diff --git a/US_air-temp_daily-precip.nc b/US_air-temp_daily-precip.nc
new file mode 100644
index 0000000..53e7265
Binary files /dev/null and b/US_air-temp_daily-precip.nc differ
diff --git a/notebooks/merge_climate_datasets_exercise.ipynb b/notebooks/merge_climate_datasets_exercise.ipynb
index 9be1adf..65034c6 100644
--- a/notebooks/merge_climate_datasets_exercise.ipynb
+++ b/notebooks/merge_climate_datasets_exercise.ipynb
@@ -28,7 +28,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 28,
       "id": "0a656265",
       "metadata": {
         "tags": [
@@ -39,7 +39,7 @@
       "source": [
         "import matplotlib.pyplot as plt\n",
         "import xarray as xr\n",
-        "\n",
+        "import numpy as np\n",
         "try:\n",
         "    import cartopy.crs as ccrs\n",
         "    import cartopy.feature as cfeature\n",
@@ -69,18 +69,78 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 6,
       "id": "3270985f",
       "metadata": {
         "tags": [
           "exercise"
         ]
       },
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "<xarray.DataArray 'air' (time: 120, lat: 13, lon: 29)> Size: 181kB\n",
+            "dask.array<getitem, shape=(120, 13, 29), dtype=float32, chunksize=(8, 13, 29), chunktype=numpy.ndarray>\n",
+            "Coordinates:\n",
+            "  * lat      (lat) float32 52B 50.0 47.5 45.0 42.5 40.0 ... 27.5 25.0 22.5 20.0\n",
+            "  * lon      (lon) float32 116B 230.0 232.5 235.0 237.5 ... 295.0 297.5 300.0\n",
+            "  * time     (time) datetime64[ns] 960B 2020-06-01 ... 2020-06-30T18:00:00\n",
+            "Attributes: (12/13)\n",
+            "    long_name:     4xDaily Air temperature at sigma level 995\n",
+            "    units:         degK\n",
+            "    precision:     2\n",
+            "    GRIB_id:       11\n",
+            "    GRIB_name:     TMP\n",
+            "    var_desc:      Air temperature\n",
+            "    ...            ...\n",
+            "    parent_stat:   Other\n",
+            "    valid_range:   [185.16 331.16]\n",
+            "    dataset:       NCEP Reanalysis\n",
+            "    actual_range:  [188.6 324.1]\n",
+            "    level_desc:    0.995 sigma\n",
+            "    _ChunkSizes:   [  1  73 144]\n",
+            "<xarray.DataArray 'precip' (time: 30, lat: 60, lon: 140)> Size: 1MB\n",
+            "dask.array<getitem, shape=(30, 60, 140), dtype=float32, chunksize=(8, 60, 140), chunktype=numpy.ndarray>\n",
+            "Coordinates:\n",
+            "  * lat      (lat) float32 240B 49.75 49.25 48.75 48.25 ... 21.25 20.75 20.25\n",
+            "  * time     (time) datetime64[ns] 240B 2020-06-01 2020-06-02 ... 2020-06-30\n",
+            "  * lon      (lon) float32 560B 230.2 230.8 231.2 231.8 ... 298.8 299.2 299.8\n",
+            "Attributes:\n",
+            "    units:         mm\n",
+            "    var_desc:      Precipitation\n",
+            "    level_desc:    Surface\n",
+            "    statistic:     Total\n",
+            "    parent_stat:   Other\n",
+            "    long_name:     Daily total of precipitation\n",
+            "    cell_methods:  time: sum\n",
+            "    valid_range:   [   0. 1000.]\n",
+            "    avg_period:    0000-00-01 00:00:00\n",
+            "    actual_range:  [  0.   776.75]\n",
+            "    dataset:       CPC Global Precipitation\n",
+            "    _ChunkSizes:   [  1 360 720]\n"
+          ]
+        }
+      ],
       "source": [
-        "# TODO: load the air temperature and precipitation datasets.\n",
-        "# Example: air = xr.open_dataset(..., chunks={\"time\": 8})\n",
-        "raise NotImplementedError(\"Assign datasets to `air` and `precip`.\")\n"
+        "# open air temp dataset\n",
+        "air = xr.open_dataset(TEMP_URL, chunks={\"time\": 8})[\"air\"]\n",
+        "air = air.sel(lat=slice(*LAT_RANGE[::-1]),\n",
+        "              lon=slice(*LON_RANGE_360),\n",
+        "              time=TIME_RANGE)\n",
