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 formulation of the model and we perform a feasibility analysis in terms of computational cost and of algorithmic scalability. We use the scale-up factor which measure the performance gain in terms of time complexity reduction. We verify the reliability of the approach on a consistent test case (the Shallow Water Equations).
On the problem-decomposition of scalable 4D-Var Data Assimilation models / Arcucci, Rossella; D'Amore, Luisa; Carracciuolo, Luisa. - (2015), pp. 589-594. (Intervento presentato al convegno High Performance Computing & Simulation (HPCS), 2015 International Conference on tenutosi a Amsterdam nel 20-24 July) [10.1109/HPCSim.2015.7237097].
On the problem-decomposition of scalable 4D-Var Data Assimilation models
ARCUCCI, ROSSELLA;D'AMORE, LUISA;CARRACCIUOLO, LUISA
2015
Abstract
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 formulation of the model and we perform a feasibility analysis in terms of computational cost and of algorithmic scalability. We use the scale-up factor which measure the performance gain in terms of time complexity reduction. We verify the reliability of the approach on a consistent test case (the Shallow Water Equations).I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
