Application of an Ensemble Kalman filter to a 1-D coupled hydrodynamic-ecosystem model of the Ligurian Sea

Lenartz, Fabian; Raick, C.; Soetaert, Karline; Grégoire, Marilaure

doi:10.1016/j.jmarsys.2006.12.001

Cited by 20 publications

(12 citation statements)

References 25 publications

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“…The log‐transformation applied here, despite being a useful approach, does not solve these issues, since it still requires a control (that poses some drawbacks) and it does not guarantee Gaussian distributions. To this regard, Gaussian anamorphosis methods have been successfully tested in twin experiments [e.g., Simon and Bertino , 2009; Doron et al , 2011], applied with in situ data [ Lenartz et al , 2007], and appear promising for applications with real ocean color data.…”

Section: Discussionmentioning

confidence: 99%

Can ocean color assimilation improve biogeochemical hindcasts in shelf seas?

et al. 2011

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[1] The objective of this paper is to investigate if the assimilation of ocean color data into a complex marine ecosystem model can improve the hindcast of key biogeochemical variables in shelf seas. A localized Ensemble Kalman filter was used to make a yearlong assimilation of weekly satellite chlorophyll data into a three-dimensional ecosystem model of the western English Channel. The skill of assimilation was evaluated with respect to non assimilated in situ data using twelve time series of biogeochemical observations collected weekly at the monitoring station L4. It was found that the assimilation scheme reduced the root mean square error and increased the correlation with the spatial distributions of the assimilated chlorophyll data, with respect to the reference run. More significantly, the skill metrics for non assimilated variables indicate that the hindcast of the mean data values at L4 was improved; however improvements in the short term forecast were not discernable. On the basis of our application, we provide general recommendations for the successful application of ocean color assimilation to hindcast key biogeochemical variables in shelf seas.

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Section: Discussionmentioning

confidence: 99%

Can ocean color assimilation improve biogeochemical hindcasts in shelf seas?

et al. 2011

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“…A possible approach to non-Gaussian estimation problems is the use of anamorphosis transformations (i.e., Bertino et al, 2003;Lenartz et al, 2007), making nonlinear changes of variables to transform the forecast pdf (of arbitrary shape) into a Gaussian pdf. At first glance, this does not necessarily simplify the problem because identifying the change of variables requires a perfect knowledge of the original multivariate pdf, i.e.…”

Section: Béal Et Al: Mixing Errors In Physical-biogeochemical Modelsmentioning

confidence: 99%

Characterization of mixing errors in a coupled physical biogeochemical model of the North Atlantic: implications for nonlinear estimation using Gaussian anamorphosis

et al. 2010

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Abstract. In biogeochemical models coupled to ocean circulation models, vertical mixing is an important physical process which governs the nutrient supply and the plankton residence in the euphotic layer. However, vertical mixing is often poorly represented in numerical simulations because of approximate parameterizations of sub-grid scale turbulence, wind forcing errors and other mis-represented processes such as restratification by mesoscale eddies. Getting a sufficient knowledge of the nature and structure of these errors is necessary to implement appropriate data assimilation methods and to evaluate if they can be controlled by a given observation system.In this paper, Monte Carlo simulations are conducted to study mixing errors induced by approximate wind forcings in a three-dimensional coupled physical-biogeochemical model of the North Atlantic with a 1/4 • horizontal resolution. An ensemble forecast involving 200 members is performed during the 1998 spring bloom, by prescribing perturbations of the wind forcing to generate mixing errors. The biogeochemical response is shown to be rather complex because of nonlinearities and threshold effects in the coupled model. The response of the surface phytoplankton depends on the region of interest and is particularly sensitive to the local stratification. In addition, the statistical relationships computed between the various physical and biogeochemical variables reflect the signature of the non-Gaussian behaviour of the system. It is shown that significant information on the ecosystem can be retrieved from observations of chlorophyll concentration or sea surface temperature if a simple nonlinear change of variables (anamorphosis) is performed by mapping separately Correspondence to: P. Brasseur (pierre.brasseur@hmg.inpg.fr) and locally the ensemble percentiles of the distributions of each state variable on the Gaussian percentiles. The results of idealized observational updates (performed with perfect observations and neglecting horizontal correlations) indicate that the implementation of this anamorphosis method into sequential assimilation schemes can substantially improve the accuracy of the estimation with respect to classical computations based on the Gaussian assumption.

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“…It has gained popularity because of its simple conceptual formulation and relative ease of implementation, e.g., it requires no derivation of a tangent linear operator or adjoint equations, and no integrations backward in time (Evensen 2006;Franssen and Kinzelbach 2009). Therefore, this has been a useful approach and has received more popularity than KF and EKF in many different study fields (Chen and Zhang 2006;Eknes and Evensen 2002;Evensen 2003Evensen , 2004Evensen , 2006Franssen and Kinzelbach 2009;Oliver 2005, 2006;Huang et al 2008aHuang et al , b, 2009Houtekamer and Mitchell 1998;Komma et al 2008;Lenartz et al 2007;Moradkhani et al 2005;Naevdal et al 2003;Nerger et al 2007;Ott et al 2004;Pan and Wood 2006;Reichle et al 2002a, b;Wen and Chen 2005).…”

Section: Introductionmentioning

confidence: 99%

Using data assimilation method to calibrate a heterogeneous conductivity field conditioning on transient flow test data

Tong

Yang

2010

Stoch Environ Res Risk Assess

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A data assimilation method is developed to calibrate a heterogeneous hydraulic conductivity field conditioning on transient pumping test data. The ensemble Kalman filter (EnKF) approach is used to update model parameters such as hydraulic conductivity and model variables such as hydraulic head using available data. A synthetical two-dimensional flow case is used to assess the capability of the EnKF method to calibrate a heterogeneous conductivity field by assimilating transient flow data from observation wells under different hydraulic boundary conditions. The study results indicate that the EnKF method will significantly improve the estimation of the hydraulic conductivity field by assimilating continuous hydraulic head measurements and the hydraulic boundary condition will significantly affect the simulation results. For our cases, after a few data assimilation steps, the assimilated conductivity field with four Neumann boundaries matches the real field well while the assimilated conductivity field with mixed Dirichlet and Neumann boundaries does not. We found in our cases that the ensemble size should be 300 or larger for the numerical simulation. The number and the locations of the observation wells will significantly affect the hydraulic conductivity field calibration.

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Application of an Ensemble Kalman filter to a 1-D coupled hydrodynamic-ecosystem model of the Ligurian Sea

Cited by 20 publications

References 25 publications

Can ocean color assimilation improve biogeochemical hindcasts in shelf seas?

Can ocean color assimilation improve biogeochemical hindcasts in shelf seas?

Characterization of mixing errors in a coupled physical biogeochemical model of the North Atlantic: implications for nonlinear estimation using Gaussian anamorphosis

Using data assimilation method to calibrate a heterogeneous conductivity field conditioning on transient flow test data

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