+        "\n",
+        "# open precip dataset\n",
+        "precip = xr.open_dataset(PRECIP_URL, chunks={\"time\": 8})[\"precip\"]\n",
+        "precip = precip.assign_coords(\n",
+        "    lon=((precip.lon % 360))\n",
+        ") # adjust lon coord\n",
+        "precip = precip.sel(lat=slice(*LAT_RANGE[::-1]),\n",
+        "                    lon=slice(*LON_RANGE_360),\n",
+        "                    time=TIME_RANGE)\n",
+        "\n",
+        "print(air)\n",
+        "print(precip)"
       ]
     },
     {
@@ -96,17 +156,69 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 7,
       "id": "264d9641",
       "metadata": {
         "tags": [
           "exercise"
         ]
       },
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "<xarray.DataArray 'air' (time: 120, lat: 13, lon: 29)> Size: 181kB\n",
+            "dask.array<getitem, shape=(120, 13, 29), dtype=float32, chunksize=(8, 13, 29), chunktype=numpy.ndarray>\n",
+            "Coordinates:\n",
+            "  * lat      (lat) float32 52B 50.0 47.5 45.0 42.5 40.0 ... 27.5 25.0 22.5 20.0\n",
+            "  * lon      (lon) float32 116B 230.0 232.5 235.0 237.5 ... 295.0 297.5 300.0\n",
+            "  * time     (time) datetime64[ns] 960B 2020-06-01 ... 2020-06-30T18:00:00\n",
+            "Attributes: (12/13)\n",
+            "    long_name:     4xDaily Air temperature at sigma level 995\n",
+            "    units:         degK\n",
+            "    precision:     2\n",
+            "    GRIB_id:       11\n",
+            "    GRIB_name:     TMP\n",
+            "    var_desc:      Air temperature\n",
+            "    ...            ...\n",
+            "    parent_stat:   Other\n",
+            "    valid_range:   [185.16 331.16]\n",
+            "    dataset:       NCEP Reanalysis\n",
+            "    actual_range:  [188.6 324.1]\n",
+            "    level_desc:    0.995 sigma\n",
+            "    _ChunkSizes:   [  1  73 144]\n",
+            "<xarray.DataArray 'precip' (time: 30, lat: 60, lon: 140)> Size: 1MB\n",
+            "dask.array<getitem, shape=(30, 60, 140), dtype=float32, chunksize=(8, 60, 140), chunktype=numpy.ndarray>\n",
+            "Coordinates:\n",
+            "  * lat      (lat) float32 240B 49.75 49.25 48.75 48.25 ... 21.25 20.75 20.25\n",
+            "  * time     (time) datetime64[ns] 240B 2020-06-01 2020-06-02 ... 2020-06-30\n",
+            "  * lon      (lon) float32 560B 230.2 230.8 231.2 231.8 ... 298.8 299.2 299.8\n",
+            "Attributes:\n",
+            "    units:         mm\n",
+            "    var_desc:      Precipitation\n",
+            "    level_desc:    Surface\n",
+            "    statistic:     Total\n",
+            "    parent_stat:   Other\n",
+            "    long_name:     Daily total of precipitation\n",
+            "    cell_methods:  time: sum\n",
+            "    valid_range:   [   0. 1000.]\n",
+            "    avg_period:    0000-00-01 00:00:00\n",
+            "    actual_range:  [  0.   776.75]\n",
+            "    dataset:       CPC Global Precipitation\n",
+            "    _ChunkSizes:   [  1 360 720]\n"
+          ]
+        }
+      ],
       "source": [
-        "# TODO: subset both datasets using `sel`, handling coordinate ordering as needed.\n",
-        "raise NotImplementedError(\"Create `air_us` and `precip_us`.\")\n"
+        "air_us = air.sel(lon=slice(230,300),\n",
+        "                 time=\"2020-06\")\n",
+        "\n",
+        "precip_us = precip.sel(lon=slice(230,300),\n",
+        "                       time=\"2020-06\")\n",
+        "\n",
+        "print(air_us)\n",
+        "print(precip_us)"
       ]
     },
     {
@@ -121,17 +233,44 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 8,
       "id": "96529bc0",
       "metadata": {
         "tags": [
           "exercise"
         ]
       },
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "<xarray.DataArray 'air' (time: 30, lat: 13, lon: 29)> Size: 45kB\n",
