Forecasting hydrodynamics using data assimilation: a case study in the North Sea
Mol, M.A. (2021). Forecasting hydrodynamics using data assimilation: a case study in the North Sea. MSc Thesis. Delft University of Technology/Deltares: Delft. viii, 106 pp.
The need for accurate estimation of hydrodynamic and water quality model variables arises from the UNITED project, which aims to create high-resolutional forecasting systems for monitoring the cultivation of seaweed and flat oysters and operating of the Belgian pilot of UNITED in the coastal area of the North Sea. Accurate observations of physical variables are usually only known for small domains of the model, on the water surface. Therefore, data assimilation is applied on different hydrodynamic models to search for improved estimates of waterlevel, water velocity and temperature. The sequential data assimilation algorithm, the Ensemble Kalman filter is considered. The algorithm is a Monte-Carlo approximation of the Kalman filter, both using accurate observations assimilated into the model, where the model each time is shifted towards the observations when available, providing an optimal trade-off estimation between the model estimations and observations. Two models are considered: a 1D model of the Western Scheldt estuary in Python and the 2D model with depth of the Western Scheldt estuary implemented with software packages Delft3D FM and OpenDA. Model estimates and estimates of model with assimilated observations are investigated in various experiments to look for improved predictions, using twin experiments to generate artificial observations over the whole domain. The various experiments contained the search for the effect of assimilation location on the estimates, of the effect of assimilation of different physical parameters, the effect of the observational error covariance and the effect of water surface assimilation on the estimation within the water column. In most cases, the Ensemble Kalman filter improved the estimate of waterlevel and water velocity with varying results up to a decrease in rmse of a factor four, while the assimilation of temperature gave a worse prediction. Furthermore, assimilation of waterlevel gave the best improvement in estimate. For the investigated models, assimilation of observations near the boundary conditions with implied noise gave the most improved estimates. Furthermore, assimilation of water surface observations improved the estimate within the water column for water velocity with a factor of 2, while temperature assimilation did not show any improvement. The result for temperature may be due to a collapsing Ensemble Kalman filter due to small standard deviations and a not realistic model setup for temperature. Therefore, assimilation of only observations at the surface may be used to accurately improves water column estimations for water velocity. For temperature, more research is needed. Advised is an experiment with a different model setup with more realistic and longer spin-up time of temperature. The collapsing Ensemble Kalman filter can be prevented by initialization of new ensembles before the the collapse happens. In further research, data assimilation can be applied on the DCSM model, a model involving the area of the Belgian pilot.
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