DATA ASSIMILATION STUDY USING THE DUTCH CONTINENTAL SHELF MODEL WITH FULL MEASUREMENT

Abstract The operational sea water level prediction system in the Netherlands is based on the decomposition of water level into the astronomical tides and the surge. While the astronomical tides are analysed and predicted by using Harmonic Analysis, the surge is predicted by using numerical hydrodynamics model named Dutch Continental Shelf Model, DCSM. Using this approach the nonlinear interaction between the two components is not well accomodated. Moreover, the performance of the system based on this decomposition now seems to have reached its limit. In attempt to further improve the prediction we are going to apply data assimilation using the DCSM model with the full water level measurements without any decomposition. In the first step of the study we use the steady-state Kalman filter as the method for data assimilation. As the success of data assimilation depends largely on the error representation, we also work with a new error representation for the open boundary condition. In the new error representation, a colored noise process, modelled using AR(1), is assigned to each harmonic parameters defining the water level boundary conditions. This choice is made based on the fact that the harmonic parameters of the astronomical tides are in fact slowly varying in time

Hydrodynamic modelling and model sensitivities to bed roughness and bathymetry offset in a micro-tidal estuary

Tidal estuaries support everyday functions for over 80% of Australia’s population living within 50 km of the coastline and thus come under immense pressure of physicochemical changes. Most studies in estuarine applications have used the bed roughness as the single calibration parameter to calibrate hydrodynamic modelling, yet errors in bathymetric data can significantly impose uncertainties into the model outputs. In this study, we evaluated the sensitivity of a hydrodynamic model of a micro-tidal estuary to both the bed roughness and bathymetry offset through comparing observed and modelled water level and velocity. Treating both bathymetry offset and bed roughness as calibration parameters, three calibration scenarios were tested to examine the impact of these parameters. To validate the model, Lagrangian drifter data as a new dataset in shallow estuaries were used. The analysis shows that model outputs are more sensitive to the variation of bathymetry offset than bed roughness. Results show that calibrating the bathymetry offset alone can significantly improve model performance. Simultaneous calibration of both parameters can provide further improvement, particularly for capturing the water level. Drifter and modelled velocities are highly correlated during flood tides, whereas the correlation is low for slack water because of wind-induced current on drifters.</p

Impact of sensor location on assimilated hydrodynamic model performance

A data assimilation (DA) framework assessed with an observing system simulation experiment (OSSE) can ensure reliable predictions for a water body, yet its application is very limited in shallow estuaries. In this study, we implemented an ensemble-based DA system to improve the accuracy of a hydrodynamic model of a micro-tidal estuary. Synthetic water level and velocity data were assimilated into the model in both single and dual variable DA forms. To evaluate the sensitivity of DA performance to the location of the hypothetical water level and velocity sensors, an OSSE assessment was used. Results revealed that DA performance is significantly sensitive to location of velocity observations, while relatively insensitive to location of water level observations. Our analysis suggests that the assimilation of velocity data at a location close to the downstream boundary (i.e., water level boundary) can result in a significant improvement in model estimates

Assessment of an ensemble-based data assimilation system for a shallow estuary

Data assimilation (DA) is an essential element for the next generation of operational forecast systems for estuaries, to improve estuarine management. With limited resources and prohibitive cost to collect observations for such system, sensor choice and location is of prime importance in improving hydrodynamic model performance. In this study, we examine an optimal ensemble-based DA platform for improving the hydrodynamic modelling of a shallow estuary. Using an ensemble Kalman filter (EnKF), a set of synthetic (twin) experiments was conducted to test different DA scenarios covering observation types (i.e. water level and velocity) and noise modelling. We also evaluated the impact of the observation location on the DA performance by performing an observing system simulation experiment (OSSE). Results revealed that the assimilation of a single variable can significantly enhance the accuracy of the variable being assimilated, while the level of improvement for another variable is smaller. However, the best model estimates were obtained via a multivariate EnKF (i.e. both observations are assimilated). EnKF was robust to under and overestimation of the model errors, although overestimation led to slightly greater improvements. Our analysis showed that model performance is more sensitive to velocity observation location, rather than water level. These findings suggest that locations with strong velocity gradients are the locations where the hydrodynamic model needs to be enhanced, and accordingly, they are the preferable locations to deploy a velocity sensor.</p

Assimilation of GPS-tracked drifter data to improve the Eulerian velocity fields in an estuary

Numerical models are invaluable for the provision of real-time and forecasting information that can be used to examine estuarine hydrodynamics, particularly during times of flood or contaminant release. However, model outputs are associated with uncertainty; this necessitates the use of data assimilation (DA) techniques to improve model accuracy. We used an open-source DA tool to effectively assimilate Lagrangian drifter data into an estuarine hydrodynamic model using an ensemble Kalman filter (EnKF) algorithm. Our aims were to (i) evaluate the potential of drifter data for improving the accuracy of model estimates, and (ii) reduce the challenge and programming effort required for assimilation of such datasets, to make this technique accessible, for a broader range of users. We showed that assimilation of Lagrangian data obtained from prompt deployment of drifters in estuaries can lead to significant improvement (here, up to 54%) in modelled velocity fields.</p

Application of data assimilation for improving forecast of water levels and residual currents in Singapore regional waters

10.1007/s10236-012-0584-yOcean Dynamics63143-6