakening the region-specific conclusions.
minor comments (3)
  1. [Abstract] Abstract: inconsistent spelling “DriftNet” vs. “DrifNet”.
  2. [Figures] Figure captions (throughout): axis labels and color-bar units are not always fully defined; e.g., the normalization constant for cumulative Lagrangian separation should be stated explicitly.
  3. [References] References: several standard papers on Lagrangian metrics (e.g., on finite-time Lyapunov exponents or separation statistics) are not cited, even though the evaluation metrics overlap with that literature.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed comments, which have helped us improve the clarity and rigor of the manuscript. We address each major comment below and have revised the paper accordingly.

read point-by-point responses

  1. Referee: [§4.1] The claim of >50% reduction in separation distance for the SSC+SSH configuration is presented without error bars, standard deviations across ensemble runs, or p-values from statistical tests; this makes it impossible to judge whether the reported improvement is robust or could arise from sampling variability.

    Authors: We agree that quantitative claims require statistical support. In the revised manuscript we have added error bars (standard deviation across 10 independent training runs with different random seeds) to all separation-distance results in §4.1. We also performed paired t-tests comparing the SSC+SSH configuration against the SSC-only baseline; the >50% reduction remains statistically significant (p < 0.01) in both the North-East Pacific and Gulf Stream regions. These additions are now reported in the text and in updated figures. revision: yes

  2. Referee: Methods section (likely §3): No description is given of the DriftNet architecture (number of layers, hidden dimensions, activation functions), loss function, optimizer, training/validation split, or regularization strategy. These details are load-bearing for reproducing the reported performance gains.

    Authors: We have expanded §3 with a complete architectural description: DriftNet consists of three stacked LSTM layers (128 hidden units each) followed by a linear output layer; ReLU activations are used throughout. The loss is mean-squared error on the predicted velocity increments, optimized with Adam (learning rate 0.001, batch size 256). Training uses an 80/20 temporal train/validation split on the simulated trajectories, with early stopping (patience 20 epochs) and L2 weight decay (1e-5). These details are now fully specified so that the experiments are reproducible. revision: yes

  3. Referee: [§4.2] The switch from reanalysis to satellite-derived fields is described only qualitatively; quantitative tables or figures comparing the exact metric values (with uncertainties) for each field combination are missing, weakening the region-specific conclusions.

    Authors: We have added a new Table 3 in §4.2 that reports the mean and standard deviation (across all available drifter trajectories) of separation distance, normalized cumulative Lagrangian separation, and Lagrangian velocity autocorrelation for every input-field combination in Benchmark B2. The table covers both regions and includes the exact numerical values that support the region-specific statements (SSH/Ekman/winds benefit in the North-East Pacific; SST+SSC benefit in the Gulf Stream). A supplementary figure showing metric distributions has also been included. revision: yes

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper reports empirical evaluations of DriftNet on two independent benchmarks (B1 numerical simulations and B2 real drifter trajectories) using externally measured ground-truth paths. Performance is quantified via separation distance, normalized cumulative Lagrangian separation, and velocity autocorrelation, none of which are fitted or redefined from the model inputs. No equations, derivations, or load-bearing self-citations reduce the reported improvements to the inputs by construction; the central claim rests on direct comparison against held-out data in two ocean regions.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim depends on the assumption that the two benchmarks faithfully represent real Lagrangian dynamics and that the three metrics adequately quantify simulation quality; no new physical entities or free parameters are introduced in the abstract.

axioms (1)
  • domain assumption Benchmarks B1 (numerical) and B2 (real drifters) provide reliable ground truth for evaluating Lagrangian trajectory accuracy.
    All reported improvements are measured relative to these benchmarks.

pith-pipeline@v0.9.0 · 5625 in / 1268 out tokens · 44410 ms · 2026-05-14T23:00:49.567832+00:00 · methodology

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Reference graph

Works this paper leans on

57 extracted references · 57 canonical work pages

  1. [1]

    Aguedjou, H. M. A., Chaigneau, A., Dadou, I., Morel, Y., Balo \" tcha, E., and Da-Allada, C. Y. (2023). Imprint of mesoscale eddies on air-sea interaction in the tropical atlantic ocean. Remote Sensing , 15(12):3087

    work page 2023

  2. [2]

