  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.
