Abstract
To understand and predict the behavior of a system one can use measurements or one can develop physically based numerical models. In many applications however neither of these approaches is able to provide an accurate description of the dynamic behavior of the system. A model is always a simplification of the real world while measurements seldom produce a complete picture of the system behaviour. Using data assimilation techniques measurements and model results are both used to obtain an optimal estimate of the state of the system. In this article we present an overview of methods available to assimilate data into a numerical model. Attention is concentrated on variational methods and on Kalman filtering. The main problem of using these advanced data assimilation schemes is the huge computational burden that is required for solving real life problems. For variational methods the adjoint model implementation is essential to obtain an efficient data assimilation algorithm. For Kalman filtering problems a number of approximate algorithms have been introduced recently: Ensemble Kalman filters and Reduced Rank filters. These algorithms make the application of Kalman filtering to large-scale data assimilation problems feasible. After a brief introduction to the most important data assimilation approaches we will discuss the advantages and disadvantages of the various methods and present a number of real life applications.
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Heemink, A.W., Hanea, R.G., Sumihar, J., Roest, M., Velzen, N., Verlaan, M. (2009). Data Assimilation Algorithms for Numerical Models. In: Koren, B., Vuik, K. (eds) Advanced Computational Methods in Science and Engineering. Lecture Notes in Computational Science and Engineering, vol 71. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03344-5_5
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