Skip to content

Tutorials

During the PROMOTE Analysis Sprint, we will be running hands-on tutorials to demonstrate how to use NEMO Cookbook to analyse NEMO ocean model outputs from UKESM1-2-LL and the CANARI Large-Ensemble.

For those with limited Python experience, we would recommend working through Tutorial 0. Getting Started with xarray prior to the hackathon to better understand the key data structures and operations used throughout the hackathon.

Pre-Hackathon

  • 0. Getting Started with xarray

    Ocean models produce large, multi-dimensional fields (e.g. temperature, salinity, velocity) that vary in time and space and are described by physical coordinates (e.g., depth). xarray provides data structures that explicitly represent these dimensions and coordinates, rather than treating model outputs as anonymous multi-dimensional arrays.

    This Jupyter Notebook provides an introduction to xarray for ocean model analysis and is intended as a quick reference that you can return to during the hackathon and beyond.

NEMO Cookbook Tutorials

  • 1. Getting Started with NEMO Cookbook.

    In this Jupyter Notebook, we will demonstrate how to use the NEMODataTree object to analyse outputs of NEMO global ocean sea-ice simulations.

    We will cover:

    • Creating NEMODataTree objects directly from netCDF files and xarray.Datasets.

    • Opening virtual NEMODataTrees from Icechunk repositories.

    • Exploring NEMO model output variables stored in a NEMODataTree.

    • Geographical plotting of NEMO output variables.

    • Perform grid-aware operations using NEMODataArrays.

  • 2. Analysing UKESM1-2-LL with NEMO Cookbook.

    In this Jupyter Notebook, we will demonstrate how to use the NEMODataTree object to analyse outputs of the TERRAFIRMA UKESM1-2-LL idealised emission scenario simulations on JASMIN.

    We will cover:

    • Opening virtual NEMODataTree objects directly from Icechunk repositories using the from_icechunk() constructor.

    • Exploring NEMO ocean & sea-ice variables stored in a NEMODataTree.

    • Calculating March mixed layer volume for the subpolar North Atlantic using NEMODataArray.integral().

  • 3. Analysing CANARI Large-Ensemble with NEMO Cookbook.

    In this Jupyter Notebook, we will demonstrate how to use the NEMODataTree object to analyse outputs of the CANARI Large-Ensemble simulations on JASMIN.

    We will cover:

    • Opening virtual NEMODataTree objects directly from Icechunk repositories using the from_icechunk() constructor.

    • Exploring NEMO ocean & sea-ice variables stored in a NEMODataTree.

    • Extracting the North Atlantic Changes (NOAC) 47°N array using NEMODataTree.extract_zonal_section().