2014
DOI: 10.1016/j.ocemod.2014.01.001
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Data assimilation within the Advanced Circulation (ADCIRC) modeling framework for the estimation of Manning’s friction coefficient

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Cited by 66 publications
(69 citation statements)
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References 27 publications
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“…However, even if the initial system state can be described awlessly, model parameters simplify the physical processes and, by doing so, will result in the growth of prediction errors. At present, assimilation methods being developed to improve morphological forecast reliability are producing encouraging results (e.g., [133][134][135][136][137]). For example, ad hoc data assimilation schemes and techniques using more re ned heuristic tuning of model state variables are being used to improve the performance of suspended sediment transport models (e.g., [138,139]).…”
Section: Improving Predictions and Reducing Uncertaintymentioning
confidence: 99%
“…However, even if the initial system state can be described awlessly, model parameters simplify the physical processes and, by doing so, will result in the growth of prediction errors. At present, assimilation methods being developed to improve morphological forecast reliability are producing encouraging results (e.g., [133][134][135][136][137]). For example, ad hoc data assimilation schemes and techniques using more re ned heuristic tuning of model state variables are being used to improve the performance of suspended sediment transport models (e.g., [138,139]).…”
Section: Improving Predictions and Reducing Uncertaintymentioning
confidence: 99%
“…Previous studies have demonstrated that hydrodynamic model parameter uncertainty has a significant effect on coastal simulations, e.g., on sediment transport (Briere et al, 2010), on water quality (Li et al, 2013), on nearshore currents and wave growth (Adrani and Kaihatu, 2012), on tidal propagation (Mayo et al, 2014), on tsunami generation and propagation (Knighton and Bastidas, 2015;Sraj et al, 2014), and on storm surge (Ferreira et al, 2014;Holt et al, 2015). Despite these findings, several recent studies on the validation of the Delft3D model have not considered potential effects of uncertainty in model parameter values, e.g., Elias et al (2000) and Golshani (2011).…”
Section: Surge and Wave Model Parameter Sensitivitymentioning
confidence: 99%
“…Smith et al, 2013;Mayo et al, 2014;Figure 1b). These techniques keep model parameters fixed and produce an updated model state that matches as closely as possible the true state by combining observational data with model predictions.…”
Section: Improving Predictions and Reducing Uncertaintymentioning
confidence: 99%
“…At present assimilation methods being developed to improve morphological forecast reliability are producing encouraging results (e.g. Scott and Mason, 2007;Smith et al, 2013;Chu et al, 2013;Margvelashvili et al, 2013;Mayo et al, 2014). For example ad hoc data assimilation schemes and techniques using more refine heuristic tuning of model state variables are being used to improve the performance of suspended sediment transport models (e.g.…”
Section: Improving Predictions and Reducing Uncertaintymentioning
confidence: 99%