Parameter sensitivity of three Kalman filter schemes for assimilation of water levels in shelf sea models

Sørensen, Jacob Viborg Tornfeldt; Madsen, Henrik

doi:10.1016/j.ocemod.2005.03.002

Cited by 12 publications

(17 citation statements)

References 18 publications

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“…SSKF has also been proven efficient in many applications (e.g. Heemink 1990;Canizares et al 2001;Verlaan et al 2005;Sørensen et al 2006;El Serafy and Mynett 2008;Karri et al 2014). Successful application of SSKF requires (i) a stationary observational network, (ii) a nearly linear process model and (iii) a stationary system error covariance.…”

Section: Steady-state Kalman Filtermentioning

confidence: 99%

Application of data assimilation for improved operational water level forecasting on the northwest European shelf and North Sea

2015

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For the Netherlands, accurate water level forecasting in the coastal region is crucial, since large areas of the land lie below sea level. During storm surges, detailed and timely water level forecasts provided by an operational storm surge forecasting system are necessary to support, for example, the decision to close the movable storm surge barriers in the Eastern Scheldt and the Rotterdam Waterway. In the past years, a new generation operational tide-surge model (Dutch Continental Shelf Model version 6) has been developed covering the northwest European continental shelf. In a previous study, a large effort has been put in representing relevant physical phenomena in this process model as well as reducing parameter uncertainty over a wide area. While this has resulted in very accurate water level representation (root-meansquare error (RMSE) ∼7-8 cm), during severe storm surges, the errors in the meteorological model forcing are generally non-negligible and can cause forecast errors of several decimetres. By integrating operationally available observational data in the forecast model by means of real-time data assimilation, the errors in the meteorological forcing are prevented from propagating to the hydrodynamic tide-surge model forecasts. This paper discusses the development of a computationally efficient steady-state Kalman filter to enhance the predictive quality for the shorter lead times by improving the system state at the start of the forecast. Besides evaluating the model quality against shelf-wide tide gauge observations for a year-long hindcast simulation, the predictive value of the Kalman filter is determined by comparing the forecast quality for various lead time intervals against the model without a steady-state Kalman filter. This shows that, even though the process model has a water level representation that is substantially better than that of other comparable operational models of this scale, substantial improvements in predictive quality in the first few hours are possible in an actual operational setting.

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Section: Steady-state Kalman Filtermentioning

confidence: 99%

Application of data assimilation for improved operational water level forecasting on the northwest European shelf and North Sea

2015

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“…Sorensen et al (2004) have also applied data assimilation in hydrodynamic modeling to deal with nonlinearlity and bias. Some people have made use of data assimilation in hydrology with a focus on uncertainty and sensitivity analysis (Liu and Gupta 2007;Sorensen et al 2006;Van Geer et al 1991;Vrugt et al 2005a). Data assimilation methods have also been used in the petroleum industry (Oliver et al 2008).…”

Section: Introductionmentioning

confidence: 99%

Using data assimilation method to calibrate a heterogeneous conductivity field conditioning on transient flow test data

Tong

Yang

2010

Stoch Environ Res Risk Assess

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A data assimilation method is developed to calibrate a heterogeneous hydraulic conductivity field conditioning on transient pumping test data. The ensemble Kalman filter (EnKF) approach is used to update model parameters such as hydraulic conductivity and model variables such as hydraulic head using available data. A synthetical two-dimensional flow case is used to assess the capability of the EnKF method to calibrate a heterogeneous conductivity field by assimilating transient flow data from observation wells under different hydraulic boundary conditions. The study results indicate that the EnKF method will significantly improve the estimation of the hydraulic conductivity field by assimilating continuous hydraulic head measurements and the hydraulic boundary condition will significantly affect the simulation results. For our cases, after a few data assimilation steps, the assimilated conductivity field with four Neumann boundaries matches the real field well while the assimilated conductivity field with mixed Dirichlet and Neumann boundaries does not. We found in our cases that the ensemble size should be 300 or larger for the numerical simulation. The number and the locations of the observation wells will significantly affect the hydraulic conductivity field calibration.

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“…A standard performance measure of data assimilation schemes is the root mean square error (RMSE) between the true and assimilated or perturbed solutions in a twin experiment [31]. To quantitatively represent the capability of KF or HF in handling the estimation error, we use the RMSE for each soil layer as the indicator:…”

Section: Performance Indicatormentioning

confidence: 99%

“…This is related to the robustness of data assimilation technique to error nature. Several studies have investigated the robustness issue of KF [21,31]. These studies showed that the KF was quite robust with respect to state estimation, but the specification of error statistics had a severe impact on the covariance estimate.…”

Section: Introductionmentioning

confidence: 99%

Robust data assimilation in hydrological modeling – A comparison of Kalman and H-infinity filters

Wang

Cai

2008

Advances in Water Resources

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Parameter sensitivity of three Kalman filter schemes for assimilation of water levels in shelf sea models

Cited by 12 publications

References 18 publications

Application of data assimilation for improved operational water level forecasting on the northwest European shelf and North Sea

Application of data assimilation for improved operational water level forecasting on the northwest European shelf and North Sea

Using data assimilation method to calibrate a heterogeneous conductivity field conditioning on transient flow test data

Robust data assimilation in hydrological modeling – A comparison of Kalman and H-infinity filters

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