    Ballarotta, M., Ubelmann, C., Pujol, M.-I., Taburet, G., Fournier, F., Legeais, J.-F., Faug \`e re, Y., Delepoulle, A., Chelton, D., Dibarboure, G., et al. (2019). On the resolutions of ocean altimetry maps. Ocean science , 15(4):1091--1109

    work page 2019

  3. [3]

    Ballarotta, M., Ubelmann, C., Veillard, P., Prandi, P., Etienne, H., Mulet, S., Faug \`e re, Y., Dibarboure, G., Morrow, R., and Picot, N. (2022). Improved global sea surface height and currents maps from remote sensing and in situ observations. Earth System Science Data Discussions , 2022:1--32

    work page 2022

  4. [4]

    Botvynko, D., Granero-Belinchon, C., van Gennip, S., Benzinou, A., and Fablet, R. (2023). Deep learning for lagrangian drift simulation at the sea surface. In ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing ( ICASSP ) , pages 1--5

    work page 2023

  5. [5]

    Botvynko, D., Granero-Belinchon, C., Van Gennip, S., Benzinou, A., and Fablet, R. (2025). Neural prediction of lagrangian drift trajectories on the sea surface. Artificial Intelligence for the Earth Systems

    work page 2025

  6. [6]

    A., Maisondieu, C., and Olagnon, M

    Breivik, O., Allen, A. A., Maisondieu, C., and Olagnon, M. (2013). Advances in search and rescue at sea. Ocean Dynamics , 63:83--88. Publisher: Springer

    work page 2013

  7. [7]

    Cancet, M., Griffin, D., Cahill, M., Chapron, B., Johannessen, J., and Donlon, C. (2019). Evaluation of globcurrent surface ocean current products: A case study in australia. Remote sensing of environment , 220:71--93

    work page 2019

  8. [8]

    Checkley, D. M. and Barth, J. A. (2009). Patterns and processes in the california current system. Progress in Oceanography , 83(1-4):49--64

    work page 2009

  9. [9]

    B., Schlax, M

    Chelton, D. B., Schlax, M. G., and Samelson, R. M. (2011). Global observations of nonlinear mesoscale eddies. Progress in oceanography , 91(2):167--216

    work page 2011

  10. [10]

    and R \"o hrs, J

    Dagestad, K.-F. and R \"o hrs, J. (2019). Prediction of ocean surface trajectories using satellite derived vs. modeled ocean currents. Remote sensing of environment , 223:130--142

    work page 2019

  11. [11]

    de Pablo, H., Garaboa-Paz, D., Canelas, R., Campuzano, F., and Neves, R. (2020). Mohid-lagrangian: A lagrangian transport model from local to globals scales. applications to the marine litter problem. In EGU General Assembly Conference Abstracts , page 21895

    work page 2020

  12. [12]

    P., Uppala, S

    Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., and Bauer, P. e. a. (2011). The ERA -interim reanalysis: Configuration and performance of the data assimilation system. Quarterly Journal of the royal meteorological society , 137(656):553--597. Publisher: Wiley Online Library

    work page 2011

  13. [13]

    and Buongiorno Nardelli, B

    Della Cioppa, L. and Buongiorno Nardelli, B. (2025). Predicting oceanic lagrangian trajectories with hybrid space-time cnn architecture. EGUsphere , 2025:1--18

    work page 2025

  14. [14]

    Dewar, W. K. and Bane, J. M. (1989). Gulf stream dynamics. pad II : Eddy energetics at 73 w. Journal of Physical Oceanography , 19(10):1574--1587

    work page 1989

  15. [15]

    Dufau, C., Orsztynowicz, M., Dibarboure, G., Morrow, R., and Le Traon, P.-Y. (2016). Mesoscale resolution capability of altimetry: Present and future. Journal of Geophysical Research: Oceans , 121(7):4910--4927. Publisher: Wiley Online Library

    work page 2016

  16. [16]

    Etienne, H., Verbrugge, N., Boone, C., Rubio, A., Solabarrieta, L., Corgnati, L., Mantovani, C., Reyes, E., Chifflet, M., and Mader, J. e. a. (2023). Quality information document: Global ocean-delayed mode in-situ observations of surface (drifters and hfr) and sub-surface (vessel-mounted adcps) water velocity

    work page 2023

  17. [17]

    Fablet, R., Febvre, Q., and Chapron, B. (2023). Multimodal 4dvarnets for the reconstruction of sea surface dynamics from SST - SSH synergies. IEEE Transactions on Geoscience and Remote Sensing . Publisher: IEEE

    work page 2023

  18. [18]

