Application of model reduced 4D-Var to a 1D ecosystem model

Pelc, Joanna S.; Simon, Ehouarn; Bertino, Laurent; Serafy, Ghada El; Heemink, A.W.

doi:10.1016/j.ocemod.2012.09.003

Cited by 24 publications

(25 citation statements)

References 41 publications

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“…The application of the adjoint method helps to reduce the number of model runs to provide access to joint posterior mode and maximum likelihood estimates. Pelc et al (2012) provide useful theoretical background for different 4DVar approaches (four-dimensional, in space and time, variational approaches) and show how this adjoint method can be used to estimate ecosystem model parameters jointly with a large number of initial condition parameters. See also Bennett (2002) for an introduction to variational DA and adjoint methods in physical oceanography.…”

Section: Variational Methodsmentioning

confidence: 99%

“…For spatial models, it seems necessary to limit the degrees of freedom of the IC uncertainty (Li et al, 2006), e.g. by using a Bayesian error model with spatial covariance in the prior (Smith et al, 2009;Pelc et al, 2012). To model IC uncertainty, Gaussian distributions are most often employed, often with a log transform to improve realism of the distributional form (see Sect.…”

Section: Uncertainty In Initial Conditions (Ics)mentioning

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: Variational Methodsmentioning

confidence: 99%

Section: Uncertainty In Initial Conditions (Ics)mentioning

confidence: 99%

Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling

et al. 2017

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“…The assimilation of model-generated synthetic data in numerical twin experiments provides a useful tool for demonstrating the feasibility of an assimilation method [e.g., Lawson et al, 1996;Crispi et al, 2006;Hemmings and Challenor, 2012;Pelc et al, 2012], investigating the adequacy of available observations [e.g., Spitz et al, 1998;Friedrichs, 2001], as well as determining sensitivities and correlations of the optimized parameter sets [e.g., Schartau et al, 2001;Fennel et al, 2001;Kuroda and Kishi, 2004;Kidston et al, 2011].…”

Section: Assimilation Experiments 241 Identical Twin Assimilation mentioning

confidence: 99%

The assimilation of satellite‐derived data into a one‐dimensional lower trophic level marine ecosystem model

Xiao

Friedrichs

2014

J. Geophys. Res. Oceans

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Lower trophic level marine ecosystem models are highly dependent on the parameter values given to key rate processes, however many of these are either unknown or difficult to measure. One solution to this problem is to apply data assimilation techniques that optimize key parameter values, however in many cases in situ ecosystem data are unavailable on the temporal and spatial scales of interest. Although multiple types of satellite-derived data are now available with high temporal and spatial resolution, the relative advantages of assimilating different satellite data types are not well known. Here these issues are examined by implementing a lower trophic level model in a one-dimensional data assimilative (variational adjoint) model testbed. A combination of experiments assimilating synthetic and actual satellite-derived data, including total chlorophyll, size-fractionated chlorophyll and particulate organic carbon (POC), reveal that this is an effective tool for improving simulated surface and subsurface distributions both for assimilated and unassimilated variables. Model-data misfits were lowest when parameters were optimized individually at specific sites; however, this resulted in unrealistic overtuned parameter values that deteriorated model skill at times and depths when data were not available for assimilation, highlighting the importance of assimilating data from multiple sites simultaneously. Finally, when chlorophyll data were assimilated without POC, POC simulations still improved, however the reverse was not true. For this twophytoplankton size class model, optimal results were obtained when satellite-derived size-differentiated chlorophyll and POC were both assimilated simultaneously.

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“…Near‐zero positive definite variables whose distributions are such that their standard deviations are of the same order of magnitude as their means are inevitably skewed and non‐Gaussian. Such variables include high‐impact weather and climate variables such as those pertaining to aerosols (O'Neill et al , ; Saide et al , ), rainfall (Simpson, ; Errico et al , ; Husak et al , ), water‐vapour mixing ratio (Kliewer et al , ), cloud‐water/ice concentrations (Willis, ; Vivekanandan et al , ), phytoplankton (Pelc et al , ) and sea ice (Wadhams et al , ; Lange and Eicken, ).…”

Section: Introductionmentioning

confidence: 99%

The GIGG‐EnKF: ensemble Kalman filtering for highly skewed non‐negative uncertainty distributions

Bishop¹

2016

Quart J Royal Meteoro Soc

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Observations and predictions of near-zero non-negative variables such as aerosol, water vapour, cloud, precipitation and plankton concentrations have uncertainty distributions that are skewed and better approximated by gamma and inverse-gamma probability distribution functions (pdfs) than Gaussian pdfs. Current Ensemble Kalman Filters (EnKFs) yield suboptimal state estimates for these variables. Here, we introduce a variation on the EnKF that accurately solves Bayes' theorem in univariate cases where the prior forecasts and error-prone observations given truth come in (gamma, inverse-gamma) or (inversegamma, gamma) or (Gaussian, Gaussian) distribution pairs. Since its multivariate extension is similar to an EnKF, we refer to it as the GIGG-EnKF or GIGG where GIGG stands for Gamma, Inverse-Gamma and Gaussian. The GIGG-EnKF enables near-zero semi-positivedefinite variables with highly skewed uncertainty distributions to be assimilated without the need for observation bias inducing log-normal or Gaussian anamorphosis nonlinear transformations. In the special case that all observations are treated as Gaussian, the GIGG-EnKF gives identical results to the original EnKF. A multi-grid-point and multi-variable idealized system was used to compare and contrast the data assimilation performance of the GIGG with that of both the perturbed observation and deterministic forms of the EnKF. This test system featured variables and observation types whose uncertainty distributions approximate Gaussian, gamma and inverse-gamma distributions. The normalized analysis error variance of the GIGG ensemble mean was found to be significantly smaller than that of the EnKFs. The higher moments of the analysed ensemble distributions were tested by subjecting the ensemble members to nonlinear 'forecast' mappings. The normalized mean square error of the mean of the corresponding GIGG forecast ensemble was found to be less than a 3rd of that obtained from either form of the original EnKF.

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Application of model reduced 4D-Var to a 1D ecosystem model

Cited by 24 publications

References 41 publications

Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling

Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling

The assimilation of satellite‐derived data into a one‐dimensional lower trophic level marine ecosystem model

The GIGG‐EnKF: ensemble Kalman filtering for highly skewed non‐negative uncertainty distributions

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