diff --git a/notebooks/merge_climate_datasets_exercise.ipynb b/notebooks/merge_climate_datasets_exercise.ipynb
index 9be1adf..630b671 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": 1,
       "id": "0a656265",
       "metadata": {
         "tags": [
@@ -69,18 +69,635 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 6,
       "id": "3270985f",
       "metadata": {
         "tags": [
           "exercise"
         ]
       },
-      "outputs": [],
+      "outputs": [
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+              "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 62MB\n",
+              "Dimensions:  (time: 1464, lat: 73, lon: 144)\n",
+              "Coordinates:\n",
+              "  * lat      (lat) float32 292B 90.0 87.5 85.0 82.5 ... -82.5 -85.0 -87.5 -90.0\n",
+              "  * lon      (lon) float32 576B 0.0 2.5 5.0 7.5 10.0 ... 350.0 352.5 355.0 357.5\n",
+              "  * time     (time) datetime64[ns] 12kB 2020-01-01 ... 2020-12-31T18:00:00\n",
+              "Data variables:\n",
+              "    air      (time, lat, lon) float32 62MB dask.array&lt;chunksize=(1464, 73, 144), meta=np.ndarray&gt;\n",
+              "Attributes:\n",
+              "    Conventions:                     COARDS\n",
+              "    title:                           4x daily NMC reanalysis (2014)\n",
+              "    history:                         created 2017/12 by Hoop (netCDF2.3)\n",
+              "    description:                     Data is from NMC initialized reanalysis\\...\n",
+              "    platform:                        Model\n",
+              "    dataset_title:                   NCEP-NCAR Reanalysis 1\n",
+              "    _NCProperties:                   version=2,netcdf=4.6.3,hdf5=1.10.5\n",
+              "    References:                      http://www.psl.noaa.gov/data/gridded/dat...\n",
+              "    DODS_EXTRA.Unlimited_Dimension:  time</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-46c23018-3715-4d02-8e36-b845d63edb98' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-46c23018-3715-4d02-8e36-b845d63edb98' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1464</li><li><span class='xr-has-index'>lat</span>: 73</li><li><span class='xr-has-index'>lon</span>: 144</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-78111658-802f-484e-b309-f5a271fec2a6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-78111658-802f-484e-b309-f5a271fec2a6' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>90.0 87.5 85.0 ... -87.5 -90.0</div><input id='attrs-3c59a603-2bda-4e9b-a727-fabb1dbfe001' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3c59a603-2bda-4e9b-a727-fabb1dbfe001' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-307ba0ae-66c8-4bf4-bd10-e0e7876f73f1' class='xr-var-data-in' type='checkbox'><label for='data-307ba0ae-66c8-4bf4-bd10-e0e7876f73f1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>actual_range :</span></dt><dd>[ 90. -90.]</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([ 90. ,  87.5,  85. ,  82.5,  80. ,  77.5,  75. ,  72.5,  70. ,  67.5,\n",
+              "        65. ,  62.5,  60. ,  57.5,  55. ,  52.5,  50. ,  47.5,  45. ,  42.5,\n",
+              "        40. ,  37.5,  35. ,  32.5,  30. ,  27.5,  25. ,  22.5,  20. ,  17.5,\n",
+              "        15. ,  12.5,  10. ,   7.5,   5. ,   2.5,   0. ,  -2.5,  -5. ,  -7.5,\n",
+              "       -10. , -12.5, -15. , -17.5, -20. , -22.5, -25. , -27.5, -30. , -32.5,\n",
+              "       -35. , -37.5, -40. , -42.5, -45. , -47.5, -50. , -52.5, -55. , -57.5,\n",
+              "       -60. , -62.5, -65. , -67.5, -70. , -72.5, -75. , -77.5, -80. , -82.5,\n",
+              "       -85. , -87.5, -90. ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 2.5 5.0 ... 352.5 355.0 357.5</div><input id='attrs-101b8cee-73b4-48fe-a9d9-534bfe90c4cd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-101b8cee-73b4-48fe-a9d9-534bfe90c4cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a2c7d659-f34a-4c56-a91d-f1c765a8592b' class='xr-var-data-in' type='checkbox'><label for='data-a2c7d659-f34a-4c56-a91d-f1c765a8592b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>actual_range :</span></dt><dd>[  0.  357.5]</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([  0. ,   2.5,   5. ,   7.5,  10. ,  12.5,  15. ,  17.5,  20. ,  22.5,\n",
