Data and service management

The PAVICS project Data Server is built around a Thematic Real-time Environmental Distributed Data Service (THREDDS). This service allows for real-time collection and presentation of archived data and metadata using remote access protocols to bridge the gap between data providers and researchers.

The THREDDS Project is an Open Source initiative maintained by UCAR’s Unidata Program. For more information on Unidata, see the Project Home Page. To learn more about THREDDS, view the Project Description on GitHub.

To better understand the way THREDDS integrates within PAVICS, see the System Architecture Overview.

NetCDF file management in THREDDS

Data preparation for inclusion in the platform

NetCDF files integrated in the PAVICS platform must follow the CF Conventions document:

In order to benefit from the search engine capabilities, typical global attributes should be set. Currently the platorms searches the following fields:


For variables, the standard_name and units should follow the CF standard name table:

It is recommended to provide a dataset_id as a global attribute in each NetCDF file that is unique for each collection of files that constitute a timeseries.

NetCDF files with multiple variables are presently not fully supported.

Adding files

NetCDF files can be manually added to the THREDDS Data Server by copying them to the directory used as a docker volume in docker-compose.yml (see Installation).

In order for new files to be catalogued, the Solr and PAVICS-DataCatalog components must be running and pavicrawler must be run:

# replace localhost and port number with your PAVICS-DataCatalog deployment address

Note that this crawls the whole THREDDS server and can take a very long time. In order to partially crawl the THREDDS server, use:


It is possible to restrict the crawling even more from the specified THREDDS path with the target_files argument to pavicrawler.


The pavicrawler scans for typical attributes (mostly defined by CMIP) in the NetCDF global attributes. This allows search by facets by other components of the platform.

After running the pavicrawler, new entries in the catalog should appear in Solr:

# must Execute Query, with relevant search criteria, or by increasing rows to get more results

By default, the dataset_id will be made up of the relative path on the thredds server.

Inspecting metadata

An essential requirement for a functional platform is that netCDF data stored in THREDDS has complete and uniform metadata. To do so, the pavics.catalog.thredds_crawler() function can be used to extract the metadata from the netCDF files and see if there are missing entries:

from pavics.catalog import thredds_crawler as crawler
crawler('', index_facets=['project'], exclude_files=['birdhouse/wps_outputs', 'birdhouse/workspaces'])

Note that running this command can take a long while, so the include_files argument can be passed to restrict the crawler to certain directories, such as birdhouse/ouranos/climex/.

Birdhouse Solr

The birdhouse solr uses deduplication ( on the fields “source” and “url”. Essentially the id is a hash of the combination of those fields. This is defined in solrconfig.xml (e.g.

THREDDS Data Server example

An example of a public THREDDS Data Server can be found here:

Adding external services


How to add WPS, WMS, WFS servers to PAVICS.

Using thredds_crawler

Test code

from thredds_crawler.crawl import Crawl