Rossella Arcucci

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Data assimilation (DA) is a methodology for combining mathematical models simulating complex systems (the background knowledge) and measurements (the reality or observational data) in order to improve the estimate of the system state (the forecast). The DA is an inverse and ill posed problem usually used to handle a huge amount of data, so, it is a large(More)
We present an innovative approach for solving Four Dimensional Variational Data Assimilation (4D-VAR DA) problems. The approach we consider starts from a decomposition of the physical domain; it uses a partitioning of the solution and a modified regularization functional describing the 4D-VAR DA problem on the decomposition. We provide a mathematical(More)
OceanVar is a Data Assimilation (DA) software which is being used in Italy within the Mediterranean Forecasting System (MFS) to combine observational data (Sea level anomaly, sea-surface temperatures, etc.) with backgrounds produced by computational models of ocean currents for the Mediterranean Sea (namely, the NEMO framework). OceanVAR is based on a(More)
We introduce a decomposition of the Tikhonov Regularization (TR) functional which split this operator into several TR functionals, suitably modified in order to enforce the matching of their solutions. As a consequence, instead of solving one problem we can solve several problems reproducing the initial one at smaller dimensions. Such approach leads to a(More)
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