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Identifier 000402393
Title Data assimilation methods
Alternative Title Μέθοδοι αφομοίωσης δεδομένων
Author Σφακιανάκη, Γεωργία
Thesis advisor Πλεξουσάκης, Μιχαήλ
Reviewer Μπάγκαβος, Δημήτριος
Κοσσιώρης, Γεώργιος
Abstract Data Assimilation (DA) methods have been extensively used the past decades in many fields of science, among which meteorology, hydrology and oceanography, to mention just a few. Generally speaking, data assimilation is a technique to combine past knowledge of the system, in the form of a numerical model, and information about the system’s state, in the form of observations. Two main categories of DA methods can be recognized, variational and sequential. Variational methods are based on optimal control theory and the aim is to minimize a given cost function that measures the model-to-data misfit. In sequential methods, on the other hand, observations are assimilated as soon as they become available. In this thesis we study and present two methods from each family. The Three-Dimensional Variational assimilation (3D-Var) and the Four-Dimensional Variational assimilation methods (4D-Var) fall into the first category. The most well-known DA method in the sequential family is the Kalman Filtering, and we focus on two of its variants, the Extended and the Ensemble Kalman Filtering. The methods presented in our work are viewed under the prism of numerical weather prediction. In this context, data assimilation is used to produce an analysis of the current state of the atmosphere to be used as initial conditions in a subsequent weather forecast, leading to more accurate predictions. We provide implementations and numerical results for the 3D-Var and Ensemble Kalman Filtering methods applied to the Lorenz-96 model. In the frame of weather forecasting, we also present the Weather Research and Forecasting (WRF) model which is a state-of-the-art atmospheric modeling system designed for numerical weather prediction and is currently being used in operational centers. We focus on the WRF Data Assimilation (WRFDA) module that includes implementations for the 3D-Var and 4DVar methods, as well as a hybrid scheme between the variational and ensemble assimilation methods.
Language English
Subject 3Dvar
Kalman filter
Optimal interpolation
Issue date 2016-07-22
Collection   Faculty/Department--Faculty of Sciences and Engineering--Department of Mathematics and Applied Mathematics--Post-graduate theses
  Type of Work--Post-graduate theses
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