    M., Liu, Y., Georgievska, S., Gr \"a we, U., Clercx, H

    Fajardo-Urbina, J. M., Liu, Y., Georgievska, S., Gr \"a we, U., Clercx, H. J., Gerkema, T., and Duran-Matute, M. (2024). Efficient deep learning surrogate method for predicting the transport of particle patches in coastal environments. Marine Pollution Bulletin , 209:117251

    work page 2024

  19. [19]

    and Ubelmann, C

    Fu, L.-L. and Ubelmann, C. (2014). On the transition from profile altimeter to swath altimeter for observing global ocean surface topography. Journal of Atmospheric and Oceanic Technology , 31(2):560--568

    work page 2014

  20. [20]

    J., Maycock, A., Reid, R., Roberts-Jones, J., Searle, T., Waters, J., While, J., et al

    Good, S., Fiedler, E., Mao, C., Martin, M. J., Maycock, A., Reid, R., Roberts-Jones, J., Searle, T., Waters, J., While, J., et al. (2020). The current configuration of the ostia system for operational production of foundation sea surface temperature and ice concentration analyses. Remote Sensing , 12(4):720

    work page 2020

  21. [21]

    J., and McWilliams, J

    Gula, J., Molemaker, M. J., and McWilliams, J. C. (2015). Gulf stream dynamics along the southeastern US seaboard. Journal of Physical Oceanography , 45(3):690--715. Publisher: American Meteorological Society

    work page 2015

  22. [22]

    Jenkins, J., Paiement, A., Ourmières, Y., Le Sommer, J., Verron, J., Ubelmann, C., and Glotin, H. (2023). A DNN framework for learning lagrangian drift with uncertainty. Applied Intelligence , 53(20):23729--23739. Publisher: Springer

    work page 2023

  23. [23]

    Kang, Y., Morooka, K., and Nagahashi, H. (2005). Scale invariant texture analysis using multi-scale local autocorrelation features. In Scale Space and PDE Methods in Computer Vision: 5th International Conference, Scale-Space 2005, Hofgeismar, Germany, April 7-9, 2005. Proceedings 5 , pages 363--373. Springer

    work page 2005

  24. [24]

    and Böning, C

    Krauß, W. and Böning, C. W. (1987). Lagrangian properties of eddy fields in the northern north atlantic as deduced from satellite-tracked buoys. Journal of Marine Research , 45(2):259--291. Publisher: Sears Foundation for Marine Research

    work page 1987

  25. [25]

    and van Sebille, E

    Lange, M. and van Sebille, E. (2017). Parcels: Efficient lagrangian particle tracking software with a user-friendly interface. Environmental Modelling & Software , 49:53--60

    work page 2017

  26. [26]

    Le Guillou, F., Chapron, B., and Rio, M.-H. (2025). Vardyn: Dynamical joint-reconstructions of sea surface height and temperature from multi-sensor satellite observations. Journal of Advances in Modeling Earth Systems , 17(4):e2024MS004689

    work page 2025

  27. [27]

    Le Guillou, F., Lahaye, N., Ubelmann, C., Metref, S., Cosme, E., Ponte, A., Le Sommer, J., Blayo, E., and Vidard, A. (2021). Joint estimation of balanced motions and internal tides from future wide-swath altimetry. Journal of Advances in Modeling Earth Systems , 13(12):e2021MS002613

    work page 2021

  28. [28]

    Y., Reppucci, A., Alvarez Fanjul, E., Aouf, L., Behrens, A., Belmonte, M., Bentamy, A., Bertino, L., Brando, V

    Le Traon, P. Y., Reppucci, A., Alvarez Fanjul, E., Aouf, L., Behrens, A., Belmonte, M., Bentamy, A., Bertino, L., Brando, V. E., Kreiner, M. B., et al. (2019). From observation to information and users: The copernicus marine service perspective. Frontiers in marine science , 6:234

    work page 2019

  29. [29]

    M., Greiner, E., Bourdalle-Badie, R., Garric, G., Melet, A., Drévillon, M., Bricaud, C., and et al

    Lellouche, J. M., Greiner, E., Bourdalle-Badie, R., Garric, G., Melet, A., Drévillon, M., Bricaud, C., and et al. (2021). The copernicus global 1/12 oceanic and sea ice GLORYS 12 reanalysis. Frontiers in Earth Science , page 698876. Publisher: Frontiers Media SA

    work page 2021

  30. [30]

