diff --git a/_toc.yml b/_toc.yml
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--- a/_toc.yml
+++ b/_toc.yml
@@ -53,6 +53,7 @@ parts:
       - file: intermediate/hvplot
       - file: intermediate/datastructures-intermediate.ipynb
       - file: intermediate/BiologyDataset.ipynb
+      - file: intermediate/hierarchical_zarr_store.ipynb
       - file: intermediate/remote_data/index
         sections:
           - file: intermediate/remote_data/cmip6-cloud.ipynb
diff --git a/intermediate/hierarchical_zarr_store.ipynb b/intermediate/hierarchical_zarr_store.ipynb
new file mode 100644
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+++ b/intermediate/hierarchical_zarr_store.ipynb
@@ -0,0 +1,331 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0",
+   "metadata": {},
+   "source": [
+    "# Zarr Stores with Xarray\n",
+    "\n",
+    "## Learning Objectives:\n",
+    "- Learn about the Zarr data format and xarray's \"zarr\" backend engine\n",
+    "- Learn how to read a Zarr store with a hierarchical structure with `xr.DataTree`\n",
+    "- Learn how to select dask arrays from a Zarr store\n",
+    "- Explore how to use Zarr stores with xarray for computations and visualizations\n",
+    "\n",
+    "## What is Zarr?\n",
+    "\n",
+    "The Zarr data format is an open, community-maintained format designed for efficient, scalable storage of large N-dimensional arrays. It stores data as compressed and chunked arrays in a format well-suited to parallel processing and cloud-native workflows. Xarray’s Zarr backend allows xarray to leverage these capabilities, including the ability to store and analyze datasets far too large fit onto disk (particularly in combination with [dask](https://docs.xarray.dev/en/latest/user-guide/dask.html#dask)).\n",
+    "\n",
+    "### Zarr Data Organization:\n",
+    "- **Arrays**: N-dimensional arrays that can be chunked and compressed.\n",
+    "- **Groups**: A container for organizing multiple arrays and other groups with a hierarchical structure.\n",
+    "- **Metadata**: JSON-like metadata describing the arrays and groups, including information about data types, dimensions, chunking, compression, and user-defined key-value fields. \n",
+    "- **Dimensions and Shape**: Arrays can have any number of dimensions, and their shape is defined by the number of elements in each dimension.\n",
+    "- **Coordinates & Indexing**: Zarr supports coordinate arrays for each dimension, allowing for efficient indexing and slicing.\n",
+    "\n",
+    "The diagram below from [the Zarr v3 specification](https://wiki.earthdata.nasa.gov/display/ESO/Zarr+Format) showing the structure of a Zarr store:\n",
+    "\n",
+    "![ZarrSpec](https://zarr-specs.readthedocs.io/en/latest/_images/terminology-hierarchy.excalidraw.png)\n",
+    "\n",
+    "\n",
+    "NetCDF and Zarr share similar terminology and functionality, but the key difference is that NetCDF is a single file, while Zarr is a directory-based “store” composed of many chunked files, making it better suited for distributed and cloud-based workflows."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1",
+   "metadata": {},
+   "source": [
+    "## Reading a zarr store\n",
+    "\n",
+    "With xarray's \"zarr\" backend, we can read data from cloud storage buckets. The Zarr store we will be using for this tutorial has groups: \"observed\" and \"reanalysis\", each containing a \"precipitation\" data variable derived from *[GPM_3IMERGHH_07](https://disc.gsfc.nasa.gov/datasets/GPM_3IMERGHH_07/summary)* and *[M2T1NXFLX_5.12.4](https://disc.gsfc.nasa.gov/datasets/M2T1NXFLX_5.12.4/summary)* products, respectively.\n",
+    "\n",
+    "Let's read in our zarr store as an `xr.DataTree`. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xarray as xr\n",
+    "\n",
+    "precipitation_store = \"https://pub-45a1d62ac8d94c4c89f4dc63681a98ed.r2.dev/precipitation.zarr\"\n",
+    "\n",
+    "precip_dt = xr.open_datatree(precipitation_store, engine=\"zarr\", chunks={}, consolidated=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3",
+   "metadata": {},
+   "source": [
+    ":::{note} We selected `\"zarr\"` backend engine, which tells xarray to load and decode a dataset from a Zarr store. The `chunks={}` parameter is used to load the data into a dask array. And `consolidated=True` enables zarr’s consolidated metadata capability. This lets us read all of the metadata from a single file which can improve performance.\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4",
+   "metadata": {},
+   "source": [
+    "## Variable selection\n",
+    "With our `DataTree` object, we can select variables from our Zarr store with either dictionary and or attribute like syntax."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5",
+   "metadata": {},
+   "source": [
