Skip to content
DraconiticResearch
Search Sources API
190,335 studies31 sourcesUpdated 28 min ago
Peer-reviewedCC BYScience Advances

A global internal tide modeling framework for improving satellite observations of fine-scale ocean circulation

  • Badarvada Yadidya1,2
  • Brian K. Arbic2,3
  • Edward D. Zaron4
  • Jay F. Shriver5
  • Maarten Cornelis Buijsman6
  • Eric P. Chassignet1
  • Loren Carrère7
  • Michel Tchilibou7
7 affiliations
  1. 1Center for Ocean-Atmospheric Prediction Studies, Florida State University, Tallahassee, FL, USA
  2. 2Department of Earth and Environmental Sciences, University of Michigan, Ann Arbor, MI, USA
  3. 3Research School of Earth Sciences, Australian National University, Canberra, ACT, Australia
  4. 4College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, Corvallis, OR, USA
  5. 5Naval Research Laboratory, Stennis Space Center, MS, USA
  6. 6Division of Marine Science, University of Southern Mississippi, Stennis Space Center, MS, USA
  7. 7Collecte Localisation Satellites, 31520 Ramonville-Saint-Agne, France
Open at sourcePDFDOICite

Abstract

Small-scale oceanic eddies and filaments mediate the vertical exchange of heat and carbon within the global ocean. The Surface Water and Ocean Topography (SWOT) mission resolves these features through wide-swath interferometry, but internal tides often mask these observations. Non-phase-locked internal tides present special difficulty because they vary with the evolving ocean background. We show that this chaotic variability can be predicted. We use a data-assimilative ocean forecast model to resolve the mesoscale environment and separate tidal signals from the broader circulation. The model captures the organized structure of these incoherent waves in the independent SWOT measurements. Correcting for the total (phase-locked plus non-phase-locked) internal tide signal reduces the error by 59% relative to current empirical methods. These findings show that non-phase-locked internal tides become predictable when the evolving ocean state is explicitly modeled. Our framework allows wide-swath altimetry to more accurately map climatically important fine-scale dynamics in the ocean.

Related

  1. Improving global precipitation forecasts with an AI weather model trained on satellite observations
    Julian F. Schmitt, Bertrand Delorme, Robert C. King +4
    arXivPreprintCC BY 4.0Full textAtmospheric science & meteorologyarXiv
  2. Satellite observations and energy balance constrain the thermal footprint of a hyperscale data-centre cluster
    Jiawei Jing
    Environmental Research LettersPeer-reviewedPublisher termsFull textEnvironmental science & pollutionIOP/AASCrossref
  3. Spatial variation of aerosol properties over Europe derived from satellite observations and comparison with model calculations
    Cristina Robles-Gonzalez, Martijn Schaap, G. de Leeuw +2
    HALPeer-reviewedLicence not stated35 citedClimate scienceOpenAlex
  4. Recurrent High-Resolution Satellite Observations Quantify Facility-Resolved Annual Large-Emitter Methane Emissions in the Permian Basin
    Dylan Jervis, Alessandro Ambler, Simon Andersson-Bastable +7
    EarthArXivPreprintPublisher termsFull textClimate scienceEarthArXiv
  5. Are surface temperature and chlorophyll in a large deep lake related? An analysis based on satellite observations in synergy with hydrodynamic modelling and in-situ data
    Damien Bouffard, Isabel Kiefer, Alfred Wüest +2
    Remote Sensing of EnvironmentPeer-reviewedLicence not statedFull text52 citedEnvironmental science & pollutionHAL

Cite

BibTeXRIS
BibTeX
@article{yadidya2026global,
  title = {{A global internal tide modeling framework for improving satellite observations of fine-scale ocean circulation}},
  author = {Badarvada Yadidya and Brian K. Arbic and Edward D. Zaron and Jay F. Shriver and Maarten Cornelis Buijsman and Eric P. Chassignet and Loren Carrère and Michel Tchilibou},
  journal = {Science Advances},
  year = {2026},
  month = jun,
  doi = {10.1126/sciadv.aee1885},
  url = {https://doi.org/10.1126/sciadv.aee1885},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - A global internal tide modeling framework for improving satellite observations of fine-scale ocean circulation
AU  - Badarvada Yadidya
AU  - Brian K. Arbic
AU  - Edward D. Zaron
AU  - Jay F. Shriver
AU  - Maarten Cornelis Buijsman
AU  - Eric P. Chassignet
AU  - Loren Carrère
AU  - Michel Tchilibou
PY  - 2026
DA  - 2026/06/03
JO  - Science Advances
DO  - 10.1126/sciadv.aee1885
UR  - https://doi.org/10.1126/sciadv.aee1885
AB  - Small-scale oceanic eddies and filaments mediate the vertical exchange of heat and carbon within the global ocean. The Surface Water and Ocean Topography (SWOT) mission resolves these features through wide-swath interferometry, but internal tides often mask these observations. Non-phase-locked internal tides present special difficulty because they vary with the evolving ocean background. We show that this chaotic variability can be predicted. We use a data-assimilative ocean forecast model to resolve the mesoscale environment and separate tidal signals from the broader circulation. The model captures the organized structure of these incoherent waves in the independent SWOT measurements. Correcting for the total (phase-locked plus non-phase-locked) internal tide signal reduces the error by 59% relative to current empirical methods. These findings show that non-phase-locked internal tides become predictable when the evolving ocean state is explicitly modeled. Our framework allows wide-swath altimetry to more accurately map climatically important fine-scale dynamics in the ocean.
SN  - 2375-2548
ER  -