"Gridded": Multi-Grid Data Analysis and Visualization with Python

In order to conform to irregular coastlines, coastal circulation models are most most often built on non-rectangular model grids. These include curvilinear grids typically used by finite difference models (e.g. ROMS) and triangular mesh grids used by finite volume (e.g. FVCOM) and finite element (e.g. ADCIRC) models. Increasingly, these more complex grid types are being used it atmospheric modeling as well. In addition to the complexity of the grid itself, different models can produce results on varying parts of the mesh: on nodes, on cells, and staggered (e.g. the Arakawa C-grid). While these varying grid types do an excellent job of allowing models to have effective computational schemes that conform to the boundaries of the domain, they pose complications for post-processing and analysis tools, particularly tools intended to work with a variety of models or inter-comparison of multiple models that may use different grid schemes.

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.