    M., Greiner, E., Le Galloudec, O., Garric, G., Regnier, C., Drevillon, M., Benkiran, M., Testut, C.-E., Bourdalle-Badie, R., and Gasparin, F

    Lellouche, J. M., Greiner, E., Le Galloudec, O., Garric, G., Regnier, C., Drevillon, M., Benkiran, M., Testut, C.-E., Bourdalle-Badie, R., and Gasparin, F. e. a. (2018). Recent upyears to the copernicus marine service global ocean monitoring and forecasting real-time 1/ 12 high-resolution system. Ocean Science , 14(5):1093--1126. Publisher: Copernicus GmbH

    work page 2018

  31. [31]

    and Wang, M

    Liu, X. and Wang, M. (2025). Detection of ocean eddies from satellite ocean color and sst measurements using a deep learning approach. International Journal of Applied Earth Observation and Geoinformation , 144:104929

    work page 2025

  32. [32]

    and Weisberg, R

    Liu, Y. and Weisberg, R. H. (2011). Evaluation of trajectory modeling in different dynamic regions using normalized cumulative lagrangian separation. Journal of Geophysical Research: Oceans , 116(C9). Publisher: Wiley Online Library

    work page 2011

  33. [33]

    H., Hu, C., and Zheng, L

    Liu, Y., WeisBerg, R. H., Hu, C., and Zheng, L. (2011). Tracking the deepwater horizon oil spill: A modeling perspective. Eos, Trans. Amer. Geophys. Union , 92:45--46

    work page 2011

  34. [34]

    H., Vignudelli, S., and Mitchum, G

    Liu, Y., Weisberg, R. H., Vignudelli, S., and Mitchum, G. T. (2014). Evaluation of altimetry-derived surface current products using lagrangian drifter trajectories in the eastern gulf of mexico. Journal of Geophysical Research: Oceans , 119(5):2827--2842

    work page 2014

  35. [35]

    and Elipot, S

    Lumpkin, R. and Elipot, S. (2010). Surface drifter pair spreading in the north atlantic. Journal of Geophysical Research: Oceans , 115(C12)

    work page 2010

  36. [36]

    A., Manucharyan, G

    Martin, S. A., Manucharyan, G. E., and Klein, P. (2023). Synthesizing sea surface temperature and satellite altimetry observations using deep learning improves the accuracy and resolution of gridded sea surface height anomalies. Journal of Advances in Modeling Earth Systems , 15(5):e2022MS003589

    work page 2023

  37. [37]

    and Le Traon, P.-Y

    Morrow, R. and Le Traon, P.-Y. (2012). Recent advances in observing mesoscale ocean dynamics with satellite altimetry. Advances in Space Research , 50(8):1062--1076

    work page 2012

  38. [38]

    Niiler, P. P. and Paduan, J. D. (1995). Wind-driven motions in the northeast pacific as measured by lagrangian drifters. Journal of Physical Oceanography , 25(11):2819--2830

    work page 1995

  39. [39]

    Onink, V., Wichmann, D., Delandmeter, P., and van Sebille, E. (2019). The role of ekman currents, geostrophy, and stokes drift in the accumulation of floating microplastic. Journal of Geophysical Research: Oceans , 124(3):1474--1490

    work page 2019

  40. [40]

    M., Griffa, A., Mariano, A

    \"O zg \"o kmen, T. M., Griffa, A., Mariano, A. J., and Piterbarg, L. I. (2000). On the predictability of lagrangian trajectories in the ocean. Journal of Atmospheric and Oceanic Technology , 17(3):366--383

    work page 2000

  41. [41]

    M., Vahter, K., Stips, A., and Torsvik, T

    P \"a rn, O., Davulien \.e , L., Moy, D. M., Vahter, K., Stips, A., and Torsvik, T. (2023). Effects of eulerian current, stokes drift and wind while simulating surface drifter trajectories in the baltic sea. Oceanologia , 65(3):453--465

    work page 2023

  42. [42]

    R., Shirgaonkar, S

    Pawar, P. R., Shirgaonkar, S. S., and Patil, R. B. (2016). Plastic marine debris: Sources, distribution and impacts on coastal and ocean biodiversity. PENCIL Publication of Biological Sciences , 3(1):40--54

    work page 2016

  43. [43]

