Common Data Models and Efficient Reproducible Workflows for Distributed Ocean Model Skill Assessment

Wednesday, 17 December 2014
Richard P Signell, USGS Woods Hole Science Ctr, Woods Hole, MA, United States, Derrick Preston Snowden, Integrated Ocean Observing System, Program Office, Silver Spring, MD, United States, Eoin Howlett, RPS Group, South Kingston, RI, United States and Filipe A Fernandes, Centro Universitário Mont-Serrat, Santos-SP, Brazil
Model skill assessment requires discovery, access, analysis, and visualization of information from both sensors and models, and traditionally has been possible only by a few experts. The US Integrated Ocean Observing System (US-IOOS) consists of 17 Federal Agencies and 11 Regional Associations that produce data from various sensors and numerical models; exactly the information required for model skill assessment. US-IOOS is seeking to develop documented skill assessment workflows that are standardized, efficient, and reproducible so that a much wider community can participate in the use and assessment of model results.

Standardization requires common data models for observational and model data. US-IOOS relies on the CF Conventions for observations and structured grid data, and on the UGRID Conventions for unstructured (e.g. triangular) grid data. This allows applications to obtain only the data they require in a uniform and parsimonious way using web services: OPeNDAP for model output and OGC Sensor Observation Service (SOS) for observed data.

Reproducibility is enabled with IPython Notebooks shared on GitHub ( These capture the entire skill assessment workflow, including user input, search, access, analysis, and visualization, ensuring that workflows are self-documenting and reproducible by anyone, using free software. Python packages for common data models are Pyugrid and the British Met Office Iris package. Python packages required to run the workflows (pyugrid, pyoos, and the British Met Office Iris package) are also available on GitHub and on so that users can run scenarios using the free Anaconda Python distribution. Hosted services such as Wakari enable anyone to reproduce these workflows for free, without installing any software locally, using just their web browser. We are also experimenting with Wakari Enterprise, which allows multi-user access from a web browser to an IPython Server running where large quantities of model output reside, increasing the efficiency.

The open development and distribution of these workflows, and the software on which they depend, is an educational resource for those new to the field and a center of focus where practitioners can contribute new software and ideas.