Wide Interoperability Nexus for Data Organized Workflows


View the story map about the WINDOW project

There is an increasing need in RAL for very high-resolution weather forecast guidance to be created and disseminated to decision-makers, collaborators/funders inreal time.  WINDOW addressed ,many of the current constraints that exist across multiple projects and data services and developed a workflow that moves data from raw model output to informational displays for researchers and stakeholders. This workflow highlighted best practices and expands upon existing tools for data formats, metadata conventions, data storage and management capabilities.WINDOW paves the way to make the information accessible through map and data services which can be visualized in a wide suite of display systems. 


WINDOW consisted of the following tasks:

1) Conduct an inventory of existing tools that can be leveraged and identify key metadata variables.

2) Develop the ‘Adaption and Interoperability Registry’ (AIR) for adding necessary Climate and Forecast (CF) (http://cfconventions.org/) metadata to ensure accurate georeferencing of model output (i.e., Coordinate and Coordinate System Variables, Grid Mapping). AIR is a series of python scripts and can be downloaded from GitHub at https://github.com/NCAR/WINDOW

3) Publish the data through a number of data servers already in use within RAL (i.e., THREDDS data server, GeoServer, ArcGIS Server) using standards-based map and data services (e.g., OGC WMS, WFC, WCS, ArcGIS API).

4) Develop a display system for the published services using open source and proprietary options (e.g., OpenLayers, ArcGIS Online).  By adopting these standards and attributing data with CF metadata we will show how the data can also be displayed in a number of desktop applications such as JAZZ, ArcGIS, and QGIS.



Arnaud Dumont

Kevin Sampson

Jeremy Sauer

Howard Soh

Olga Wilhelmi


RAL Opportunity Fund