A Study of the Transport of Marine Pollutants Using Adjoint Method of Data Assimilation with Method of Characteristics

Li, Xiaona; Xu, Minjie; Lv, Xianqing; Fu, Kai

doi:10.1155/2018/7408263

Cited by 3 publications

(4 citation statements)

References 25 publications

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“…On the basis of cost function, the process of adjusting control variables can be conducted. The cost function, a quantitative metric to measure the mentioned error, is defined as [24,30]:J(P)=12∫Τ×ΩW(P−Po)2dΤdΩ where Τ and Ω are the symbols of time domain and space domain, respectively; W represents the weighting matrix and its elements equal to 1 when the observations are available, and 0 otherwise; P and P o denote the simulation results and observations of phytoplankton, respectively.…”

Section: Methodsmentioning

confidence: 99%

“…Yaremchuk et al [23] proposed a hybrid program in the adjoint-free-4-dimensional data assimilation method, which searched in the subspace formed by an ensemble of directions, and its advantage was proved through experiments. The adjoint assimilation method can also be used in the marine pollutant transport model to simulate the temporal and spatial distribution of pollution [24].…”

Section: Introductionmentioning

confidence: 99%

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Application of the Spline Interpolation in Simulating the Distribution of Phytoplankton in a Marine NPZD Type Ecosystem Model

Zheng

2019

IJERPH

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The available observations for the model are usually sparse and uneven. The application of interpolation methods help researchers obtain an approximate form of the original data. A marine nutrient, phytoplankton, zooplankton and detritus (NPZD) type ecosystem model is applied to simulate the distribution of phytoplankton combined with the spline interpolation (SI) and the Cressman interpolation (CI). In the idealized twin experiments, the performance of these two interpolation methods is validated through the analysis of several quantitative metrics, which show the minor error and high efficiency when using the SI. Namely, the given distributions can be better inverted with the SI. The actual distribution of phytoplankton in the Bohai Sea is interpolated in the practical experiment, where a satisfactory simulation result is obtained by the model with the SI. The model experiments and results verify the feasibility and effectiveness of SI.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of the Spline Interpolation in Simulating the Distribution of Phytoplankton in a Marine NPZD Type Ecosystem Model

Zheng

2019

IJERPH

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“…In the aspect of reconstructing the original eld in the case of undersampled, a series of studies have been carried out and many theoretical methods have been also put forward, such as ordered-subsets expectation-maximization method [17], Least squares tting algorithm [18,19], NURBS curve tting method [20,21], B-spline curve tting method [22,23], etc. Because the B-spline curve method has excellent properties such as geometric invariance, convex hull, and local support, this method is o en used in scienti c research and engineering applications, such as data analysis and distribution reconstruction [24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of Velocity Distribution in Partially-Filled Pipe Based on Non-Uniform Under-Sampling

Jiang

Qing-min

Ding

2020

Advances in Mathematical Physics

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In the process of research on the flow velocity distribution in a partially filled pipe, the under-sampling of measurement data often occurs. For the first time, this problem is solved by the improved non-uniform B-spline curve fitting approximation (NBSC) method. The main innovation of this method is to reconstruct the flow velocity distribution fitting curve with a small amount of non-uniform feature points containing flow velocity information. First, the curvature of a whole discrete sampled data is analyzed, then the weighted threshold is set, and the sampled points that satisfy the threshold are extracted as the initial velocity distribution feature points. Next the node vectors were constructed according to the initial feature points, and the initial interpolation fitting curves are generated. Secondly, by using the relative deviation between the initial approximation curve and each sampled point, new feature points were added where the curve allowable deviation exceeded the specified tolerance, and then a new interpolation fitting curve was obtained. The above procedure was repeated until the fitting curve reached expected accuracy, thus the appropriate feature points were determined. Experimental results showed that, in the case of the same approximation deviation, the proposed NBSC method can solve the problem of under-sampling of measurement data better.

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“…A three-dimensional numerical model of gravity flows was introduced in the research of Huang et al [12], which was used to investigate the distribution features of various pollutants discharged at different positions in a wide river. Li et al [13] simulated the temporal and spatial distribution of pollutants of the Bohai Sea in twin experiments with the adjoint assimilation method.…”

Section: Introductionmentioning

confidence: 99%

Application of Dynamically Constrained Interpolation Methodology to the Surface Nitrogen Concentration in the Bohai Sea

Zheng

2019

IJERPH

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Observations of ocean pollutants are usually spatiotemporally dispersive, while it is of great importance to obtain continuous distribution of ocean pollutants in a certain area. In this paper, a dynamically constrained interpolated methodology (DCIM) is proposed to interpolate surface nitrogen concentration (SNC) in the Bohai Sea. The DCIM takes the pollutant transport advection diffusion equation as a dynamic constraint to interpolate SNCs and optimizes the interpolation results with adjoint method. Feasibility and validity of the DCIM are testified by ideal twin experiments. In ideal experiments, mean absolute gross errors between interpolated observations and final interpolated SNCs are all no more than 0.03 mg/L, demonstrating that the DCIM can provide convincing results. In practical experiment, SNCs are interpolated and the final interpolated surface nitrogen distribution is acquired. Correlation coefficient between interpolated and observed SNCs is 0.77. In addition, distribution of the final interpolated SNCs shows a good agreement with the observed ones.

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A Study of the Transport of Marine Pollutants Using Adjoint Method of Data Assimilation with Method of Characteristics

Cited by 3 publications

References 25 publications

Application of the Spline Interpolation in Simulating the Distribution of Phytoplankton in a Marine NPZD Type Ecosystem Model

Application of the Spline Interpolation in Simulating the Distribution of Phytoplankton in a Marine NPZD Type Ecosystem Model

Reconstruction of Velocity Distribution in Partially-Filled Pipe Based on Non-Uniform Under-Sampling

Application of Dynamically Constrained Interpolation Methodology to the Surface Nitrogen Concentration in the Bohai Sea

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