+    "### Dictionary-like interface"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "precip_dt[\"observed/precipitation\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7",
+   "metadata": {},
+   "source": [
+    "### Attribute-like access"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "precip_dt.observed.precipitation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9",
+   "metadata": {},
+   "source": [
+    "### Dictionary and Attribute like access"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "10",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "precip_dt.observed[\"precipitation\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "11",
+   "metadata": {},
+   "source": [
+    ":::{note} All of these variable selection options return the same \"precipitation\" `xr.DataArray` object, as a chunked `dask.Array`, from the \"observed\" group of our Zarr store.\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "12",
+   "metadata": {},
+   "source": [
+    "## Time slicing\n",
+    "\n",
+    "We can index and subset by time on our `xr.DataTree` object. Each time slice in our Zarr store represents one hour of data with a total of 10 hours of data.\n",
+    "\n",
+    "Let's explore the different ways we can get the first 5 hours of data. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "13",
+   "metadata": {},
+   "source": [
+    "### Label-based indexing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "14",
+   "metadata": {},
+   "source": [
+    "Let's try getting the first 5 hours of data with `.sel(time=)`. \n",
+    "\n",
+    "Since the time slices are ordered we can get a subset of the array of our time coordinate and pass it to the `.sel` method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "15",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "time_index = precip_dt.time[0:5]\n",
+    "precip_dt.sel(time=time_index)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "16",
+   "metadata": {},
+   "source": [
+    "### Datetime indexing\n",
+    "We can also subset by time with a `datetime` string."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "17",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "precip_dt.sel(time=slice(\"2021-08-29T07:30:00\", \"2021-08-29T16:30:00\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "18",
+   "metadata": {},
+   "source": [
+    "### Positional Indexing\n",
+    "Or by the index of our time dimension `.isel(time=slice())`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "19",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "precip_dt.isel(time=slice(0, 5))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "source": [
+    "## Chunking\n",
+    "Chunking is the process of dividing arrays into smaller pieces, which allows for parallel processing and efficient storage.\n",
+    "\n",
+    "To examine the chunks in our Zarr store, with `xarray` you can use the `chunks` attribute on the `xr.DataArray` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "21",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "precip_dt.observed[\"precipitation\"].data.chunks"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "22",
+   "metadata": {},
+   "source": [
+    "### Selecting by chunks\n",
+    "\n",
+    "Since we loaded our data as a `dask.Array`, we can access data from each chunked array in our Zarr store. \n",
+    "\n",
+    "Let's get the first chunk of the \"observed/precipitation\" variable in our zarr store."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "23",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "precip_dt.observed[\"precipitation\"].data.blocks[0, 0, 0].compute()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "24",
+   "metadata": {},
+   "source": [
+    ":::{note}\n",
+    "We added `.data` to our `xr.DataArray` to access the `dask.Array`. The `.blocks[]` method allows you to index by chunk and `.compute()` returns the `np.ndarray`. \n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "25",
+   "metadata": {},
+   "source": [
+    "## Exercise"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "26",
+   "metadata": {},
+   "source": [
+    "::::{admonition} Exercise\n",
+    ":class: tip\n",
+    "\n",
+    "Can you calculate and plot the mean precipitation, starting at 09:55 for the reanalysis group in this zarr store?\n",
+    "\n",
+    ":::{admonition} Hint\n",
+    ":class: dropdown\n",
+    "This is how you could calculate mean \"precipitation\"\n",
+    "\n",
+    "```python\n",
+    "precip_dt.reanalysis['precipitation'].mean(dim='time')\n",
+    "```\n",
+    ":::\n",
+    "\n",
+    ":::{admonition} Solution\n",
+    ":class: dropdown\n",
+    "\n",
+    "```python\n",
+    "precip_dt.reanalysis['precipitation'].sel(time=slice('2021-08-29T09:55:00', '2021-08-29T16:30:00')).mean(dim='time').plot()\n",
+    "```\n",
+    ":::\n",
+    "::::"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}