A data assimilation technique applied to a predator-prey model

Lawson, L. M.; Spitz, Yvette H.; Hofmann, Eileen E.; Long, Robert Bryan

doi:10.1016/s0092-8240(05)80759-1

Cited by 69 publications

(58 citation statements)

References 27 publications

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“…Adjoint methods allow the gradient of the cost function with respect to all fitted parameters to be computed in an extremely efficient manner; see Lawson et al (1995), and Appendix C. This is particularly useful when dealing with a large number of fitted parameters (highdimensional ) of computationally expensive models (e.g. Tjiputra et al, 2007).…”

Section: Variational Methodsmentioning

confidence: 99%

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: Variational Methodsmentioning

confidence: 99%

Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling

et al. 2017

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“…The variational adjoint method is a nonlinear, weighted least-squares optimization method that minimizes the misfit between the model estimates and the observational data by optimizing a subset of model parameters (e.g., Lawson et al, 1995Lawson et al, , 1996. The choice of parameters for optimization depends strongly on the data available for optimization.…”

Section: Data Assimilation Frameworkmentioning

confidence: 99%

Using biogeochemical data assimilation to assess the relative skill of multiple ecosystem models in the Mid-Atlantic Bight: effects of increasing the complexity of the planktonic food web

Xiao

Friedrichs

2014

Biogeosciences

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Abstract. Now that regional circulation patterns can be reasonably well reproduced by ocean circulation models, significant effort is being directed toward incorporating complex food webs into these models, many of which now routinely include multiple phytoplankton (P) and zooplankton (Z) compartments. This study quantitatively assesses how the number of phytoplankton and zooplankton compartments affects the ability of a lower-trophic-level ecosystem model to reproduce and predict observed patterns in surface chlorophyll and particulate organic carbon. Five ecosystem model variants are implemented in a one-dimensional assimilative (variational adjoint) model testbed in the Mid-Atlantic Bight. The five models are identical except for variations in the level of complexity included in the lower trophic levels, which range from a simple 1P1Z food web to a considerably more complex 3P2Z food web. The five models assimilated satellite-derived chlorophyll and particulate organic carbon concentrations at four continental shelf sites, and the resulting optimal parameters were tested at five independent sites in a cross-validation experiment. Although all five models showed improvements in model-data misfits after assimilation, overall the moderately complex 2P2Z model was associated with the highest model skill. Additional experiments were conducted in which 20 % random noise was added to the satellite data prior to assimilation. The 1P and 2P models successfully reproduced nearly identical optimal parameters regardless of whether or not noise was added to the assimilated data, suggesting that random noise inherent in satellitederived data does not pose a significant problem to the assimilation of satellite data into these models. However, the most complex model tested (3P2Z) was sensitive to the level of random noise added to the data prior to assimilation, highlighting the potential danger of over-tuning inherent in such complex models.

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“…The adjoint method has been used for parameter estimation in a variety of oceanographic systems (Panchang and O'Brien, 1989;Lardner and Das, 1994). More recently, it has been used in simple biological models (Lawson et al, 1995) and coupled physical-biological models (Gunson et al, 1999;Tjipurea et al, 2007;Zhao and Lu, 2008;Li et al, 2012). An adjoint data assimilation method is very useful in determining model inputs (parameters, forcings, etc.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of physical and biological control over phytoplankton in the Gulf of Maine-Georges Bank region using an adjoint data assimilation approach

Wang

Malanotte‐Rizzoli

2014

J. Ocean Univ. China

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The linkage between physical and biological processes, particularly the effect of the circulation field on the distribution of phytoplankton, is studied by applying a two-dimensional model and an adjoint data assimilation approach to the Gulf of Maine-Georges Bank region. The model results, comparing well with observation data, reveal seasonal and geographic variations of phytoplankton concentration and verify that the seasonal cycles of phytoplankton are controlled by both biological sources and advection processes which are functions of space and time and counterbalance each other. Although advective flux divergences have greater magnitudes on Georges Bank than in the coastal region of the western Gulf of Maine, advection control over phytoplankton concentration is more significant in the coastal region of the western Gulf of Maine. The model results also suggest that the two separated populations in the coastal regions of the western Gulf of Maine and on Georges Bank are self-sustaining.

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A data assimilation technique applied to a predator-prey model

Cited by 69 publications

References 27 publications

Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling

Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling

Using biogeochemical data assimilation to assess the relative skill of multiple ecosystem models in the Mid-Atlantic Bight: effects of increasing the complexity of the planktonic food web

Diagnosis of physical and biological control over phytoplankton in the Gulf of Maine-Georges Bank region using an adjoint data assimilation approach

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