NOAA-EPIC · mariahpope · Oct 23, 2025 · Oct 23, 2025 · Oct 23, 2025 · Oct 23, 2025
diff --git a/.readthedocs.yaml b/.readthedocs.yaml
@@ -29,6 +29,6 @@ sphinx:
 # Optional but recommended, declare the Python requirements required
 # to build your documentation
 # See https://docs.readthedocs.io/en/stable/guides/reproducible-builds.html
-# python:
-#   install:
-#     - requirements: docs/requirements.txt
+python:
+  install:
+    - requirements: docs/requirements.txt
diff --git a/docs/data_creation.rst b/docs/data_creation.rst
@@ -0,0 +1,36 @@
+=================
+Create Training Data
+=================
+
+We use ufs2arco to generate training, validation, and test datasets.
+
+First, create a recipe.yaml. An example can be found here. Next, run the following:
+
+```
+ufs2arco recipe.yaml
+```
+
+For further information see the ufs2arco `github <https://ufs2arco.readthedocs.io/en/latest/>`_ or `documentation <https://ufs2arco.readthedocs.io/en/latest/>`_
+
+Helpful quick tips for ufs2arco
+------------------
+
+Choosing Dates
+~~~~~~~~~~~~~~~~~~~~~~
+Update the dates you wish to include in your dataset by changing the below section in your recipe. 
+These dates will include all data that you plan to use for training, validation, and testing.
+The full dataset will be split into these individual sets later on.
+
+
+.. code-block:: yaml
+    start: 2022-01-01T06
+    end: 2022-12-31T18
+    freq: 6h
+
+Then ensure that you have updated the statistics_period section to match:
+
+.. code-block:: yaml
+    start: 2022-01-01T06
+    end: 2022-10-31T18
+
+Note: it is best practice to ensure that your statistics period only includes dates you plan to include in your training dataset.
diff --git a/docs/getting_started.rst b/docs/getting_started.rst
@@ -2,17 +2,14 @@
 Getting Started
 =================
 
-Learn how to use the full `ufs2arco + Anemoi + wxvx` pipeline.
+We have included a few options to learn how to get started with this pipeline.
 
-You will learn how to:
+Google Collab
+~~~~~~~~~~~~~~~~~~~~~~
 
-1) Use `ufs2arco` to create training and validation datasets with NOAA Replay reanalysis
-2) Use `anemoi-core` modules to train a graph-based model
-3) Use `anemoi-inference` to run inference
-4) Use `wxvx` to verify a forecast
+Insert info about collab and point to it
 
--------------
-Ursa
--------------
+Train a Small Model with NOAA-Replay Data
+~~~~~~~~~~~~~~~~~~~~~~
 
-Insert instructions for Ursa
+Insert info about replay option and point to it
diff --git a/docs/index.rst b/docs/index.rst
@@ -1,5 +1,25 @@
-=================
+###################
 Welcome to Eagle!
-=================
+###################
 
-Enter overview of Eagle project....?
+Insert overview of Eagle
+
+###################
+Table of Contents
+###################
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Full Pipeline
+
+   data_creation
+   train_a_model
+   inference
+   verification
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Examples
+
+   getting_started
+   nested_eagle
diff --git a/docs/inference.rst b/docs/inference.rst
@@ -4,6 +4,9 @@ Inference
 
 We use the anemoi-inference package to create a forecast.
 
+See `anemoi-inference documentation <https://anemoi.readthedocs.io/projects/inference/en/latest/>`_ documentation for further information
+
 Helpful quick tips for using anemoi-inference
+------------------
 
-See `anemoi-inference documentation <https://anemoi.readthedocs.io/projects/inference/en/latest/>`_ documentation for further information:
+Insert tips
diff --git a/docs/nested-eagle.rst → docs/nested_eagle.rst b/docs/nested-eagle.rst → docs/nested_eagle.rst
diff --git a/docs/pipeline_overview.rst b/docs/pipeline_overview.rst
@@ -0,0 +1,23 @@
+=================
+Overview of Full Pipeline
+=================
+
+You will learn how to complete a full machine learning pipeline for weather prediction!
+
+The steps of this pipeline include:
+
+Step 1: Dataset Creation 
+~~~~~~~~~~~~~~~~~~~~~~
+Use `ufs2arco` to create training and validation datasets with NOAA Replay reanalysis
+
+Step 2: Train a Graph-Based Model
+~~~~~~~~~~~~~~~~~~~~~~
+Use `anemoi-core` modules to train a graph-based model
+
+Step 3: Create a Forecast
+~~~~~~~~~~~~~~~~~~~~~~
+Use `anemoi-inference` to run inference
+
+Step 4: Verify a Forecast
+~~~~~~~~~~~~~~~~~~~~~~
+Use `wxvx` to verify a forecast
diff --git a/docs/requirements.txt b/docs/requirements.txt
@@ -0,0 +1,2 @@
+sphinx==8.2.3
+sphinx-rtd-theme==3.0.2
diff --git a/docs/training.rst → docs/train_a_model.rst b/docs/training.rst → docs/train_a_model.rst
@@ -4,9 +4,13 @@ Train a Graph-Based Model
 
 We use the anemoi-core modules to train a graph-based model.
 
-Helpful quick tips for using Anemoi
-
 See Anemoi documentation for further information:
+
 - `anemoi-graphs <https://anemoi.readthedocs.io/projects/graphs/en/latest/>`_
 - `anemoi-training <https://anemoi.readthedocs.io/projects/training/en/latest/>`_
 - `anemoi-models <https://anemoi.readthedocs.io/projects/models/en/latest/index.html>`_
+
+Helpful quick tips for using anemoi-core
+------------------
+
+Insert tips
diff --git a/docs/ufs2arco.rst b/docs/ufs2arco.rst
diff --git a/docs/verification.rst b/docs/verification.rst
@@ -4,6 +4,9 @@ Validation
 
 We use wxvx for forecast verification.
 
-Helpful quick tips for using wxvx.
+See `wxvx <https://github.com/maddenp-cu/wxvx>`_  for further information.
 
-See `wxvx <https://github.com/maddenp-cu/wxvx>`_  for further information.
+Helpful quick tips for using wxvx
+------------------
+
+Insert tips