diff --git a/examples/nwm/nwm_swe_point_scale_evaluation.ipynb b/examples/nwm/nwm_swe_point_scale_evaluation.ipynb
index e974054..c01c054 100644
--- a/examples/nwm/nwm_swe_point_scale_evaluation.ipynb
+++ b/examples/nwm/nwm_swe_point_scale_evaluation.ipynb
@@ -77,10 +77,6 @@
     "import matplotlib.pyplot as plt\n",
     "import matplotlib.dates as mdates\n",
     "\n",
-    "# Import the Evaluation library from the project root.\n",
-    "repo_root = Path.cwd().resolve().parents[1]\n",
-    "sys.path.insert(0, str(repo_root / \"src\"))\n",
-    "\n",
     "from cssi_evaluation.models import nwm_utils\n",
     "from cssi_evaluation.variables import snow_utils\n",
     "from cssi_evaluation.utils import plot_utils, dataPrep_utils, evaluation_utils, metric_utils\n",
@@ -283,7 +279,7 @@
    "id": "35b1321e",
    "metadata": {},
    "source": [
-    "## 4. Retrieve Snow Model Outputs"
+    "## 3. Retrieve Snow Model Outputs"
    ]
   },
   {
@@ -304,10 +300,10 @@
     "%%time \n",
     "ds = xr.open_zarr(\n",
     "    store=conus_bucket_url,\n",
-    "    consolidated=True,\n",
-    "    storage_options={\n",
-    "        \"anon\": True,\n",
-    "        \"client_kwargs\": {\"region_name\": \"us-east-1\"}\n",
+    "    consolidated=True,      # Tells Xarray to use consolidated metadata (speeds up access)\n",
+    "    storage_options={       \n",
+    "        \"anon\": True,       # Means anonymous access to the public S3 bucket without needing AWS credentials\n",
+    "        \"client_kwargs\": {\"region_name\": \"us-east-1\"}  # Specifies the AWS region where the bucket lives\n",
     "    }\n",
     ")"
    ]
@@ -496,7 +492,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plot_utils.comparison_plots(obs_df,  model_df, f'{my_site_code}', f'{my_site_code}', site_label=None)"
+    "plot_utils.comparison_plots(obs_df,  \n",
+    "                            model_df, \n",
+    "                            f'{my_site_code}',  # Column name in df_obs\n",
+    "                            f'{my_site_code}',  # Column name in df_model\n",
+    "                            site_label=None)"
    ]
   },
   {
@@ -594,7 +594,6 @@
    "source": [
     "# isolate the columns associated with observations and model predictions.\n",
     "# these will be inputs to our same-day comparison function.\n",
-    "combined_df = pd.concat([obs_df, model_df], axis=1)\n",
     "obs_swe_cols = obs_df.columns.tolist()\n",
     "mod_swe_cols = model_df.columns.tolist()"
    ]
@@ -634,7 +633,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Rearrange the dataframe to long format for easier plotting\n",
+    "# Rearrange the dataframe to long format for easier plotting. The function `.melt({})` unpivots the DataFrame from wide to long format.\n",
     "df_long = (\n",
     "    df_observed_peak\n",
     "    .reset_index()  \n",
@@ -1205,6 +1204,16 @@
     "The Condon diagram separates model performance into quadrants based on two metrics: **Spearman’s rho** (shape/time agreement) and **relative bias** (magnitude error). The horizontal line at 0.5 distinguishes whether the model captures the temporal pattern well (above 0.5 = good shape), while the vertical line is traditionally placed at a relative bias of 1.0, which represents a 100% error. This means the model’s total error is as large as the observed signal itself. This threshold has a clear physical interpretation and is used in the original Condon framework to distinguish acceptable vs. large bias. "
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "10a68672",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%reload_ext autoreload"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/src/cssi_evaluation/utils/plot_utils.py b/src/cssi_evaluation/utils/plot_utils.py
index 87caff4..12fd1c2 100644
--- a/src/cssi_evaluation/utils/plot_utils.py
+++ b/src/cssi_evaluation/utils/plot_utils.py
@@ -1059,7 +1059,7 @@ def plot_scatter_melt_metrics(df):
     lims = [min(x.min(), y.min()), max(x.max(), y.max())]
     axes[0].plot(lims, lims, 'k--')
 
-    axes[0].set_title("Melt Rate (Obs vs Model)")
+    axes[0].set_title("Melt Rate (Obs vs Model): m/day")
     axes[0].set_xlabel("Observed")
     axes[0].set_ylabel("Modeled")
     axes[0].grid(True)
@@ -1073,7 +1073,7 @@ def plot_scatter_melt_metrics(df):
     lims = [min(x.min(), y.min()), max(x.max(), y.max())]
     axes[1].plot(lims, lims, 'k--')
 
-    axes[1].set_title("Melt Duration (Obs vs Model)")
+    axes[1].set_title("Melt Duration (Obs vs Model): days")
     axes[1].set_xlabel("Observed")
     axes[1].set_ylabel("Modeled")
     axes[1].grid(True)