Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/gmd-2018-30
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Development and technical paper
22 Mar 2018
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).
DATeS: A Highly-Extensible Data Assimilation Testing Suite
Ahmed Attia1 and Adrian Sandu2 1Mathematics 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
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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Short summary
This work describes DATeS, a highly-extensible data assimilation package. DATeS seeks to provide a unified testing suite for data assimilation applications that allows researchers to easily compare different methodologies in different settings with minimal coding effort. The core of DATeS is written in Python. The main functionalities, such as model propagation, and assimilation, can however be written in low-level languages such as C or Fortran to attain high levels of computational efficiency.
This work describes DATeS, a highly-extensible data assimilation package. DATeS seeks to provide...
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