ATMS 523 Module 1 template repository

A GitHub template repository for short exercises on xarray with a small, cloud-hosted climate dataset.
The included notebook loads NOAA OISST v2.1 (daily sea surface temperature) from the AWS Public Datasets program via Zarr (anonymous S3 access), computes some basic statistics, and draws a simple global map.

After you push this to GitHub, mark it as a Template repository in the repo settings so others can click Use this template.

Quick start (local)

1. Create env (Conda recommended):

   conda env create -f environment.yml
   conda activate xarray-climate

   or with pip:

   python -m venv .venv && source .venv/bin/activate
   pip install -r requirements.txt

2. Launch Jupyter: Use vscode or launch a jupyter lab instance on your local or remote machine:

   jupyter lab

3. Open notebooks/01_xarray_oisst_exercises.ipynb and follow the prompts.

4. Be sure to use the xarray-climate conda environment so you have the required libraries installed above.

Data Notes

• Dataset: NOAA Optimum Interpolation Sea Surface Temperature (OISST) v2.1, daily
• Cloud location: s3://noaa-oisst-zarr (public, no credentials required)
• Access method: netCDF + fsspec/s3fs with anon=True
• Variable used: sst (°C); OISST provides °C values already (check attributes).

Learning goals

• Open a netCDF dataset in the cloud with xarray
• Inspect coordinates, variables, and metadata
• Lazy-load & chunk with Dask
• Subset in space/time
• Compute descriptive stats (mean, std, anomalies, seasonal cycle)
• Make a quick global map of a time-aggregated field
• Export a small NetCDF (Check in for Week 2)

Attribution

• NOAA OISST v2.1 (NOAA/NCEI).
• Thanks to the Pangeo community for tools that make cloud datasets easy to use.