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Assessment of an Ensemble System That Assimilates Jason-1/Envisat Altimeter Data in a Probabilistic Model of the North Atlantic Ocean Circulation : Volume 11, Issue 6 (04/12/2014)

By Candille, G.

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Book Id: WPLBN0004020818
Format Type: PDF Article :
File Size: Pages 44
Reproduction Date: 2015

Title: Assessment of an Ensemble System That Assimilates Jason-1/Envisat Altimeter Data in a Probabilistic Model of the North Atlantic Ocean Circulation : Volume 11, Issue 6 (04/12/2014)  
Author: Candille, G.
Volume: Vol. 11, Issue 6
Language: English
Subject: Science, Ocean, Science
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus GmbH
Historic
Publication Date:
2014
Publisher: Copernicus Gmbh, Göttingen, Germany

Citation

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Brankart, J. M., Brasseur, P., & Candille, G. (2014). Assessment of an Ensemble System That Assimilates Jason-1/Envisat Altimeter Data in a Probabilistic Model of the North Atlantic Ocean Circulation : Volume 11, Issue 6 (04/12/2014). Retrieved from http://www.ebooklibrary.org/


Description
Description: CNRS, LGGE, 38041 Grenoble, France. A realistic circulation model of the North Atlantic ocean at 1/4° resolution (NATL025 NEMO configuration) has been adapted to explicitly simulate model uncertainties. This is achieved by introducing stochastic perturbations in the equation of state to represent the effect of unresolved scales on the model dynamics. The main motivation for this work is to develop ensemble data assimilation methods, assimilating altimetric data from past missions JASON-1 and ENVISAT. The assimilation experiment is designed to better control the Gulf Stream circulation for years 2005/06, focusing on frontal regions which are predominantly affected by unresolved dynamical scales. An ensemble based on such stochastic perturbations is first produced and evaluated using along-track altimetry observations. The Incremental Analysis Update (IAU) scheme is applied in order to obtain an ensemble of continuous trajectories all over the 2005/06 assimilation period. These three elements – stochastic parameterization, ensemble simulation and 4-D observation operator – are then used together to perform a 4-D analysis of along-track altimetry over 10 day windows. Finally, the results of this experiment are objectively evaluated using the standard probabilistic approach developed for meteorological applications (Toth et al., 2003; Candille et al., 2007).

The results show that the free ensemble – before starting the assimilation process – correctly reproduces the statistical variability over the Gulf Stream area: the system is then pretty reliable but not informative (null probabilistic resolution). Updating the free ensemble with altimetric data leads to a better reliability with an information gain around 30% (for 10 day forecasts of the SSH variable). Diagnoses on fully independent data (i.e. data that are not assimilated, like temperature and salinity profiles) provide more contrasted results when the free and updated ensembles are compared.


Summary
Assessment of an ensemble system that assimilates Jason-1/Envisat altimeter data in a probabilistic model of the North Atlantic ocean circulation

Excerpt
Anderson, J.: A method for producing and evaluating probabilistic forecasts from ensemble model integrations, J. Climate, 9, 1518–1530, 1996.; Barnier, B., G., Madec, T., Penduff, J.-M., Molines, A.-M., Treguier, J., Le Sommer, A., Beckmann, A., Biastoch, C., Böning, J., Dengg, C., Derval, E., Durand, Gulev, S., Remy, E., Talandier, C., Theetten, S., Maltrud, M., McClean, J., and DeCuevas, B.: Impact of partial steps and momentum advection schemes in a global ocean circulation model at eddy permitting resolution, Ocean Dynam., 56, 543–567, 2006.; 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.; Brasseur, P. and Verron, J.: The SEEK filter method for data assimilation in oceanography: a synthesis, Ocean Dynam., 56, 650–661, 2006.; Brankart, J.-M., Cosme, E., Testut, C.-E., Brasseur, P., and Verron, J.: Efficient local error parameterization for square root or ensemble Kalman filters: application to a basin-scale ocean turbulent flow, Mon. Weather Rev., 139, 474–493, 2011.; Brankart, J.-M., Testut, C.-E., Béal, D., Doron, M., Fontana, C., Meinvielle, M., Brasseur, P., and Verron, J.: Towards an improved description of ocean uncertainties: effect of local anamorphic transformations on spatial correlations, Ocean Sci., 8, 121–142, doi:10.5194/os-8-121-2012, 2012.; Buizza, R., Miller, M., and Palmer, T. N.: Stochastic representation of model uncertainties in the ECMWF ensemble prediction system, Q. J. Roy. Meteor. Soc., 125, 2887–2908, 1999.; Burgers, G., van Leeuwen, P. J., and Evensen, G.: Analysis scheme in the ensemble Kalman filter, Mon. Weather Rev., 126, 1719–1724, 1998.; Candille, G. and Talagrand, O.: Evaluation of probabilistic prediction systems for a scalar variable, Q. J. Roy. Meteor. Soc., 131, 2131–2150, 2005.; Candille, G., Côté, C., Houtekamer, P. L., and Pellerin, G.: Verification of an ensemble prediction system against observations, Mon. Weather Rev., 135, 2688–2699, 2007.; Cooper, M. and Haines, K.: Altimetric assimilation with water property conservation, J. Geophys. Res., 101, 1059–1077, 1996.; Dee, D. P.: On-line estimation of error covariance parameters for atmospheric data assimilation, Mon. Weather Rev., 123, 1128–1145, 1995.; Evensen, G.: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte-Carlo methods to forecast error statistics, J. Geophys. Res., 99, 10143–10162, 1994.; Evensen, G.: The Ensemble Kalman filter: theoretical formulation and practical implementation, Ocean Dynam., 53, 343–367, 2003.; Ferry, N., Parent, L., Garric, G., Bricaud, C., Testut, C.-E., Le Galloudec, O., Lellouche, J.-M., Drevillon, M., Greiner, E., Barnier, B., Molines, J.-M., Jourdain, N. C., Guinehut, S., Cabanes, C., and Zawadzki, L.: GLORYS2V1 global ocean reanalysis of the altimetric era (1992–2009) at meso scale, Mercator Quarterly Newsletter, 44, 29–39, 2012.; Haines, K.: Ocean data assimilation, in: Data assimilation: Making sense of observations, edited by: Lahoz, W., Khattatov, B., and Menard, R., Springer-Verlag, Berlin Heidelberg, 517–548, 2010.; Hamill, T. M. and Juras, J.: Measuring forecast skill: is it real skill or is it the varying climatology?, Q. J. Roy. Meteor. Soc., 132, 2905–2923, 2006.; Hamill, T. M., Whitaker, J. S., and Snyder, C.: Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter, Mon. Weather Rev., 129, 2776–2790, 2001.; Hersbach, H.: Decomposition of the continuous ranked probability score for ensemble p

 

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