Articles | Volume 11, issue 3
https://doi.org/10.5194/os-11-425-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/os-11-425-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
04 Jun 2015
Research article |
| 04 Jun 2015
Assessment of an ensemble system that assimilates Jason-1/Envisat altimeter data in a probabilistic model of the North Atlantic ocean circulation
G. Candille
CORRESPONDING AUTHOR
CNRS, LGGE, 38041 Grenoble, France
J.-M. Brankart
CNRS, LGGE, 38041 Grenoble, France
P. Brasseur
CNRS, LGGE, 38041 Grenoble, France
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Cited
13 citations as recorded by crossref.
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- Ocean Reanalyses: Recent Advances and Unsolved Challenges A. Storto et al. https://doi.org/10.3389/fmars.2019.00418
- Assessment of a regional physical–biogeochemical stochastic ocean model. Part 2: Empirical consistency V. Vervatis et al. https://doi.org/10.1016/j.ocemod.2021.101770
- Assimilation of chlorophyll data into a stochastic ensemble simulation for the North Atlantic Ocean Y. Santana-Falcón et al. https://doi.org/10.5194/os-16-1297-2020
- Ensemble quantification of short-term predictability of the ocean dynamics at a kilometric-scale resolution: a Western Mediterranean test case S. Leroux et al. https://doi.org/10.5194/os-18-1619-2022
- Uncertainty and scale interactions in ocean ensembles: From seasonal forecasts to multidecadal climate predictions L. Zanna et al. https://doi.org/10.1002/qj.3397
- CrocO_v1.0: a particle filter to assimilate snowpack observations in a spatialised framework B. Cluzet et al. https://doi.org/10.5194/gmd-14-1595-2021
- Arctic sea ice predictability on daily-to-weekly timescales: sensitivity to initial positional errors under different rheology formulations L. Fiol et al. https://doi.org/10.5194/tc-20-4655-2026
- Assimilation of satellite swaths versus daily means of sea ice concentration in a regional coupled ocean–sea ice model M. Durán Moro et al. https://doi.org/10.5194/tc-18-1597-2024
- An Efficient Way to Account for Observation Error Correlations in the Assimilation of Data from the Future SWOT High-Resolution Altimeter Mission G. Ruggiero et al. https://doi.org/10.1175/JTECH-D-16-0048.1
- Assessment of a regional physical–biogeochemical stochastic ocean model. Part 1: Ensemble generation V. Vervatis et al. https://doi.org/10.1016/j.ocemod.2021.101781
- Stochastic parameterizations of biogeochemical uncertainties in a 1/4° NEMO/PISCES model for probabilistic comparisons with ocean color data F. Garnier et al. https://doi.org/10.1016/j.jmarsys.2015.10.012
- Development of a probabilistic ocean modelling system based on NEMO 3.5: application at eddying resolution L. Bessières et al. https://doi.org/10.5194/gmd-10-1091-2017
13 citations as recorded by crossref.
- Ensemble analysis and forecast of ecosystem indicators in the North Atlantic using ocean colour observations and prior statistics from a stochastic NEMO–PISCES simulator M. Popov et al. https://doi.org/10.5194/os-20-155-2024
- Ocean Reanalyses: Recent Advances and Unsolved Challenges A. Storto et al. https://doi.org/10.3389/fmars.2019.00418
- Assessment of a regional physical–biogeochemical stochastic ocean model. Part 2: Empirical consistency V. Vervatis et al. https://doi.org/10.1016/j.ocemod.2021.101770
- Assimilation of chlorophyll data into a stochastic ensemble simulation for the North Atlantic Ocean Y. Santana-Falcón et al. https://doi.org/10.5194/os-16-1297-2020
- Ensemble quantification of short-term predictability of the ocean dynamics at a kilometric-scale resolution: a Western Mediterranean test case S. Leroux et al. https://doi.org/10.5194/os-18-1619-2022
- Uncertainty and scale interactions in ocean ensembles: From seasonal forecasts to multidecadal climate predictions L. Zanna et al. https://doi.org/10.1002/qj.3397
- CrocO_v1.0: a particle filter to assimilate snowpack observations in a spatialised framework B. Cluzet et al. https://doi.org/10.5194/gmd-14-1595-2021
- Arctic sea ice predictability on daily-to-weekly timescales: sensitivity to initial positional errors under different rheology formulations L. Fiol et al. https://doi.org/10.5194/tc-20-4655-2026
- Assimilation of satellite swaths versus daily means of sea ice concentration in a regional coupled ocean–sea ice model M. Durán Moro et al. https://doi.org/10.5194/tc-18-1597-2024
- An Efficient Way to Account for Observation Error Correlations in the Assimilation of Data from the Future SWOT High-Resolution Altimeter Mission G. Ruggiero et al. https://doi.org/10.1175/JTECH-D-16-0048.1
- Assessment of a regional physical–biogeochemical stochastic ocean model. Part 1: Ensemble generation V. Vervatis et al. https://doi.org/10.1016/j.ocemod.2021.101781
- Stochastic parameterizations of biogeochemical uncertainties in a 1/4° NEMO/PISCES model for probabilistic comparisons with ocean color data F. Garnier et al. https://doi.org/10.1016/j.jmarsys.2015.10.012
- Development of a probabilistic ocean modelling system based on NEMO 3.5: application at eddying resolution L. Bessières et al. https://doi.org/10.5194/gmd-10-1091-2017
Saved (final revised paper)
Latest update: 22 Sep 2026
Short summary
A realistic ocean circulation model is adapted to explicitly simulate model uncertainties and an ensemble data assimilation -stochastic perturbations, altimetric data and 4-D observation operator- is developed in order to control the Gulf Stream dynamic. The performance of the ensemble system is measured through probabilistic approach; the update then adjusts the bias and the dispersion of the ensemble (reliability) and reduces the uncertainty by 30% (resolution) for the SSH variable.
A realistic ocean circulation model is adapted to explicitly simulate model uncertainties and an...