+              "        25. ,  27.5,  30. ,  32.5,  35. ,  37.5,  40. ,  42.5,  45. ,  47.5,\n",
+              "        50. ,  52.5,  55. ,  57.5,  60. ,  62.5,  65. ,  67.5,  70. ,  72.5,\n",
+              "        75. ,  77.5,  80. ,  82.5,  85. ,  87.5,  90. ,  92.5,  95. ,  97.5,\n",
+              "       100. , 102.5, 105. , 107.5, 110. , 112.5, 115. , 117.5, 120. , 122.5,\n",
+              "       125. , 127.5, 130. , 132.5, 135. , 137.5, 140. , 142.5, 145. , 147.5,\n",
+              "       150. , 152.5, 155. , 157.5, 160. , 162.5, 165. , 167.5, 170. , 172.5,\n",
+              "       175. , 177.5, 180. , 182.5, 185. , 187.5, 190. , 192.5, 195. , 197.5,\n",
+              "       200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,\n",
+              "       225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,\n",
+              "       250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,\n",
+              "       275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,\n",
+              "       300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,\n",
+              "       325. , 327.5, 330. , 332.5, 335. , 337.5, 340. , 342.5, 345. , 347.5,\n",
+              "       350. , 352.5, 355. , 357.5], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-01-01 ... 2020-12-31T18:00:00</div><input id='attrs-d82aa6d1-0861-4e38-8823-730297c987f2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d82aa6d1-0861-4e38-8823-730297c987f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd29feb9-d94e-4bd4-abb0-f371de6684c8' class='xr-var-data-in' type='checkbox'><label for='data-fd29feb9-d94e-4bd4-abb0-f371de6684c8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>delta_t :</span></dt><dd>0000-00-00 06:00:00</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>actual_range :</span></dt><dd>[1928472. 1937250.]</dd><dt><span>_ChunkSizes :</span></dt><dd>512</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-01-01T00:00:00.000000000&#x27;, &#x27;2020-01-01T06:00:00.000000000&#x27;,\n",
+              "       &#x27;2020-01-01T12:00:00.000000000&#x27;, ..., &#x27;2020-12-31T06:00:00.000000000&#x27;,\n",
+              "       &#x27;2020-12-31T12:00:00.000000000&#x27;, &#x27;2020-12-31T18:00:00.000000000&#x27;],\n",
+              "      shape=(1464,), dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6015ddf2-9725-43c5-8d7c-f57ade9edd83' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6015ddf2-9725-43c5-8d7c-f57ade9edd83' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>air</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1464, 73, 144), meta=np.ndarray&gt;</div><input id='attrs-710ea12f-1595-4a7c-bccf-4f7668b984b6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-710ea12f-1595-4a7c-bccf-4f7668b984b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eff7f284-4adf-496c-8c59-1e0bef1c5325' class='xr-var-data-in' type='checkbox'><label for='data-eff7f284-4adf-496c-8c59-1e0bef1c5325' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>4xDaily Air temperature at sigma level 995</dd><dt><span>units :</span></dt><dd>degK</dd><dt><span>precision :</span></dt><dd>2</dd><dt><span>GRIB_id :</span></dt><dd>11</dd><dt><span>GRIB_name :</span></dt><dd>TMP</dd><dt><span>var_desc :</span></dt><dd>Air temperature</dd><dt><span>statistic :</span></dt><dd>Individual Obs</dd><dt><span>parent_stat :</span></dt><dd>Other</dd><dt><span>valid_range :</span></dt><dd>[185.16 331.16]</dd><dt><span>dataset :</span></dt><dd>NCEP Reanalysis</dd><dt><span>actual_range :</span></dt><dd>[188.6 324.1]</dd><dt><span>level_desc :</span></dt><dd>0.995 sigma</dd><dt><span>_ChunkSizes :</span></dt><dd>[  1  73 144]</dd></dl></div><div class='xr-var-data'><table>\n",
+              "    <tr>\n",
+              "        <td>\n",
+              "            <table style=\"border-collapse: collapse;\">\n",
+              "                <thead>\n",
+              "                    <tr>\n",
+              "                        <td> </td>\n",
+              "                        <th> Array </th>\n",
+              "                        <th> Chunk </th>\n",
+              "                    </tr>\n",
+              "                </thead>\n",
+              "                <tbody>\n",
+              "                    \n",
+              "                    <tr>\n",
