ECCO v4 computations

The ECCO version 4 Python Tutorial website has a wide range of tutorials to help users get started with accessing and using ECCOv4 output. These tutorials cover subjects ranging from loading data files using Python’s xarray package, to more advanced computations like the steric height, meridional heat transport, and budgets.

Tutorials hosted in ecco-2024

A subset of these tutorials have been copied over to the ecco-2024 Jupyter book and can be accessed below, to give you a sense of their range and structure. The tutorials hosted here already have settings enabled for cloud access, and will use the efs_ecco mounted drive for any downloads.

Tutorial	Topics
The Dataset and DataArray objects used in the ECCOv4 Python package	xarray intro with ECCOv4 output
Coordinates and Dimensions of ECCOv4 NetCDF files	Arakawa C-grid and ECCOv4 coordinates
Accessing and Subsetting Variables	xarray accessing and subsetting
Memory management in Python	Memory management when using numpy, dask, and xarray
Example calculations with scalar quantities	Sample calculations (e.g., SSH global mean)
Calculating gradients and curl on the ECCO native grid	Horizontal derivatives on the C-grid
Steric height	Steric height and thermosteric/halosteric components
Compute meridional heat transport	Meridional heat transport
ECCOv4 Global Volume Budget Closure	Volume budget (minus global mean steric, IB, sea-ice loading)

All ECCOv4 Python tutorials

The full set of tutorials are available for viewing on the website and using the links under All ECCOv4 Python tutorials below. They can be run by cloning the tutorial Github repo. Note that in order to run these successfully on the OSS cloud system, you will need to take two steps:

• Do a text search for incloud_access and set this variable to incloud_access = True.

• Set the download directory (called ECCO_dir or sometimes download_root_dir in the notebooks) to ECCO_dir = join(user_home_dir,'efs_ecco','ECCO_V4r4_PODAAC').

Tutorials:

Getting started
The ECCO Ocean and Sea-Ice State Estimate
ECCO v4 state estimate ocean, sea-ice, and atmosphere fields
Python and Python Packages
Using Python to Download ECCO Datasets
Downloading Subsets of ECCO Datasets
Using wget to Download ECCO Datasets from PO.DAAC
AWS Cloud: getting started and retrieving ECCO datasets
Tutorial Overview

ECCO data structures
The Dataset and DataArray objects used in the ECCOv4 Python package
Coordinates and Dimensions of ECCOv4 NetCDF files

Input/output, data structure manipulation
Loading the ECCOv4 native model grid parameters
Loading the ECCOv4 state estimate fields on the native model grid
ECCOv4 Loading llc binary files in the ‘compact’ format
Combining multiple Datasets
Saving Datasets and DataArrays to NetCDF

Operating on data variables
Accessing and Subsetting Variables
Operating on Numpy arrays
Memory management in Python

Plotting & interpolation
Plotting Tiles
Interpolating fields from the model llc grid to a regular lat lon grid

Scalar and vector calculations
Example calculations with scalar quantities
Calculating gradients and curl on the ECCO native grid

Intro to PO Tutorials
Intro to PO Tutorials: Getting Started
Part 1: Geostrophic balance
Part 2: Thermal Wind
Part 3: Steric height

More advanced calculations
Compute meridional heat transport
Compute MOC along the approximate OSNAP array from ECCO
ECCOv4 Global Volume Budget Closure
Global Heat Budget Closure
Salt, Salinity and Freshwater Budgets
Calculate ocean thermal forcing from ECCOv4r4 data, direct from PO.DAAC S3 storage