Introduction and Assessment of Measures for Quantitative Model-Data Comparison Using Satellite Images

Mattern, Jann Paul; Fennel, Katja; Dowd, Michael K.

doi:10.3390/rs2030794

Cited by 10 publications

(9 citation statements)

References 24 publications

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“…Parameter estimation requires an appropriate measure of model‐observation misfit that is minimized in the estimation process. We determine the mismatch between observed and simulated surface chlorophyll fields using the adapted grey block distance (AGB) [ Mattern et al , ]. AGB incorporates the computation of the model observation misfit on multiple spatial scales, which is advantageous compared to standard distance measures such as the root mean square error when dealing with noise, missing values, and coherent features at various spatial scales that may be present in the model output or the observations.…”

Section: Methodsmentioning

confidence: 99%

Periodic time‐dependent parameters improving forecasting abilities of biological ocean models

Mattern

Fennel

Dowd

2014

Geophysical Research Letters

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Using two emulator‐based procedures, we estimate time‐dependent values for two key plankton parameters in a three‐dimensional biogeochemical (BGC) ocean model. The estimation is based on a 4 year time series of daily surface satellite chlorophyll observations. The estimated parameters display an annual periodicity that can be explained by the succession of plankton groups in the study region. Model simulations using these parameters show improved fit to observations and better forecasting abilities compared to simulations with constant optimal parameters; the newly introduced sequential parameter estimation procedure creates the strongest improvement. The inclusion of time‐dependent parameters represents a simple way to improve the predictive skill of BGC models and their representation of plankton dynamics.

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Section: Methodsmentioning

confidence: 99%

Periodic time‐dependent parameters improving forecasting abilities of biological ocean models

Mattern

Fennel

Dowd

2014

Geophysical Research Letters

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“…The model‐data comparison is now reduced to the problem of comparing two real‐valued images, both of which typically contains missing values (land grid cells within the model domain are turned into missing values, see Mattern et al . []).…”

Section: Particle Filter Implementationmentioning

confidence: 99%

“…For image comparison, we use the adapted grey block (AGB) distance measure introduced by Mattern et al . [], which was also used in the emulator study by Mattern et al . [].…”

Section: Particle Filter Implementationmentioning

confidence: 99%

“…Using the same approach, we also transform the corresponding model surface chlorophyll field into an image. The model-data comparison is now reduced to the problem of comparing two real-valued images, both of which typically contains missing values (land grid cells within the model domain are turned into missing values, see Mattern et al [2010b]).…”

Section: Sir Weightingmentioning

confidence: 99%

“…[23] For image comparison, we use the adapted grey block (AGB) distance measure introduced by Mattern et al [2010b], which was also used in the emulator study by Mattern et al [2012]. To compute an AGB distance value, two images are compared at different resolution levels by dividing them into successively smaller blocks and computing the mean value of the pixels within each block.…”

Section: Sir Weightingmentioning

confidence: 99%

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Particle filter‐based data assimilation for a three‐dimensional biological ocean model and satellite observations

2013

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[1] We assimilate satellite observations of surface chlorophyll into a three-dimensional biological ocean model in order to improve its state estimates using a particle filter referred to as sequential importance resampling (SIR). Particle Filters represent an alternative to other, more commonly used ensemble-based state estimation techniques like the ensemble Kalman filter (EnKF). Unlike the EnKF, Particle Filters do not require normality assumptions about the model error structure and are thus suitable for highly nonlinear applications. However, their application in oceanographic contexts is typically hampered by the high dimensionality of the model's state space. We apply SIR to a high-dimensional model with a small ensemble size (20) and modify the standard SIR procedure to avoid complications posed by the high dimensionality of the model state. Two extensions to the SIR include a simple smoother to deal with outliers in the observations, and stateaugmentation which provides the SIR with parameter memory. Our goal is to test the feasibility of biological state estimation with SIR for realistic models. For this purpose we compare the SIR results to a model simulation with optimal parameters with respect to the same set of observations. By running replicates of our main experiments, we assess the robustness of our SIR implementation. We show that SIR is suitable for satellite data assimilation into biological models and that both extensions, the smoother and stateaugmentation, are required for robust results and improved fit to the observations. Citation: Mattern, J. P., M. Dowd, and K. Fennel (2013), Particle filter-based data assimilation for a 3-dimensional biological ocean model and satellite observations,

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Sensitivity and uncertainty analysis of model hypoxia estimates for the Texas‐Louisiana shelf

2013

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[1] Numerical ocean models are becoming increasingly important tools for marine research and for management of marine resources. It is therefore crucial that uncertainty in model predictions and model sensitivity to errors in the model inputs be quantified. We performed a combined sensitivity and uncertainty analysis for a realistic physical-biological model of the Texas-Louisiana shelf in the northern Gulf of Mexico. The model simulates the major physical and biological processes involved in the formation of the hypoxic zone that develops on the shelf every summer. With the help of a statistical emulator technique, we introduced uncertainty in selected model inputs and assessed the effects of these uncertainties on the predicted development and spatial distribution of bottom hypoxia. The uncertain inputs we examined belong to two categories: (i) biological inputs including river nutrient concentration, phytoplankton growth rate and initial and boundary conditions of biological variables, and (ii) physical inputs including freshwater river runoff, wind forcing, and mixing coefficients. We show that uncertainty in different inputs has distinct effects on model output which vary in magnitude, time, and space. Uncertainty in physical inputs was found to have a strong impact on estimates of hypoxia, e.g., hypoxic area estimates vary by more than 40%, due to a 20% variation in the freshwater river runoff.Citation: Mattern, J. P., K. Fennel, and M. Dowd (2013), Sensitivity and uncertainty analysis of model hypoxia estimates for the Texas-Louisiana shelf,

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Introduction and Assessment of Measures for Quantitative Model-Data Comparison Using Satellite Images

Cited by 10 publications

References 24 publications

Periodic time‐dependent parameters improving forecasting abilities of biological ocean models

Periodic time‐dependent parameters improving forecasting abilities of biological ocean models

Particle filter‐based data assimilation for a three‐dimensional biological ocean model and satellite observations

Sensitivity and uncertainty analysis of model hypoxia estimates for the Texas‐Louisiana shelf

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