+              "                        <th> Bytes </th>\n",
+              "                        <td> 58.71 MiB </td>\n",
+              "                        <td> 58.71 MiB </td>\n",
+              "                    </tr>\n",
+              "                    \n",
+              "                    <tr>\n",
+              "                        <th> Shape </th>\n",
+              "                        <td> (1464, 73, 144) </td>\n",
+              "                        <td> (1464, 73, 144) </td>\n",
+              "                    </tr>\n",
+              "                    <tr>\n",
+              "                        <th> Dask graph </th>\n",
+              "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+              "                    </tr>\n",
+              "                    <tr>\n",
+              "                        <th> Data type </th>\n",
+              "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+              "                    </tr>\n",
+              "                </tbody>\n",
+              "            </table>\n",
+              "        </td>\n",
+              "        <td>\n",
+              "        <svg width=\"169\" height=\"155\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+              "\n",
+              "  <!-- Horizontal lines -->\n",
+              "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+              "  <line x1=\"10\" y1=\"34\" x2=\"80\" y2=\"105\" style=\"stroke-width:2\" />\n",
+              "\n",
+              "  <!-- Vertical lines -->\n",
+              "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"34\" style=\"stroke-width:2\" />\n",
+              "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"105\" style=\"stroke-width:2\" />\n",
+              "\n",
+              "  <!-- Colored Rectangle -->\n",
+              "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,105.07249830807874 10.0,34.484263013961076\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+              "\n",
+              "  <!-- Horizontal lines -->\n",
+              "  <line x1=\"10\" y1=\"0\" x2=\"48\" y2=\"0\" style=\"stroke-width:2\" />\n",
+              "  <line x1=\"80\" y1=\"70\" x2=\"119\" y2=\"70\" style=\"stroke-width:2\" />\n",
+              "\n",
+              "  <!-- Vertical lines -->\n",
+              "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+              "  <line x1=\"48\" y1=\"0\" x2=\"119\" y2=\"70\" style=\"stroke-width:2\" />\n",
+              "\n",
+              "  <!-- Colored Rectangle -->\n",
+              "  <polygon points=\"10.0,0.0 48.50505545286762,0.0 119.09329074698528,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+              "\n",
+              "  <!-- Horizontal lines -->\n",
+              "  <line x1=\"80\" y1=\"70\" x2=\"119\" y2=\"70\" style=\"stroke-width:2\" />\n",
+              "  <line x1=\"80\" y1=\"105\" x2=\"119\" y2=\"105\" style=\"stroke-width:2\" />\n",
+              "\n",
+              "  <!-- Vertical lines -->\n",
+              "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"105\" style=\"stroke-width:2\" />\n",
+              "  <line x1=\"119\" y1=\"70\" x2=\"119\" y2=\"105\" style=\"stroke-width:2\" />\n",
+              "\n",
+              "  <!-- Colored Rectangle -->\n",
+              "  <polygon points=\"80.58823529411765,70.58823529411765 119.09329074698528,70.58823529411765 119.09329074698528,105.07249830807874 80.58823529411765,105.07249830807874\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+              "\n",
+              "  <!-- Text -->\n",
+              "  <text x=\"99.840763\" y=\"125.072498\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >144</text>\n",
+              "  <text x=\"139.093291\" y=\"87.830367\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,139.093291,87.830367)\">73</text>\n",
+              "  <text x=\"35.294118\" y=\"89.778381\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,89.778381)\">1464</text>\n",
+              "</svg>\n",
+              "        </td>\n",
+              "    </tr>\n",
