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

Viewed

Total article views: 2,212 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,483 692 37 2,212 43 39
  • HTML: 1,483
  • PDF: 692
  • XML: 37
  • Total: 2,212
  • BibTeX: 43
  • EndNote: 39
Views and downloads (calculated since 22 Mar 2018)
Cumulative views and downloads (calculated since 22 Mar 2018)

Viewed (geographical distribution)

Total article views: 1,896 (including HTML, PDF, and XML) Thereof 1,890 with geography defined and 6 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 23 Jan 2022
Download
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.