DATeS: A Highly-Extensible Data Assimilation Testing Suite
Ahmed Attia1 and Adrian Sandu21Mathematics and Computer Science Division, Argonne National Laboratory, 9700 S. Cass Ave. Bldg. 240, Lemont, IL 60439, USA 2Computational Science Laboratory, Department of Computer Science, Virginia Polytechnic Institute and State University, 2201 Knowledgeworks II, 2202 Kraft Drive, Blacksburg, VA 24060, USA
Received: 06 Feb 2018 – Accepted for review: 21 Mar 2018 – Discussion started: 22 Mar 2018
Abstract. A flexible and highly-extensible data assimilation testing suite, named DATeS, is described in this paper. DATeS aims to offer a unified testing environment that allows researchers to compare different data assimilation methodologies and understand their performance in various settings. The core of DATeS is implemented in Python and takes advantage of its object-oriented capabilities. The main components of the package (the numerical models, the data assimilation algorithms, the linear algebra solvers, and the time discretization routines) are independent of each other, which offers great flexibility to configure data assimilation applications. DATeS can interface easily with large third-party numerical models written in Fortran or in C, and with a plethora of external solvers. Citation:
Attia, A. and Sandu, A.: DATeS: A Highly-Extensible Data Assimilation Testing Suite, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2018-30, in review, 2018.
Ahmed Attia and Adrian Sandu
Ahmed Attia and Adrian Sandu
Ahmed Attia and Adrian Sandu
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