+              "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-fafacb2d-5eff-42a1-abb5-a997fa5ec6a3' class='xr-section-summary-in' type='checkbox'  ><label for='section-fafacb2d-5eff-42a1-abb5-a997fa5ec6a3' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-0251f29f-8798-428c-a895-9849a02d91fe' class='xr-index-data-in' type='checkbox'/><label for='index-0251f29f-8798-428c-a895-9849a02d91fe' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 90.0,  87.5,  85.0,  82.5,  80.0,  77.5,  75.0,  72.5,  70.0,  67.5,\n",
+              "        65.0,  62.5,  60.0,  57.5,  55.0,  52.5,  50.0,  47.5,  45.0,  42.5,\n",
+              "        40.0,  37.5,  35.0,  32.5,  30.0,  27.5,  25.0,  22.5,  20.0,  17.5,\n",
+              "        15.0,  12.5,  10.0,   7.5,   5.0,   2.5,   0.0,  -2.5,  -5.0,  -7.5,\n",
+              "       -10.0, -12.5, -15.0, -17.5, -20.0, -22.5, -25.0, -27.5, -30.0, -32.5,\n",
+              "       -35.0, -37.5, -40.0, -42.5, -45.0, -47.5, -50.0, -52.5, -55.0, -57.5,\n",
+              "       -60.0, -62.5, -65.0, -67.5, -70.0, -72.5, -75.0, -77.5, -80.0, -82.5,\n",
+              "       -85.0, -87.5, -90.0],\n",
+              "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-db0d9673-4f0b-4a18-8d96-f31443ceef8c' class='xr-index-data-in' type='checkbox'/><label for='index-db0d9673-4f0b-4a18-8d96-f31443ceef8c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0.0,   2.5,   5.0,   7.5,  10.0,  12.5,  15.0,  17.5,  20.0,  22.5,\n",
+              "       ...\n",
+              "       335.0, 337.5, 340.0, 342.5, 345.0, 347.5, 350.0, 352.5, 355.0, 357.5],\n",
+              "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=144))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-914d99e1-dab0-472e-aea4-1f8b9f818e87' class='xr-index-data-in' type='checkbox'/><label for='index-914d99e1-dab0-472e-aea4-1f8b9f818e87' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-01-01 00:00:00&#x27;, &#x27;2020-01-01 06:00:00&#x27;,\n",
+              "               &#x27;2020-01-01 12:00:00&#x27;, &#x27;2020-01-01 18:00:00&#x27;,\n",
+              "               &#x27;2020-01-02 00:00:00&#x27;, &#x27;2020-01-02 06:00:00&#x27;,\n",
+              "               &#x27;2020-01-02 12:00:00&#x27;, &#x27;2020-01-02 18:00:00&#x27;,\n",
+              "               &#x27;2020-01-03 00:00:00&#x27;, &#x27;2020-01-03 06:00:00&#x27;,\n",
+              "               ...\n",
+              "               &#x27;2020-12-29 12:00:00&#x27;, &#x27;2020-12-29 18:00:00&#x27;,\n",
+              "               &#x27;2020-12-30 00:00:00&#x27;, &#x27;2020-12-30 06:00:00&#x27;,\n",
+              "               &#x27;2020-12-30 12:00:00&#x27;, &#x27;2020-12-30 18:00:00&#x27;,\n",
+              "               &#x27;2020-12-31 00:00:00&#x27;, &#x27;2020-12-31 06:00:00&#x27;,\n",
+              "               &#x27;2020-12-31 12:00:00&#x27;, &#x27;2020-12-31 18:00:00&#x27;],\n",
+              "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=1464, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6e3b2ed9-d34f-41df-a9fa-10b32c4b7404' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6e3b2ed9-d34f-41df-a9fa-10b32c4b7404' class='xr-section-summary' >Attributes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>COARDS</dd><dt><span>title :</span></dt><dd>4x daily NMC reanalysis (2014)</dd><dt><span>history :</span></dt><dd>created 2017/12 by Hoop (netCDF2.3)</dd><dt><span>description :</span></dt><dd>Data is from NMC initialized reanalysis\n",
+              "(4x/day).  These are the 0.9950 sigma level values.</dd><dt><span>platform :</span></dt><dd>Model</dd><dt><span>dataset_title :</span></dt><dd>NCEP-NCAR Reanalysis 1</dd><dt><span>_NCProperties :</span></dt><dd>version=2,netcdf=4.6.3,hdf5=1.10.5</dd><dt><span>References :</span></dt><dd>http://www.psl.noaa.gov/data/gridded/data.ncep.reanalysis.html</dd><dt><span>DODS_EXTRA.Unlimited_Dimension :</span></dt><dd>time</dd></dl></div></li></ul></div></div>"
+            ],
+            "text/plain": [
+              "<xarray.Dataset> Size: 62MB\n",
+              "Dimensions:  (time: 1464, lat: 73, lon: 144)\n",
+              "Coordinates:\n",
+              "  * lat      (lat) float32 292B 90.0 87.5 85.0 82.5 ... -82.5 -85.0 -87.5 -90.0\n",
+              "  * lon      (lon) float32 576B 0.0 2.5 5.0 7.5 10.0 ... 350.0 352.5 355.0 357.5\n",
+              "  * time     (time) datetime64[ns] 12kB 2020-01-01 ... 2020-12-31T18:00:00\n",
+              "Data variables:\n",
+              "    air      (time, lat, lon) float32 62MB dask.array<chunksize=(1464, 73, 144), meta=np.ndarray>\n",
