Abstract
Coastal management and maritime safety strongly rely on accurate representations of the sea state. Both dynamical models and observations provide abundant pieces of information. However, none of them provides the complete picture. The assimilation of observations into models is one way to improve our knowledge of the ocean state. Its application in coastal models remains challenging because of the wide range of temporal and spatial variabilities of the processes involved. This study investigates the assimilation of temperature profiles with the ensemble Kalman filter in 3-D North Sea simulations. The model error is represented by the standard deviation of an ensemble of model states. Parameters’ values for the ensemble generation are first computed from the misfit between the data and the model results without assimilation. Then, two square root algorithms are applied to assimilate the data. The impact of data assimilation on the simulated temperature is assessed. Results show that the ensemble Kalman filter is adequate for improving temperature forecasts in coastal areas, under adequate model error specification.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Anderson JL (2007) Exploring the need for localization in ensemble data assimilation using a hierarchical ensemble filter. Phys D 230:99–111
Barth A, Alvera-Azcárate A, Gurgel K.-W, Staneva J, Port A, Beckers J-M, Stanev EV (2010) Ensemble perturbation smoother for optimizing tidal conditions by assimilation of high-frequency radar surface currents—application to the in German Bight. Ocean Dyn 6:161–178
Brasseur P, Ballabrera-Poy J, Verron J (1999) Assimilation of altimetric data in the mid-latitude oceans using the singular evolutive extended Kalman filter with an eddy-resolving primitive equation model. J Mar Syst 22:269–294
Breivik O, Saetra O (2001) Real time assimilation of HF radar currents into a coastal ocean model. J Mar Syst 28:161–182
Echevin V, De Mey P (2000) Horizontal and vertical structure of the representer functions for sea surface measurements in a coastal circulation model. J Phys Oceanogr 30:2627–2635
Evensen G (1994) Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J Geophys Res 99:10143–10162
Evensen G (2003) The ensemble Kalman filter: theoretical formulation and practical implementation. Ocean Dyn 53:343–367. doi:10.1007/s10236-003-0036-9
Evensen G (2004) Sampling strategies and square root analysis schemes for the EnKF. Ocean Dyn 54:539–560. doi:10.1007/s10236-004-0099-2
Hamill TM, Withaker JS, Snyder C (2001) Distance-dependent filtering of background covariances estimates in an ensemble Kalman filter. Mon Weather Rev 129:2884–2903
Haugen VE, Evensen G (2002) Assimilation of SLA and SST data into an OGCM for the Indian Ocean. Ocean Dyn 52:133–151
Holt JT (2008) POLCOMS user guide. Version 6.4
Holt JT, James ID (1999) A simulation of the Southern North Sea in comparison with measurements from the North Sea Project. Part 1: temperature. Cont Shelf Res 19:1087–1112
Holt JT, James ID (2001) An s coordinate density evolving model of the northwest European continental shelf: 1. Model description and density structure. J Geophys Res Oceans 106:14015–14034. doi:10.1029/2000JC000304
Houtekamer P, Mitchell HL (1998) A sequential ensemble Kalman filter technique. Mon Weather Rev 129:2776–2790
Jensen TG (1998) Open boundary conditions in stratified ocean models. J Mar Syst 16:297–322
Kalman RE (1960) A new approach to linear filter and prediction theory. J Basic Eng 82 D:35–45
Keppenne CL, Rienecker MM (2002) Initial testing of a massively parallel ensemble filter with the Poseidon isopycnal ocean general circulation model. Mon Weather Rev 130:2951–2965
Luyten P (2011) COHERENS—a coupled hydrodynamical-ecological model for regional and shelf seas: user documentation. Version 2.1.2 RBINS-MUMM Report, Royal Belgian Institute of Natural Sciences
Luyten PJ, Deleersnijder E, Ozer J, Ruddick KG (1996) Presentation of a family of turbulence closure models for stratified shallow water flows and preliminary application to the Rhine outflow region. Cont Shelf Res 16:101–130
Luyten P, Carniel S, Umgiesser G (2002) Validation of turbulence closure parameterisations for stably stratified flows using the PROVESS turbulence measurements in the North Sea. J Sea Res 47:239–267
Luyten P, Jones JE, Proctor R (2003) A numerical study of the long- and short-term temperature variability and thermal circulation in the North Sea. J Phys Oceanogr 33:37–56
Oke PR, Allen JS, Miller RN, Egbert GD, Kosro PM (2002) Assimilation of surface velocity data into a primitive equation coastal model. J Geophys Res 107. doi:10.1029/2000JC000511
Ponsar S, Luyten P (2009) Data assimilation with the EnKF in a 1-D numerical model of a North Sea station. Ocean Dyn 59:983–996. doi:10.1007/s10236-009-0224-3
Robinson AR, Lermusiaux PFJ, Quincy Sloan N III (1998) Chapter 20. Data assimilations. The Sea
She J, Amstrup B, Borenas K, Buch E, Funkquist L, Luyten P, Proctor R (2006) ODON: optimal design of observational networks. Scientific Report
Tippett MK, Anderson JL, Bishop CH (2003) Ensemble square root filters. Mon Weather Rev 131:1485–1490
van Leeuwen PJ (1999) Comment on ‘Data assimilation using an ensemble Kalman filter technique’. Mon Weather Rev 127:1374–1377
Verlaan M (1998) Efficient Kalman filtering algorithms for hydrodynamic models. PhD thesis
Verlaan M, Zijderveld A, de Vries H, Kroos J (2005) Operational storm surge forecasting in the Netherlands: developments in the last decade. Phil Trans R Soc A 363:1441–1453. doi:10.1098/rsta.2005.1578
Zheng F, Zhu J (2008) Balanced multivariate model errors of an intermediate coupled model for ensemble Kalman filter data assimilation. J Geophys Res 113:C07002. doi:10.1029/2007JC004621
Acknowledgments
The anonymous reviewers whose comments greatly improved this paper are gratefully acknowledged. Part of the work presented in this article has been supported by the EC project MyOcean under contract N ∘: FP7-SPACE-2007-1 and by the EC project JERICO under contract F P7−I N F R A S T R U C T U R E S−2010−1. Acknowledgment is made for the use of ECMWF’s computing and archive facilities in this research.
Author information
Authors and Affiliations
Corresponding author
Additional information
Responsible Editor: Yajing Yan
This article is part of the Topical Collection on the 47th International Liège Colloquium on Ocean Dynamics, Liège, Belgium, 4–8 May 2015
Rights and permissions
About this article
Cite this article
Ponsar, S., Luyten, P. & Dulière, V. Data assimilation with the ensemble Kalman filter in a numerical model of the North Sea. Ocean Dynamics 66, 955–971 (2016). https://doi.org/10.1007/s10236-016-0968-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10236-016-0968-5