Articles | Volume 12, issue 2
Geosci. Model Dev., 12, 629–649, 2019
https://doi.org/10.5194/gmd-12-629-2019
Geosci. Model Dev., 12, 629–649, 2019
https://doi.org/10.5194/gmd-12-629-2019

Development and technical paper 12 Feb 2019

Development and technical paper | 12 Feb 2019

DATeS: a highly extensible data assimilation testing suite v1.0

Ahmed Attia and Adrian Sandu

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Cited articles

Ades, M. and van Leeuwen, P. J.: The equivalent-weights particle filter in a high-dimensional system, Q. J. Roy. Meteor. Soc., 141, 484–503, 2015. a
Anderson, J. L.: A method for producing and evaluating probabilistic forecasts from ensemble model integrations, J. Climate, 9, 1518–1530, 1996. a, b
Anderson, J. L.: A local least squares framework for ensemble filtering, Mon. Weather Rev., 131, 634–642, 2003. a
Anderson, J. L., Hoar, T., Raeder, K., Liu, H., Collins, N., Torn, R., and Avellano, A.: The data assimilation research testbed: A community facility, B. Am. Meteorol. Soc., 90, 1283–1296, 2009. a
Asch, M., Bocquet, M., and Nodet, M.: Data assimilation: methods, algorithms, and applications, The Society for Industrial and Applied Mathematics (SIAM), Philadelphia, USA, vol. 11, ISBN 9781611974539, 2016. a
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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.