+              "Attributes:\n",
+              "    Conventions:                     COARDS\n",
+              "    title:                           4x daily NMC reanalysis (2014)\n",
+              "    history:                         created 2017/12 by Hoop (netCDF2.3)\n",
+              "    description:                     Data is from NMC initialized reanalysis\\...\n",
+              "    platform:                        Model\n",
+              "    dataset_title:                   NCEP-NCAR Reanalysis 1\n",
+              "    _NCProperties:                   version=2,netcdf=4.6.3,hdf5=1.10.5\n",
+              "    References:                      http://www.psl.noaa.gov/data/gridded/dat...\n",
+              "    DODS_EXTRA.Unlimited_Dimension:  time"
+            ]
+          },
+          "execution_count": 6,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
       "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"
+        "air = xr.open_dataset(TEMP_URL, chunks='auto')\n",
+        "precip = xr.open_dataset(PRECIP_URL, chunks='auto')\n",
+        "\n",
+        "air\n"
       ]
     },
     {
@@ -96,7 +713,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 7,
       "id": "264d9641",
       "metadata": {
         "tags": [
@@ -105,8 +722,18 @@
       },
       "outputs": [],
       "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(\n",
+        "    lat=slice(LAT_RANGE[1], LAT_RANGE[0]),  # lat goes from high to low (90 to -90)\n",
+        "    lon=slice(LON_RANGE_360[0], LON_RANGE_360[1]),  # lon goes from 0 to 360\n",
+        "    time=TIME_RANGE\n",
+        ")\n",
+        "\n",
+        "# Subset precipitation dataset  \n",
+        "precip_us = precip.sel(\n",
+        "    lat=slice(LAT_RANGE[1], LAT_RANGE[0]),  # lat goes from high to low\n",
+        "    lon=slice(LON_RANGE_360[0], LON_RANGE_360[1]),  # lon goes from 0 to 360\n",
+        "    time=TIME_RANGE\n",
+        ")\n"
       ]
     },
     {
@@ -121,17 +748,655 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 11,
       "id": "96529bc0",
       "metadata": {
         "tags": [
           "exercise"
         ]
       },
-      "outputs": [],
+      "outputs": [
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+              "}\n",
+              "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 46kB\n",
+              "Dimensions:  (time: 30, lat: 13, lon: 29)\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",
+              "Data variables:\n",
+              "    air      (time, lat, lon) float32 45kB dask.array&lt;chunksize=(1, 13, 29), meta=np.ndarray&gt;\n",
+              "Attributes:\n",
+              "    Conventions:                     COARDS\n",
+              "    title:                           4x daily NMC reanalysis (2014)\n",
+              "    history:                         created 2017/12 by Hoop (netCDF2.3)\n",
+              "    description:                     Data is from NMC initialized reanalysis\\...\n",
+              "    platform:                        Model\n",
+              "    dataset_title:                   NCEP-NCAR Reanalysis 1\n",
+              "    _NCProperties:                   version=2,netcdf=4.6.3,hdf5=1.10.5\n",
+              "    References:                      http://www.psl.noaa.gov/data/gridded/dat...\n",
+              "    DODS_EXTRA.Unlimited_Dimension:  time</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-db13d63e-41b3-419a-a696-3a762f1852f6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-db13d63e-41b3-419a-a696-3a762f1852f6' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 30</li><li><span class='xr-has-index'>lat</span>: 13</li><li><span class='xr-has-index'>lon</span>: 29</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c797a0f8-fc37-4efb-9284-ad9f34df9d55' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c797a0f8-fc37-4efb-9284-ad9f34df9d55' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>50.0 47.5 45.0 ... 25.0 22.5 20.0</div><input id='attrs-7e619304-9993-4eca-ad85-f8eba2713307' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7e619304-9993-4eca-ad85-f8eba2713307' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a272eb7c-9055-4c48-8a08-4ba76ccc09c4' class='xr-var-data-in' type='checkbox'><label for='data-a272eb7c-9055-4c48-8a08-4ba76ccc09c4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>actual_range :</span></dt><dd>[ 90. -90.]</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([50. , 47.5, 45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5,\n",
