Master thesis title: Data assimilation toolbox for Matlab
Master Program: Master in Mathematical Engineering
University: KU Leuven
Academic year : 2012-2013

Author: Wannes Van den Bossche
Supervisor: Dr. Ir. Oscar Mauricio Agudelo
Promotor: Prof. Dr. Ir. Bart De Moor

Reference:
W. Van den Bossche, "Data assimilation toolbox for Matlab", Master Thesis (Supervisors: Oscar Mauricio Agudelo, Bart De Moor), Faculty of Engineering, KU Leuven, 2013, 103 p.

"Note: The code is not being maintained"

Data assimilation is the common name given to the techniques that combine numerical models and measurements in order to obtain an improved estimation of the state of a system. In data assimilation it is assumed that both models and measurements are subject to uncertainties that can be defined as a statistical distribution. The goal of data assimilation is to combine the knowledge of models, measurements and uncertainties to obtain a better estimation than either the measurements or the models alone can provide. The application of this technique arises in many fields such as weather forecasting, oceanography, space weather forecasting and air quality.

Although data assimilation is a highly active research domain, Matlab does not have an official data assimilation toolbox. This is why the goal of this thesis has been to develop a generic data assimilation toolbox for Matlab with at least five data assimilation schemes to improve the estimations of any given model as defined by the user. Not only is this initial goal achieved, it has been extended by providing a wider range of data assimilation schemes together with several possibilities to configure noise models and state space models. In addition, several tools to analyze the acquired data assimilation results are incorporated. By applying the Matlab object-oriented programming approach, the toolbox not only provides the required generic behaviour, but also maintains a structured framework that can be easily extended with new algorithms and classes. Furthermore, the interface of the toolbox is intuitively straightforward since it resembles the ones of official Matlab toolboxes.

The toolbox currently contains data assimilation techniques that range from the regular Kalman filter up to particles filters. More specifically the data assimilation toolbox is equipped with the following techniques: the Kalman Filter (KF), the Extended Kalman Filter (EKF), the Unscented Kalman Filter (UKF), the Ensemble Kalman Filter (EnKF), the Deterministic Ensemble Kalman Filter (DEnKF), the Ensemble Transform Kalman Filter (ETKF), the Ensemble Square Root Filter (EnSRF), the Optimal Interpolation (OI) technique, the Generic Particle filter (GEN), the Sampling Importance Resampling (SIR) Particle filter and the Auxiliary Sampling Importance Resampling (ASIR) Particle filter.

Check Appendix A of the document/file "manuscript_user_guide.pdf".

Check Chapters 3 and 4 of the document/file "manuscript_user_guide.pdf".