+            "dask.array<stack, shape=(30, 13, 29), dtype=float32, chunksize=(1, 13, 29), chunktype=numpy.ndarray>\n",
+            "Coordinates:\n",
+            "  * lat      (lat) float32 52B 50.0 47.5 45.0 42.5 40.0 ... 27.5 25.0 22.5 20.0\n",
+            "  * lon      (lon) float32 116B 230.0 232.5 235.0 237.5 ... 295.0 297.5 300.0\n",
+            "  * time     (time) datetime64[ns] 240B 2020-06-01 2020-06-02 ... 2020-06-30\n",
+            "Attributes: (12/13)\n",
+            "    long_name:     4xDaily Air temperature at sigma level 995\n",
+            "    units:         degK\n",
+            "    precision:     2\n",
+            "    GRIB_id:       11\n",
+            "    GRIB_name:     TMP\n",
+            "    var_desc:      Air temperature\n",
+            "    ...            ...\n",
+            "    parent_stat:   Other\n",
+            "    valid_range:   [185.16 331.16]\n",
+            "    dataset:       NCEP Reanalysis\n",
+            "    actual_range:  [188.6 324.1]\n",
+            "    level_desc:    0.995 sigma\n",
+            "    _ChunkSizes:   [  1  73 144]\n"
+          ]
+        }
+      ],
       "source": [
-        "# TODO: use xr.resample to create daily means.\n",
-        "raise NotImplementedError(\"Create `air_daily`.\")\n"
+        "air_daily = air_us.resample(time=\"1D\").mean()\n",
+        "print(air_daily)"
       ]
     },
     {
@@ -146,17 +285,44 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 9,
       "id": "52eb7321",
       "metadata": {
         "tags": [
           "exercise"
         ]
       },
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "<xarray.DataArray 'air' (time: 30, lat: 60, lon: 140)> Size: 1MB\n",
+            "dask.array<transpose, shape=(30, 60, 140), dtype=float32, chunksize=(1, 60, 140), chunktype=numpy.ndarray>\n",
+            "Coordinates:\n",
+            "  * time     (time) datetime64[ns] 240B 2020-06-01 2020-06-02 ... 2020-06-30\n",
+            "  * lat      (lat) float32 240B 49.75 49.25 48.75 48.25 ... 21.25 20.75 20.25\n",
+            "  * lon      (lon) float32 560B 230.2 230.8 231.2 231.8 ... 298.8 299.2 299.8\n",
+            "Attributes: (12/13)\n",
+            "    long_name:     4xDaily Air temperature at sigma level 995\n",
+            "    units:         degK\n",
+            "    precision:     2\n",
+            "    GRIB_id:       11\n",
+            "    GRIB_name:     TMP\n",
+            "    var_desc:      Air temperature\n",
+            "    ...            ...\n",
+            "    parent_stat:   Other\n",
+            "    valid_range:   [185.16 331.16]\n",
+            "    dataset:       NCEP Reanalysis\n",
+            "    actual_range:  [188.6 324.1]\n",
+            "    level_desc:    0.995 sigma\n",
+            "    _ChunkSizes:   [  1  73 144]\n"
+          ]
+        }
+      ],
       "source": [
-        "# TODO: interpolate the resampled temperature field onto the precipitation grid.\n",
-        "raise NotImplementedError(\"Create `air_interp`.\")\n"
+        "air_interp = air_daily.interp(lat=precip_us.lat, lon=precip_us.lon)\n",
+        "print(air_interp)"
       ]
     },
     {
@@ -171,17 +337,36 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 10,
       "id": "b766aff5",
       "metadata": {
         "tags": [
           "exercise"
         ]
       },
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "<xarray.Dataset> Size: 2MB\n",
+            "Dimensions:          (time: 30, lat: 60, lon: 140)\n",
+            "Coordinates:\n",
+            "  * time             (time) datetime64[ns] 240B 2020-06-01 ... 2020-06-30\n",
+            "  * lat              (lat) float32 240B 49.75 49.25 48.75 ... 21.25 20.75 20.25\n",
+            "  * lon              (lon) float32 560B 230.2 230.8 231.2 ... 298.8 299.2 299.8\n",
+            "Data variables:\n",
+            "    air_temperature  (time, lat, lon) float32 1MB dask.array<chunksize=(1, 60, 140), meta=np.ndarray>\n",
