Study on linear and nonlinear bottom friction parameterizations for regional tidal models using data assimilation

Zhang, Jicai; Lü, Xianqing; Wang, Ping; Wang, Ya Ping

doi:10.1016/j.csr.2010.12.011

Cited by 57 publications

(54 citation statements)

References 30 publications

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“…The tidal level will decrease with the spread of tide landward ( Lian and Zhao, 1995;Wang et al, 1995) , due to the influence of friction of beach surface ( Kamphuis, 1978 ) , and the coefficient of friction increases with decreasing depth ( Yu and Li, 2002;Zhang, Lv, and Sun, 2012 ) . But the differences of the tidal level at different locations are neglected in the single-well method.…”

Section: Comparison With the Single-well Methodsmentioning

confidence: 99%

Seawater-groundwater Exchange in a Silty Tidal Flat in the South Coast of Laizhou Bay, China

Hou

Chen

et al. 2016

Journal of Coastal Research

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Section: Comparison With the Single-well Methodsmentioning

confidence: 99%

Seawater-groundwater Exchange in a Silty Tidal Flat in the South Coast of Laizhou Bay, China

Hou

Chen

et al. 2016

Journal of Coastal Research

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“…Generally, K m should be the inverse of the covariance matrix of observation errors, which can be simplified by assuming that observation errors are uncorrelated and equally weighted. In this study, K m is set as a unit matrix (Zhang and Lu 2010).…”

Section: B Adjoint Model (Inverse Model)mentioning

confidence: 99%

“…Among all the data assimilation methods, 4DVAR is one of the most effective and powerful approaches (Kazantsev 2012). As one of 4DVAR, the adjoint method is an advanced data assimilation technique and has the advantage of directly assimilating various observations distributed in time and space into numerical models, while maintaining dynamical and physical consistency with the model (Zhang and Lu 2010). The adjoint method is a powerful tool for parameter estimation (Navon et al 1992;Navon 1998).…”

Section: Introductionmentioning

confidence: 99%

A Method on Estimating Time-Varying Vertical Eddy Viscosity for an Ekman Layer Model with Data Assimilation

Zhang

et al. 2019

J. Atmos. Oceanic Technol.

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Temporal vertical eddy viscosity coefficient (VEVC) in an Ekman layer model is estimated using an adjoint method. Twin experiments are carried out to investigate the influences of several factors on inversion results, and the conclusions of twin experiments are 1) the adjoint method is a capable method to estimate different kinds of temporal distributions of VEVCs; 2) the gradient descent algorithm is better than CONMIN and L-BFGS for the present problem, although the posterior two algorithms perform better on convergence efficiency; 3) inversion results are sensitive to initial guesses; 4) the model is applicable to different wind conditions; 5) the inversion result with thick boundary layer depth (BLD) is slightly better than thin BLD; 6) inversion results are more sensitive to observations in upper layers than those in lower layers; 7) inversion results are still acceptable when data noise exists, indicating the method can sustain noise to a certain degree; 8) a regularization method is proved to be useful to improve the results for present problem; and 9) the present method can tolerate the existence of balance errors due to the imperfection of governing equations. The methodology is further validated in practical experiments where Ekman currents are derived from Bermuda Testbed Mooring data and assimilated. Modeled Ekman currents coincide well with observed ones, especially for upper layers. The results demonstrate that the assumptions of depth dependence and time dependence are equally important for VEVCs. The feasibility of the typical Ekman model, the imperfection of Ekman balance equations, and the deficiencies of the present method are discussed. This method provides a potential way to realize the time variations of VEVCs in ocean models.

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“…Nowadays, there seems to be no secrets about the twodimensional tides, as many numerical models (Zu et al 2008;Rosenfeld et al 2009;Wijeratne et al 2012;Wang et al 2013; and reference therein) and inverse models (Egbert and Erofeeva 2002;Zhang et al 2011;Cao et al 2012; and references therein) have been applied to the simulation of global and regional tides. However, for the simultaneous simulation of multiple constituents, no conclusion has been drawn about the optimum length of time series (TS) for harmonic analysis (HA).…”

Section: Introductionmentioning

confidence: 99%

Harmonic Analysis in the Simulation of Multiple Constituents: Determination of the Optimum Length of Time Series

Cao

Wang

2015

Journal of Atmospheric and Oceanic Technology

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To investigate the optimum length of time series (TS) for harmonic analysis (HA) in the simulation of multiple constituents, a two-dimensional tidal model is used to simulate the M 2 , S 2 , K 1 , and O 1 constituents in the Bohai and Yellow Seas. By analyzing the HA results of several nonoverlapping TS of the same length, which varies from 15 to 365 days, a field-average deviation of HA results is calculated. A deviation that is sufficiently small means that HA results are independent of the choice of TS, and the corresponding TS length is regarded as the optimum. Results indicate that the range of 180-195 days is the optimum length of TS for HA in the simulation of the four principal constituents. To investigate what determines the optimum length, experiments with different computed area and model settings are carried out. Results indicate that the optimum length is independent of advection, nodal corrections, and computed area, and only depends on bottom friction. Nonlinear bottom friction results in the appearance of higher harmonics and explains why the optimum length of TS for HA is 180-195 days.

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Study on linear and nonlinear bottom friction parameterizations for regional tidal models using data assimilation

Cited by 57 publications

References 30 publications

Seawater-groundwater Exchange in a Silty Tidal Flat in the South Coast of Laizhou Bay, China

Seawater-groundwater Exchange in a Silty Tidal Flat in the South Coast of Laizhou Bay, China

A Method on Estimating Time-Varying Vertical Eddy Viscosity for an Ekman Layer Model with Data Assimilation

Harmonic Analysis in the Simulation of Multiple Constituents: Determination of the Optimum Length of Time Series

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