What is it?#


These docs assume readers have a basic knowledge of Python and are familiar with the Python package, xarray. If you’re not familiar with xarray, check out the excellent xarray tutorial webpage.

The short, high-level description#

The ACCESS-NRI catalog is essentially a table of climate data products that exist on Gadi. Each entry in the table corresponds to a different product, and the columns contain attributes associated with each product–things like the models, frequencies and variables available. Users can search on the attributes to find the products that might be useful to them. For example, a user might want to know which data products contain variables X, Y and Z at monthly frequency. The ACCESS-NRI catalog enables users to find products that satisfy their query and to subsequently load their data without having to know the location and structure of the underlying files.

The more detailed description#

The ACCESS-NRI catalog provides a catalog of Intake sources and associated metadata.

The entries in the ACCESS-NRI catalog are Intake-ESM datastores for climate data products that exist on Gadi.

A set of core metadata attributes are associated with each entry in the ACCESS-NRI catalog. At the moment these include:

  • The name of the data product

  • A short description of the data product

  • The model(s) used

  • The realm(s) available

  • The frequency(/ies) available

  • The variable(s) available

A simple search API allows users to filter the entries in the catalog based on these metadata attributes. The idea is that users will:

  1. search the ACCESS-NRI catalog for data products, e.g. products containing the models, variables etc that are of interest to them.

  2. open the Intake-ESM datastore(s) for the product(s) of interest.

  3. search the Intake-ESM datastore(s) for the datasets within each product that are of interest to them. A “dataset” here is a set of files that can be readily opened and combined for analysis.

  4. open the datasets of interest as xarray Dataset(s).

  5. perform some analysis on the xarray Dataset(s).