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Creators/Authors contains: "Attia, Ahmed"

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  1. ; (, Geoscientific Model Development)
    Abstract. A flexible and highly extensible data assimilation testing suite, namedDATeS, is described in this paper. DATeS aims to offer a unified testingenvironment that allows researchers to compare different data assimilationmethodologies and understand their performance in various settings. The coreof DATeS is implemented in Python and takes advantage of its object-orientedcapabilities. The main components of the package (the numerical models, thedata assimilation algorithms, the linear algebra solvers, and the timediscretization routines) are independent of each other, which offers greatflexibility to configure data assimilation applications. DATeS can interfaceeasily with large third-party numerical models written in Fortran or in C,and with a plethora of external solvers. 
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  2. ; ; (, Atmosphere)
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