L. D’Amore1 , R. Arcucci2 , L. Marcellino3 and A. Murli2
1. Department of Mathematics and Application, University of Naples Federico II, Italy.
2. Centro Euro-Mediterraneo per i Cambiamenti Climatici (CMCC), Italy.
3. Department of Applied Sciences, University of Naples Parthenope, Italy.
Received 30 January, 2012; accepted in revised form 22 December, 2012
Abstract: The most significant features of Data Assimilation (DA) are that both the models
and the observations are very large and non-linear (of order at least O(108 )). Further, DA
is an ill-posed inverse problem. Such properties make the numerical solution of DA very
difficult so that, as stated in [19], ”solving this problem in ”real-time” it is not always pos-
sible and many different approximations to the basic assimilation schemes are employed”.
Thus, the exploitation of advanced computing environments is mandatory, reducing the
computational cost to a suitable turnaround time. This activity should be done according
to a co-design methodology where software requirements drive hardware design decisions
and hardware design constraints motivate changes in the software design to better fit within
those constraints.
In this paper, we address high performance computation issues of the three dimensional
DA scheme underlying the oceanographic 3D-VAR assimilation scheme, named Ocean-
VAR, developed at CMCC (Centro Euro Mediterraneo per i Cambiamenti Climatici), in
Italy. The aim is to develop a parallel software architecture which is able to effectively
take advantage of the available high performance computing resources.
c 2012 European Society of Computational Methods in Sciences, Engineering and Technology
keywords: Data Assimilation, Inverse Problem, Parallel Software, Oceanography
MSC: 65Y05, 65F22, 65Z05
PACS: 02.30.Zz
Download Full PDF