Parameter estimation for a cohesive sediment transport model by assimilating satellite observations in the Hangzhou Bay: Temporal variations and spatial distributions

Wang, Daosheng; Zhang, Jicai; He, Xianqiang; Chu, Dongdong; Lv, Xianqing; Wang, Ya Ping; Yang, Yang; Fan, Daidu; Gao, Shu

doi:10.1016/j.ocemod.2017.11.007

Cited by 18 publications

(19 citation statements)

References 85 publications

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“…The governing equation, boundary conditions, and initial conditions for the suspended cohesive sediment transport are as follows (Wang et al 2018a):…”

Section: B the Dynamical Modelmentioning

confidence: 99%

“…Equations ( 1)-( 9) are referred to as the dynamical model of the suspended cohesive sediment transport (i.e., the suspended cohesive sediment transport model), which will be used to constrain the gridded four-dimensional (both three-dimensional spatial and one-dimensional temporal) SSCs representing the interpolated results in this study. The numerical schemes for solving this dynamical model can be found in Wang et al (2018a).…”

Section: B the Dynamical Modelmentioning

confidence: 99%

“…The simulated results of the sediment transport are sensitive to the initial conditions (Wang et al 2018a); therefore, the accurate specification of the initial values of SSCs is important for the sediment transport simulation over a short period (Lee et al 2007;Yang et al 2016). In this study the initial conditions were obtained by interpolating the observed SSCs.…”

Section: Default Model Settings Of the Dynamical Modelmentioning

confidence: 99%

“…The data monitors the Kuroshio Extension with dynamically constrained synthesis of the acoustic tomography, satellite altimetry, and in situ data (Lebedev et al 2003), and in other studies, but it has not been used in the interpolation of the sparse SSC observations, which activates the present work. Wang et al (2018a) developed a three-dimensional sigma-coordinate suspended cohesive sediment transport model with the adjoint data assimilation, which has been used to investigate the parameter sensitivity (Wang et al 2018a) and to estimate the parameters by assimilating in situ observations (Wang et al 2017) or satellite observations (Wang et al 2018b). Based on this model, a dynamically constrained interpolation methodology (DCIM) for the sparsely observed SSCs in both space and time is developed and applied in the Bohai Sea in this study.…”

Section: Introductionmentioning

confidence: 99%

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Dynamically Constrained Interpolation of the Sparsely Observed Suspended Sediment Concentrations in Both Space and Time: A Case Study in the Bohai Sea

Mao

Wang

Zhang

et al. 2018

Journal of Atmospheric and Oceanic Technology

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The observed suspended sediment concentrations (SSCs) obtained from the water sampling are usually sparsely distributed in both space and time, which are traditionally applied just to calibrate other types of observations. In this study a dynamically constrained interpolation methodology (DCIM) is developed to interpolate these sparsely observed SSCs in the Bohai Sea. In this method the suspended sediment transport model is taken as dynamical constraints to interpolate the observations. Meanwhile, the interpolated results are optimized iteratively by adjusting the key model parameters using the adjoint method. The DCIM is first verified using the synthetic observations produced by twin model runs. The modeling results reveal that this method is effective at interpolating the sparsely observed artificial SSCs, even when the observations are heavily contaminated by data noise. Then, the sparsely observed practical SSCs obtained from a large area survey in the Bohai Sea are interpolated using the DCIM. The interpolated results are verified by randomly selected independent observations. The discrepancies between the interpolated SSCs and the observations are significantly decreased. When all the observations are interpolated, the final interpolated SSCs captured a majority (96.88%) of observations with a factor of 2 and the correlation coefficient between the observed and interpolated SSCs is 0.98. Besides, the interpolated results have presented the reasonable dynamical variations of SSCs in the space and time domains. The modeling results indicate that the DCIM is an effective tool for interpolating the sparsely observed SSCs in both space and time.

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“…The governing equation, boundary conditions, and initial conditions for the suspended cohesive sediment transport are as follows (Wang et al 2018a):…”

Section: B the Dynamical Modelmentioning

confidence: 99%

Section: B the Dynamical Modelmentioning

confidence: 99%

Section: Default Model Settings Of the Dynamical Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dynamically Constrained Interpolation of the Sparsely Observed Suspended Sediment Concentrations in Both Space and Time: A Case Study in the Bohai Sea

Mao

Wang

Zhang

et al. 2018

Journal of Atmospheric and Oceanic Technology

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“…Data assimilation is an effective method to make the best of limited observations, and has been widely used in meteorological and oceanographic predictions in recent years. With the development of data assimilation technology, an advanced data assimilation method which involves the adjoint technique is used to optimize and estimate many parameters of numerical models (Dimet and Talagrand, 1986;Zhang and Lu, 2008;Li et al, 2013;Chen et al, 2014;Wang et al, 2018). As a result, an 'optimal' initial condition can be obtained through applying the adjoint method to assimilating observation data (Peng and Xie, 2006).…”

Section: Introductionmentioning

confidence: 99%

Improvement of the ocean pollutant transport model by using the surface spline interpolation

Zong

Xu³

et al. 2018

Tellus A: Dynamic Meteorology and Oceanography

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The independent point scheme (IPS) is applied to inverting initial condition with the adjoint method for the ocean pollutant transport model in this work. As an improvement, the linear Cressman interpolation is removed and the surface spline interpolation is implemented in the IPS. A series of numerical experiments are carried out to test and compare the improved IPS. And experiment results show that through applying the improved IPS, what is further reduced is mean absolute errors between simulation results and observations. Moreover, the inverted distributions are more smooth, accurate and reasonable. In addition, the application of improved IPS also reduces the variables that need to be inverted and promotes the computational efficiency. By these numerical experiment results, it is demonstrated that the combination of improved IPS and adjoint method can be used for the inversion of initial conditions and parameters estimation more effectively and reliably.

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A dynamic 2DH flocculation model for coastal domains

Escobar

Fettweis

et al. 2023

Ocean Dynamics

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Parameter estimation for a cohesive sediment transport model by assimilating satellite observations in the Hangzhou Bay: Temporal variations and spatial distributions

Cited by 18 publications

References 85 publications

Dynamically Constrained Interpolation of the Sparsely Observed Suspended Sediment Concentrations in Both Space and Time: A Case Study in the Bohai Sea

Dynamically Constrained Interpolation of the Sparsely Observed Suspended Sediment Concentrations in Both Space and Time: A Case Study in the Bohai Sea

Improvement of the ocean pollutant transport model by using the surface spline interpolation

A dynamic 2DH flocculation model for coastal domains

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