+              "       20. ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>230.0 232.5 235.0 ... 297.5 300.0</div><input id='attrs-97769ad5-4c4b-4264-bb8a-625fe639157b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-97769ad5-4c4b-4264-bb8a-625fe639157b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74cb00bd-6c32-4731-8dea-bc90efdd4821' class='xr-var-data-in' type='checkbox'><label for='data-74cb00bd-6c32-4731-8dea-bc90efdd4821' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>actual_range :</span></dt><dd>[  0.  357.5]</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5, 250. , 252.5,\n",
+              "       255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5, 275. , 277.5,\n",
+              "       280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5, 300. ],\n",
+              "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-06-01 ... 2020-06-30</div><input id='attrs-c345a405-0386-41c5-afa4-29e7c52b3cea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c345a405-0386-41c5-afa4-29e7c52b3cea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7c646c3-0f1b-4e1e-9f6e-72875cbdbb82' class='xr-var-data-in' type='checkbox'><label for='data-b7c646c3-0f1b-4e1e-9f6e-72875cbdbb82' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>delta_t :</span></dt><dd>0000-00-00 06:00:00</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>actual_range :</span></dt><dd>[1928472. 1937250.]</dd><dt><span>_ChunkSizes :</span></dt><dd>512</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-06-01T00:00:00.000000000&#x27;, &#x27;2020-06-02T00:00:00.000000000&#x27;,\n",
+              "       &#x27;2020-06-03T00:00:00.000000000&#x27;, &#x27;2020-06-04T00:00:00.000000000&#x27;,\n",
+              "       &#x27;2020-06-05T00:00:00.000000000&#x27;, &#x27;2020-06-06T00:00:00.000000000&#x27;,\n",
+              "       &#x27;2020-06-07T00:00:00.000000000&#x27;, &#x27;2020-06-08T00:00:00.000000000&#x27;,\n",
+              "       &#x27;2020-06-09T00:00:00.000000000&#x27;, &#x27;2020-06-10T00:00:00.000000000&#x27;,\n",
+              "       &#x27;2020-06-11T00:00:00.000000000&#x27;, &#x27;2020-06-12T00:00:00.000000000&#x27;,\n",
+              "       &#x27;2020-06-13T00:00:00.000000000&#x27;, &#x27;2020-06-14T00:00:00.000000000&#x27;,\n",
+              "       &#x27;2020-06-15T00:00:00.000000000&#x27;, &#x27;2020-06-16T00:00:00.000000000&#x27;,\n",
+              "       &#x27;2020-06-17T00:00:00.000000000&#x27;, &#x27;2020-06-18T00:00:00.000000000&#x27;,\n",
+              "       &#x27;2020-06-19T00:00:00.000000000&#x27;, &#x27;2020-06-20T00:00:00.000000000&#x27;,\n",
+              "       &#x27;2020-06-21T00:00:00.000000000&#x27;, &#x27;2020-06-22T00:00:00.000000000&#x27;,\n",
+              "       &#x27;2020-06-23T00:00:00.000000000&#x27;, &#x27;2020-06-24T00:00:00.000000000&#x27;,\n",
+              "       &#x27;2020-06-25T00:00:00.000000000&#x27;, &#x27;2020-06-26T00:00:00.000000000&#x27;,\n",
+              "       &#x27;2020-06-27T00:00:00.000000000&#x27;, &#x27;2020-06-28T00:00:00.000000000&#x27;,\n",
+              "       &#x27;2020-06-29T00:00:00.000000000&#x27;, &#x27;2020-06-30T00:00:00.000000000&#x27;],\n",
+              "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8cdea270-82e9-4bca-89a7-5fd10a0a083b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8cdea270-82e9-4bca-89a7-5fd10a0a083b' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>air</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 13, 29), meta=np.ndarray&gt;</div><input id='attrs-62260c94-33f8-417b-afef-f14a405764ca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-62260c94-33f8-417b-afef-f14a405764ca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-95542773-e1bb-40d1-99e1-b2297c733620' class='xr-var-data-in' type='checkbox'><label for='data-95542773-e1bb-40d1-99e1-b2297c733620' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>4xDaily Air temperature at sigma level 995</dd><dt><span>units :</span></dt><dd>degK</dd><dt><span>precision :</span></dt><dd>2</dd><dt><span>GRIB_id :</span></dt><dd>11</dd><dt><span>GRIB_name :</span></dt><dd>TMP</dd><dt><span>var_desc :</span></dt><dd>Air temperature</dd><dt><span>statistic :</span></dt><dd>Individual Obs</dd><dt><span>parent_stat :</span></dt><dd>Other</dd><dt><span>valid_range :</span></dt><dd>[185.16 331.16]</dd><dt><span>dataset :</span></dt><dd>NCEP Reanalysis</dd><dt><span>actual_range :</span></dt><dd>[188.6 324.1]</dd><dt><span>level_desc :</span></dt><dd>0.995 sigma</dd><dt><span>_ChunkSizes :</span></dt><dd>[  1  73 144]</dd></dl></div><div class='xr-var-data'><table>\n",
