Articles | Volume 11, issue 3
https://doi.org/10.5194/os-11-425-2015
https://doi.org/10.5194/os-11-425-2015
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, J.-M. Brankart, and P. Brasseur

Related authors

A generic approach to explicit simulation of uncertainty in the NEMO ocean model
J.-M. Brankart, G. Candille, F. Garnier, C. Calone, A. Melet, P.-A. Bouttier, P. Brasseur, and J. Verron
Geosci. Model Dev., 8, 1285–1297, https://doi.org/10.5194/gmd-8-1285-2015,https://doi.org/10.5194/gmd-8-1285-2015, 2015
Short summary

Cited articles

Anderson, J.: A method for producing and evaluating probabilistic forecasts from ensemble model integrations, J. Climate, 9, 1518–1530, 1996.
Bishop, H. C., Etherton, B. J., and Majumdar, S. J.: Adaptive sampling with the Ensemble Transform Kalman Filter. Part I: theoretical aspects, Mon. Weather Rev., 129, 420–436, 2001.
Bouttier, P.-A., Blayo, E., Brankart, J.-M., Brasseur, P., Cosme, E., Verron, J., and Vidard, A.: Toward a data assimilation system for NEMO, Merc. Quart. Newsl., 46, 24–30, 2012.
Brankart, J.-M.: Impact of uncertainties in the horizontal density gradient upon low resolution global ocean model, Ocean Model., 66, 64–76, 2013.
Download
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.