Experiences in multiyear combined state–parameter estimation with an ecosystem model of the North Atlantic and Arctic Oceans using the Ensemble Kalman Filter

Simon, Ehouarn; Samuelsen, Annette; Bertino, Laurent; Mouysset, Sandrine

doi:10.1016/j.jmarsys.2015.07.004

Cited by 40 publications

(36 citation statements)

References 71 publications

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“…4.1). For systems with strong physical control, it may be possible to limit IC uncertainty to only the physical variables, allowing this to generate biochemical uncertainty over an initial burnin period (Natvik and Evensen, 2003;Simon et al, 2015).…”

Section: Uncertainty In Initial Conditions (Ics)mentioning

confidence: 99%

“…An experiment allowing both time and space variation in biogeochemical parameters that includes cross-validation is presented by Simon et al (2015). Performance is compared against that of a model with constant spatially uniform parameters specified a priori but not against static and/or uniform parameter solutions to the DA problem.…”

Section: Time-varying Parametersmentioning

confidence: 99%

“…when applying sequential DA approaches with recurrent analyses steps and corrections of the simulated state variables. An example is the study of Simon et al (2015), who introduced an ensemble-based DA method for a large-scale biogeochemical model of the North Atlantic and Arctic oceans. The focus of their study was to estimate spatial and temporal variations of phytoplankton and zooplankton loss rate parameters as well as model states, in order to establish an operational system for hindcasts and forecasts of Chl a concentrations.…”

Section: Consistency Between Tracer Distribution and Ocean Circulatiomentioning

confidence: 99%

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Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling

et al. 2017

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Abstract. To describe the underlying processes involved in oceanic plankton dynamics is crucial for the determination of energy and mass flux through an ecosystem and for the estimation of biogeochemical element cycling. Many planktonic ecosystem models were developed to resolve major processes so that flux estimates can be derived from numerical simulations. These results depend on the type and number of parameterizations incorporated as model equations. Furthermore, the values assigned to respective parameters specify a model's solution. Representative model results are those that can explain data; therefore, data assimilation methods are utilized to yield optimal estimates of parameter values while fitting model results to match data. Central difficulties are (1) planktonic ecosystem models are imperfect and (2) data are often too sparse to constrain all model parameters. In this review we explore how problems in parameter identification are approached in marine planktonic ecosystem modelling.We provide background information about model uncertainties and estimation methods, and how these are considered for assessing misfits between observations and model results. We explain differences in evaluating uncertainties in parameter estimation, thereby also discussing issues of parameter identifiability. Aspects of model complexity are addressed and we describe how results from cross-validation studies provide much insight in this respect. Moreover, approaches are discussed that consider time-and spacedependent parameter values. We further discuss the use of dynamical/statistical emulator approaches, and we elucidate issues of parameter identification in global biogeochemical models. Our review discloses many facets of parameter identification, as we found many commonalities between the objectives of different approaches, but scientific insight differed between studies. To learn more from results of planktonic ecosystem models we recommend finding a good balance in the level of sophistication between mechanistic modelling and statistical data assimilation treatment for parameter estimation.

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Section: Uncertainty In Initial Conditions (Ics)mentioning

confidence: 99%

Section: Time-varying Parametersmentioning

confidence: 99%

Section: Consistency Between Tracer Distribution and Ocean Circulatiomentioning

confidence: 99%

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Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling

et al. 2017

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“…Because when the ensemble spread is too small, the state variables will conform to the background and the observation will barely exert any effect. Parameter ensemble collapsing is a common problem encountered by many studies of parameter estimation in an augmented state vector framework (Aksoy et al, 2006;Simon et al, 2015). Regarding our test, firstly, after the 1st cycle, the estimated parameters have to be severely tampered before they can be used to evolve the analysis ensemble to the beginning of the 2nd cycle (therefore formulating the forecast ensemble).…”

Section: D Case Results Synthetic Observations and Real Datamentioning

confidence: 99%

“…If we assume that the parameter variables are random variables, then the extension of state space-based methods to parameter space is straightforward by encapsulating the parameters. The state augmentation technique has shown to be effective in diverse applications (Tong and Xue, 2008;Kang et al, 2011;Sawada et al, 2015;Simon et al, 2015). The parameter estimation is also related to the estimation of model error from ensemble-based approach.…”

Section: Estimation Through Data Assimilation Processmentioning

confidence: 99%

High-resolution data assimilation through stochastic subgrid tensor and parameter estimation from 4DEnVar

Yin¹,

Mémin

2017

Tellus A: Dynamic Meteorology and Oceanography

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In this paper, we explore a dynamical formulation allowing to assimilate high-resolution data in a large-scale fluid flow model. This large-scale formulation relies on a random modelling of the small-scale velocity component and allows to take into account the scale discrepancy between the dynamics and the observations. It introduces a subgrid stress tensor that naturally emerges from a modified Reynolds transport theorem adapted to this stochastic representation of the flow. This principle is used within a stochastic shallow water model coupled with an 4DEnVar assimilation technique to estimate both the flow initial conditions and the inhomogeneous time-varying subgrid parameters. The performance of this modelling has been assessed numerically with both synthetic and real-world data. Our strategy has shown to be very effective in providing a more relevant prior/posterior ensemble in terms of the dispersion compared to other tests using the standard shallow water equations with no subgrid parameterization or with simple eddy viscosity models. We also compared two localization techniques. The results indicate the localized covariance approach is more suitable to deal with the scale discrepancy-related errors.

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Parameter and state estimation with ensemble Kalman filter based algorithms for convective‐scale applications

Ruckstuhl

Janjić

2018

Quart J Royal Meteoro Soc

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Representation of clouds in convection‐permitting models is sensitive to numerical weather prediction (NWP) model parameters that are often very crudely known (for example roughness length). Our goal is to allow for uncertainty in these parameters and estimate them from data using the ensemble Kalman filter (EnKF) approach. However, to deal with difficulties associated with convective‐scale applications, such as non‐Gaussianity and constraints on state and parameter values, modifications to classical EnKF are necessary. In this article, we evaluate and extend several recently developed EnKF‐based algorithms that either incorporate constraints such as mass conservation and positivity of precipitation explicitly or introduce higher order moments on the joint state and parameter estimation problem. We compare their results with the localized EnKF for a common idealized test case. The test case uses perfect model experiments with the one‐dimensional modified shallow‐water model, which was designed to mimic important properties of convection. We use a stochastic dynamical model for parameters, in order to prevent underdispersion in parameter space. To deal with localization for estimation of parameters, we introduce a method called global updating, which is a computationally cheap modification of spatial updating and was proven successful in this context. The sensitivity of the results to the number of ensemble members and localization, as well as observation coverage and frequency, is shown. Although all algorithms are capable of reducing the initial state and parameter errors, it is concluded that mass conservation is important when the localization radius is small and/or the observations are sparse. In addition, accounting for higher order moments in the joint space and parameter estimation problem is beneficial when the ensemble size is large enough or when applied to parameter estimation only.

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Experiences in multiyear combined state–parameter estimation with an ecosystem model of the North Atlantic and Arctic Oceans using the Ensemble Kalman Filter

Cited by 40 publications

References 71 publications

Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling

Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling

High-resolution data assimilation through stochastic subgrid tensor and parameter estimation from 4DEnVar

Parameter and state estimation with ensemble Kalman filter based algorithms for convective‐scale applications

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