The first step in resolving these complications to establish data format standards. The CF metadata conventions for netcdf files has been very successful in enabling data interchange, but it does not currently support non-rectangular grid types. Over the years, the community has created conventions to help facilitate this interchange: The UGRID Conventions (http://ugrid-conventions.github.io/ugrid-conventions/), and the SGRID Conventions (http://sgrid.github.io/sgrid/). In order for these conventions to be useful tools need to be available that understand them, and provide functionality for developing analysis and visualization tools that support them.
This presentation will present the "gridded" Python package. gridded provides a single API that allows users to analyse and visualize data from a variety of models grids. Essentially, a gridded.Dataset provides an abstraction for field variables irespective of teh underying grid the data are computed from. gridded provides utilities for navigating and interpolating the grid, so that users can work with the data set as a field of variables, rather than concern themselves with the intricacies of grid structure. This talk will give a quick overview of the two data conventions, the API provided by the tools, and examples of their use in data analysis, visualization, re-gridding, inter-comparison, and particle tracking.
Supplementary URL: https://github.com/NOAA-ORR-ERD/gridded