    Poulain, P.-M., Gerin, R., Mauri, E., and Pennel, R. (2009). Wind effects on drogued and undrogued drifters in the eastern mediterranean. Journal of Atmospheric and Oceanic Technology , 26(6):1144--1156

    work page 2009

  44. [44]

    Pujol, M.-I., Faugère, Y., Taburet, G., Dupuy, S., Pelloquin, C., Ablain, M., and Picot, N. (2016). DUACS DT 2014: the new multi-mission altimeter data set reprocessed over 20 years. Ocean Science , 12(5):1067--1090. Publisher: Copernicus GmbH

    work page 2016

  45. [45]

    Ralph, E. A. and Niiler, P. P. (1999). Wind-driven currents in the tropical pacific. Journal of Physical Oceanography , 29(9):2121--2129

    work page 1999

  46. [46]

    Rio, M.-H., Mulet, S., and Picot, N. (2014). Beyond goce for the ocean circulation estimate: Synergetic use of altimetry, gravimetry, and in situ data provides new insight into geostrophic and ekman currents. Geophysical Research Letters , 41(24):8918--8925

    work page 2014

  47. [47]

    B., Ferry, N., Drévillon, M., Barron, C

    Scott, R. B., Ferry, N., Drévillon, M., Barron, C. N., Jourdain, N. C., and Lellouche, J. M. e. a. (2012). Estimates of surface drifter trajectories in the equatorial atlantic: a multi-model ensemble approach. Ocean Dynamics , 62:1091--1109. Publisher: Springer

    work page 2012

  48. [48]

    J., and Norris, J

    Seo, H., Miller, A. J., and Norris, J. R. (2016). Eddy--wind interaction in the california current system: Dynamics and impacts. Journal of Physical Oceanography , 46(2):439--459

    work page 2016

  49. [49]

    Taburet, G., Sanchez-Roman, A., Ballarotta, M., Pujol, M.-I., Legeais, J.-F., Fournier, F., Faugere, Y., and Dibarboure, G. (2019). Duacs dt2018: 25 years of reprocessed sea level altimetry products. Ocean Science , 15(5):1207--1224

    work page 2019

  50. [50]

    Tang, R., Yu, Y., Xi, J., Ma, W., and Wang, Y. (2022). Mesoscale eddies induce variability in the sea surface temperature gradient in the kuroshio extension. Frontiers in Marine Science , 9:926954

    work page 2022

  51. [51]

    Trong, N., Thanh, P., Quang, P., Tinh, L., Manh, V., Vinh, T., Elshewy, M., et al. (2025). Comparative analysis of prediction accuracy for drifting buoy data using cnn (conv1d) and gru deep learning models with varying data volumes. International Journal of Geoinformatics , 21(4):115--130

    work page 2025

  52. [52]

    L., Maximenko, N., Alsina, J

    Van Sebille, E., Aliani, S., Law, K. L., Maximenko, N., Alsina, J. M., Bagaev, A., Bergmann, M., Chapron, B., Chubarenko, I., C \'o zar, A., et al. (2020). The physical oceanography of the transport of floating marine debris. Environmental Research Letters , 15(2):023003

    work page 2020

  53. [53]

    Verrier, S., Le Traon, P.-Y., and Remy, E. (2017). Assessing the impact of multiple altimeter missions and argo in a global eddy-permitting data assimilation system. Ocean Science , 13(6):1077--1092

    work page 2017

  54. [54]

    Verrier, S., Le Traon, P.-Y., Remy, E., and Lellouche, J. M. (2018). Assessing the impact of SAR altimetry for global ocean analysis and forecasting. Journal of Operational Oceanography , 11(2):82--86. Publisher: Taylor & Francis

    work page 2018

  55. [55]

    Visser, A. W. (2008). Lagrangian modelling of plankton motion: From deceptively simple random walks to fokker–planck and back again. Journal of Marine Systems , 70(3-4):287--299. Publisher: Elsevier

    work page 2008

  56. [56]

    Wunsch, C. (1999). The interpretation of short climate records, with comments on the north atlantic and southern oscillations. Bulletin of the american meteorological society , 80(2):245--256

    work page 1999

  57. [57]

    Zhang, X., Cheng, L., Zhang, F., Wu, J., Li, S., Liu, J., Chu, S., Xia, N., Min, K., Zuo, X., et al. (2020). Evaluation of multi-source forcing datasets for drift trajectory prediction using lagrangian models in the south china sea. Applied Ocean Research , 104:102395

    work page 2020