+            "    daily_precip     (time, lat, lon) float32 1MB dask.array<chunksize=(8, 60, 140), meta=np.ndarray>\n"
+          ]
+        }
+      ],
       "source": [
-        "# TODO: build datasets and merge them into one object named `merged`.\n",
-        "raise NotImplementedError(\"Create `merged`.\")\n"
+        "# build two datasets then merge\n",
+        "air_ds = xr.Dataset({\"air_temperature\": air_interp})\n",
+        "precip_ds = xr.Dataset({\"daily_precip\": precip_us})\n",
+        "merged = xr.merge([air_ds, precip_ds])\n",
+        "print(merged)"
       ]
     },
     {
@@ -196,14 +381,30 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 11,
       "id": "70410d84",
       "metadata": {
         "tags": [
           "validation"
         ]
       },
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "<xarray.Dataset> Size: 2MB\n",
+            "Dimensions:          (time: 30, lat: 60, lon: 140)\n",
+            "Coordinates:\n",
+            "  * time             (time) datetime64[ns] 240B 2020-06-01 ... 2020-06-30\n",
+            "  * lat              (lat) float32 240B 49.75 49.25 48.75 ... 21.25 20.75 20.25\n",
+            "  * lon              (lon) float32 560B 230.2 230.8 231.2 ... 298.8 299.2 299.8\n",
+            "Data variables:\n",
+            "    air_temperature  (time, lat, lon) float32 1MB dask.array<chunksize=(1, 60, 140), meta=np.ndarray>\n",
+            "    daily_precip     (time, lat, lon) float32 1MB dask.array<chunksize=(8, 60, 140), meta=np.ndarray>\n"
+          ]
+        }
+      ],
       "source": [
         "# The assertions below should pass once you have completed the exercise.\n",
         "assert set(merged.data_vars) == {\"air_temperature\", \"daily_precip\"}\n",
@@ -224,15 +425,97 @@
       ]
     },
     {
-      "cell_type": "markdown",
+      "cell_type": "code",
+      "execution_count": 22,
       "id": "d1c320a1",
       "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1200x300 with 4 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# plot map\n",
+        "fig, axs = plt.subplots(1, 2, figsize=(12, 3),\n",
+        "                        subplot_kw={\"projection\": ccrs.PlateCarree()})\n",
+        "\n",
+        "merged.air_temperature.isel(time=0).plot(\n",
+        "    ax=axs[0], transform=ccrs.PlateCarree(),\n",
+        "    cmap=\"jet\", cbar_kwargs={\"label\": \"Air Temp (K)\"},\n",
+        ")\n",
+        "axs[0].coastlines()\n",
+        "axs[0].set_title(f\"Air Temp\")\n",
+        "\n",
+        "merged.daily_precip.isel(time=0).plot(\n",
+        "    ax=axs[1], transform=ccrs.PlateCarree(),\n",
+        "    cmap=\"Blues\", cbar_kwargs={\"label\": \"Precip (mm/day)\"}\n",
+        ")\n",
+        "axs[1].coastlines()\n",
+        "axs[1].set_title(f\"Precip\")\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 33,
+      "id": "182df097",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 600x500 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# plot scatter\n",
+        "air_vals = merged.air_temperature.isel(time=0).values\n",
+        "precip_vals = merged.daily_precip.isel(time=0).values\n",
+        "\n",
+        "plt.figure(figsize=(6, 5))\n",
+        "plt.scatter(air_vals, precip_vals, s=5, alpha=.5)\n",
+        "plt.xlabel(\"Air Temp (K)\")\n",
+        "plt.ylabel(\"Precip (mm/day)\")\n",
+        "plt.title(f\"Scatter: Air Temperature vs Daily Precipitation\")\n",
+        "plt.grid(True, alpha=.3)\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "id": "f9689ea9",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# save\n",
+        "merged.to_netcdf(\"../US_air-temp_daily-precip.nc\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "837c7b5e",
+      "metadata": {},
+      "outputs": [],
       "source": []
     }
   ],
   "metadata": {
     "kernelspec": {
-      "display_name": "xarray-climate",
+      "display_name": "Python 3",
       "language": "python",
       "name": "python3"
     },