A Bayesian parameter estimation method applied to a marine ecosystem model for the coastal Gulf of Alaska

Fiechter, Jérôme; Herbei, Radu; Leeds, William B.; Brown, Jeremiah; Milliff, Ralph F.; Wikle, Christopher K.; Moore, Andrew M.; Powell, Thomas M.

doi:10.1016/j.ecolmodel.2013.03.003

Cited by 22 publications

(30 citation statements)

References 31 publications

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“…In a parallel Bayes' hierarchical modelling study for the same model, Leeds et al (2013) assimilated satellite chlorophyll data at nine sites using a spatial Gaussian process model for the parameters with an anisotropic correlation matrix to allow for differences between along-shelf and cross-shelf dependence. The methods employed by Leeds et al (2013) and Fiechter et al (2013) seem promising because of their potential for rigorous treatment of uncertainty. However, in the absence of crossvalidation experiments, their potential for improving the predictive skill of the models is not well evaluated at present.…”

Section: Spatially Varying Parameter Estimates Derived With Bayesian mentioning

confidence: 99%

“…In this framework, posterior parameter distributions can vary between sites but the sites share common prior distributions. Fiechter et al (2013) used this approach to estimate parameter distributions for a 1-D NPZD-iron model at two sites in the Gulf of Alaska. Noninformative prior distributions were employed for each parameter so the influence of the priors on the solution for each site was fairly weak.…”

Section: Spatially Varying Parameter Estimates Derived With Bayesian mentioning

confidence: 99%

“…Simon and Bertino, 2012;Fiechter et al, 2013;Parslow et al, 2013;Weir et al, 2013;Dowd et al, 2014). In the Bayesian approach, model parameters and system state are both random variables.…”

Section: Recent Approachesmentioning

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: Spatially Varying Parameter Estimates Derived With Bayesian mentioning

confidence: 99%

Section: Spatially Varying Parameter Estimates Derived With Bayesian mentioning

confidence: 99%

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Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling

et al. 2017

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“…Kuroda and Kishi, 2004;Fiechter et al, 2013;Toyoda et al, 2013;Xiao and Friedrichs, 2014). Shigemitsu et al (2012) applied a unique assimilative approach to an LTL marine ecosystem model, using a micro-genetic algorithm (µ-GA) (Krishnakumar, 1990).…”

Section: Introductionmentioning

confidence: 99%

Biological data assimilation for parameter estimation of a phytoplankton functional type model for the western North Pacific

et al. 2018

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Abstract. Ecosystem models are used to understand ecosystem dynamics and ocean biogeochemical cycles and require optimum physiological parameters to best represent biological behaviours. These physiological parameters are often tuned up empirically, while ecosystem models have evolved to increase the number of physiological parameters. We developed a three-dimensional (3-D) lower-trophic-level marine ecosystem model known as the Nitrogen, Silicon and Iron regulated Marine Ecosystem Model (NSI-MEM) and employed biological data assimilation using a micro-genetic algorithm to estimate 23 physiological parameters for two phytoplankton functional types in the western North Pacific. The estimation of the parameters was based on a onedimensional simulation that referenced satellite data for constraining the physiological parameters. The 3-D NSI-MEM optimized by the data assimilation improved the timing of a modelled plankton bloom in the subarctic and subtropical regions compared to the model without data assimilation. Furthermore, the model was able to improve not only surface concentrations of phytoplankton but also their subsurface maximum concentrations. Our results showed that surface data assimilation of physiological parameters from two contrasting observatory stations benefits the representation of vertical plankton distribution in the western North Pacific.

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“…Thus, parametric models, such as those used in geostatistical analysis, are often considered (see Cressie, ; Stein, ; Chen and Dunson, ; Fuentes et al, ; Cressie and Wikle, ; among others). In particular, we are interested in models for one‐dimensional spatial covariances that are ubiquitous in environmental data assimilation applications (e.g., Lenartz et al , ; Mitchell and Gottwald, ; Williams et al , ; and Fiechter et al , ). However, in practice, such models are difficult to implement in time‐varying settings given the constraints that must be accommodated to guarantee that the spatio‐temporal covariance matrix is positive definite and hence valid.…”

Section: Introductionmentioning

confidence: 99%

Covariate‐based cepstral parameterizations for time‐varying spatial error covariances

2014

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The difference between a mechanistic model of a spatio‐temporal process and associated observations can reasonably be represented by a random process with possible dependence structure in space and time. Often, such error processes are assumed to be stationary in time, so that a unique spatial error covariance structure is applicable for all time. However, model performance can be related to large‐scale time‐varying features, causing the error process to be nonstationary, leading to a time‐varying spatial covariance. Because of dimensionality constraints, one cannot estimate such time‐varying covariances reliably, and therefore they must be modeled. Such models for covariances should be able to accommodate influences from exogenous covariates. However, the inclusion of covariates in a standard covariance model is challenging due to the fact that the covariance must be positive definite. We mitigate this issue by modeling the time‐varying spatial dependence through the Fourier coefficients of the log spectrum, as originally developed for time series representations. The convenient feature of these so‐called “cepstral” models (or “exponential models”) is that the associated parameters are unrestricted yet still are guaranteed to produce a positive definite covariance. Thus, it is fairly straightforward to model these parameters as time‐varying random processes that depend on other covariates within the Bayesian hierarchical modeling paradigm. We illustrate the effectiveness of this model through various simulated examples and by applying it to differences from a long‐lead sea surface temperature forecast model. In this case, we consider the Pacific Decadal Oscillation and Southern Oscillation Index as possible factors that influence the error covariance. Copyright © 2014 John Wiley & Sons, Ltd.

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A Bayesian parameter estimation method applied to a marine ecosystem model for the coastal Gulf of Alaska

Cited by 22 publications

References 31 publications

Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling

Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling

Biological data assimilation for parameter estimation of a phytoplankton functional type model for the western North Pacific

Covariate‐based cepstral parameterizations for time‐varying spatial error covariances

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