Abstract
Two strategies for estimating open boundary conditions (OBCs) with adjoint method are compared by carrying out semi-idealized numerical experiments. In the first strategy, the OBC is assumed to be partly space varying and generated by linearly interpolating the values at selected feature points. The advantage is that the values at feature points are taken as control variables so that the variations of the curves can be reproduced by the minimum number of points. In the second strategy, the OBC is assumed to be fully space varying and the values at every open boundary points are taken as control variables. A series of semi-idealized experiments are carried out to compare the effectiveness of two inversion strategies. The results demonstrate that the inversion effect is in inverse proportion to the number of feature points which characterize the spatial complexity of open boundary forcing. The effect of ill-posedness of inverse problem will be amplified if the observations contain noises. The parameter estimation problems with more control variables will be much more sensitive to data noises, and the negative effects of noises can be restricted by reducing the number of control variables. This work provides a concrete evidence that ill-posedness of inverse problem can generate wrong parameter inversion results and produce an unreal “good data fitting.”
Highlights
The tides and tidal currents are the basic motion forms of ocean water and play an important role in the research on other processes, such as the storm surge, the circulation and the estuarine dynamics [1, 2]
The feature points are selected by calculating the second-order derivatives of discrete curves and the values at selected feature points are taken as control variables to be estimated
It is necessary and helpful to perform identical semi-idealized experiments in order to find the optimal choices for the number of control variables and inversion strategy
Summary
The tides and tidal currents are the basic motion forms of ocean water and play an important role in the research on other processes, such as the storm surge, the circulation and the estuarine dynamics [1, 2]. Among all the data assimilation methods, the 4DVAR is one of the most effective and powerful approaches It is based on the optimal control methods and perturbation theory [8, 9]. This technique allows us to retrieve an optimal data for a given model from heterogeneous observation fields [9]. It is an advanced data assimilation method which involves the adjoint method 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. Navon [10] presented an important overview on the state of the art of parameter estimation in meteorology and oceanography in view of application of Abstract and Applied Analysis
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
