Emulator-assisted reduced-rank ecological data assimilation for nonlinear multivariate dynamical spatio-temporal processes

Leeds, William B.; Wikle, Christopher K.; Fiechter, Jérôme

doi:10.1016/j.stamet.2012.11.004

Cited by 25 publications

(23 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although there are methods available to reduce the dimensionality for multi-dimensional model output (e.g. Higdon et al, 2008;Leeds et al, 2014), it remains practically infeasible to capture the complete output of a 3-D coupled ocean ecosystem model. While the theory for statistical emulation is relatively well described (e.g.…”

Section: Statistical Emulatorsmentioning

confidence: 99%

See 1 more Smart Citation

Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling

et al. 2017

View full text Add to dashboard Cite

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.

show abstract

Section: Statistical Emulatorsmentioning

confidence: 99%

“…In contrast to most other approaches, where variances are estimated based on Bayesian techniques, Hooten et al (2011) used a Bayesian approach to estimate the mean values. The study of Leeds et al (2014) applied a similar technique for DA.…”

Section: Statistical Emulatorsmentioning

confidence: 99%

Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling

et al. 2017

View full text Add to dashboard Cite

show abstract

“…Such emulator‐based models can accommodate nonlinear and multivariate spatio‐temporal dynamical process information. Typically, in the DSTM context, these emulator‐based models should be considered from a first‐order perspective in which the model evolution is accounted for explicitly in the mean structure …”

Section: Dynamic Spatio‐temporal Modelsmentioning

confidence: 99%

“…Typically, in the DSTM context, these emulator-based models should be considered from a first-order perspective in which the model evolution is accounted for explicitly in the mean structure. 22,23…”

Section: Parameter Reductionmentioning

confidence: 99%

Modern perspectives on statistics for spatio‐temporal data

Wikle

2014

WIREs Computational Stats

View full text Add to dashboard Cite

Spatio-temporal statistical models are increasingly being used across a wide variety of scientific disciplines to describe and predict spatially explicit processes that evolve over time. Although descriptive models that approach this problem from the second-order (covariance) perspective are important, many real-world processes are dynamic, and it can be more efficient in such cases to characterize the associated spatio-temporal dependence by the use of dynamical models. The challenge with the specification of such dynamical models has been related to the curse of dimensionality and the specification of realistic dependence structures. Even in fairly simple linear/Gaussian settings, spatio-temporal statistical models are often over parameterized. This problem is compounded when the spatio-temporal dynamical processes are nonlinear or multivariate. Hierarchical models have proven invaluable in their ability to deal to some extent with this issue by allowing dependency among groups of parameters and science-based parameterizations. Such models are best considered from a Bayesian perspective, with associated computational challenges. Spatio-temporal statistics remains an active and vibrant area of research.

show abstract

“…There are some parametric DSTMs that have been developed to accommodate complex structure, notably those in the class of general quadratic nonlinear models (Wikle and Hooten, 2010), which are flexible and which have been shown to be suitable for modelling many real-world processes. Since these models are highly parameterised, one typically employs process dimension reduction, and/or regularisation when making inference, either by directly incorporating knowledge about the underlying dynamics of the system of interest and/or through prior specification within a multi-level (deep) Bayesian hierarchical modelling (BHM) framework (e.g., Wikle et al, 2001;Wikle and Hooten, 2010;Leeds et al, 2014). Quadratic nonlinear models tend to be quite complex, they require a relatively large amount of training data, and are computationally challenging to implement.…”

Section: Introductionmentioning

confidence: 99%

Deep integro-difference equation models for spatio-temporal forecasting

Zammit‐Mangion

Wikle

2020

Spatial Statistics

View full text Add to dashboard Cite

Integro-difference equation (IDE) models describe the conditional dependence between the spatial process at a future time point and the process at the present time point through an integral operator. Nonlinearity or temporal dependence in the dynamics is often captured by allowing the operator parameters to vary temporally, or by re-fitting a model with a temporally-invariant linear operator at each time point in a sliding window. Both procedures tend to be excellent for prediction purposes over small time horizons, but are generally time-consuming and, crucially, do not provide a global prior model for the temporally-varying dynamics that is realistic. Here, we tackle these two issues by using a deep convolution neural network (CNN) in a hierarchical statistical IDE framework, where the CNN is designed to extract process dynamics from the process' most recent behaviour. Once the CNN is fitted, probabilistic forecasting can be done extremely quickly online using an ensemble Kalman filter with no requirement for repeated parameter estimation. We conduct an experiment where we train the model using 13 years of daily sea-surface temperature data in the North Atlantic Ocean. Forecasts are seen to be accurate and calibrated. A key advantage of our approach is that the CNN provides a global prior model for the dynamics that is realistic, interpretable, and computationally efficient. We show the versatility of the approach by successfully producing 10-minute nowcasts of weather radar reflectivities in Sydney using the same model that was trained on daily sea-surface temperature data in the North Atlantic Ocean.

show abstract

Emulator-assisted reduced-rank ecological data assimilation for nonlinear multivariate dynamical spatio-temporal processes

Cited by 25 publications

References 32 publications

Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling

Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling

Modern perspectives on statistics for spatio‐temporal data

Deep integro-difference equation models for spatio-temporal forecasting

Contact Info

Product

Resources

About