+              "    <tr>\n",
+              "        <td>\n",
+              "            <table style=\"border-collapse: collapse;\">\n",
+              "                <thead>\n",
+              "                    <tr>\n",
+              "                        <td> </td>\n",
+              "                        <th> Array </th>\n",
+              "                        <th> Chunk </th>\n",
+              "                    </tr>\n",
+              "                </thead>\n",
+              "                <tbody>\n",
+              "                    \n",
+              "                    <tr>\n",
+              "                        <th> Bytes </th>\n",
+              "                        <td> 44.18 kiB </td>\n",
+              "                        <td> 1.47 kiB </td>\n",
+              "                    </tr>\n",
+              "                    \n",
+              "                    <tr>\n",
+              "                        <th> Shape </th>\n",
+              "                        <td> (30, 13, 29) </td>\n",
+              "                        <td> (1, 13, 29) </td>\n",
+              "                    </tr>\n",
+              "                    <tr>\n",
+              "                        <th> Dask graph </th>\n",
+              "                        <td colspan=\"2\"> 30 chunks in 124 graph layers </td>\n",
+              "                    </tr>\n",
+              "                    <tr>\n",
+              "                        <th> Data type </th>\n",
+              "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+              "                    </tr>\n",
+              "                </tbody>\n",
+              "            </table>\n",
+              "        </td>\n",
+              "        <td>\n",
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+              "</svg>\n",
+              "        </td>\n",
+              "    </tr>\n",
+              "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-becbba88-932b-4fab-8da3-b46ed04ee125' class='xr-section-summary-in' type='checkbox'  ><label for='section-becbba88-932b-4fab-8da3-b46ed04ee125' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-b4de4873-df34-44fc-b676-b5ef6c87d525' class='xr-index-data-in' type='checkbox'/><label for='index-b4de4873-df34-44fc-b676-b5ef6c87d525' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([50.0, 47.5, 45.0, 42.5, 40.0, 37.5, 35.0, 32.5, 30.0, 27.5, 25.0, 22.5,\n",
+              "       20.0],\n",
+              "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-9169125f-c156-44cb-9f0e-c6717bc837bc' class='xr-index-data-in' type='checkbox'/><label for='index-9169125f-c156-44cb-9f0e-c6717bc837bc' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([230.0, 232.5, 235.0, 237.5, 240.0, 242.5, 245.0, 247.5, 250.0, 252.5,\n",
+              "       255.0, 257.5, 260.0, 262.5, 265.0, 267.5, 270.0, 272.5, 275.0, 277.5,\n",
+              "       280.0, 282.5, 285.0, 287.5, 290.0, 292.5, 295.0, 297.5, 300.0],\n",
+              "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-a6c5aea4-8187-4280-bd9c-00f791553882' class='xr-index-data-in' type='checkbox'/><label for='index-a6c5aea4-8187-4280-bd9c-00f791553882' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-06-01&#x27;, &#x27;2020-06-02&#x27;, &#x27;2020-06-03&#x27;, &#x27;2020-06-04&#x27;,\n",
+              "               &#x27;2020-06-05&#x27;, &#x27;2020-06-06&#x27;, &#x27;2020-06-07&#x27;, &#x27;2020-06-08&#x27;,\n",
+              "               &#x27;2020-06-09&#x27;, &#x27;2020-06-10&#x27;, &#x27;2020-06-11&#x27;, &#x27;2020-06-12&#x27;,\n",
+              "               &#x27;2020-06-13&#x27;, &#x27;2020-06-14&#x27;, &#x27;2020-06-15&#x27;, &#x27;2020-06-16&#x27;,\n",
+              "               &#x27;2020-06-17&#x27;, &#x27;2020-06-18&#x27;, &#x27;2020-06-19&#x27;, &#x27;2020-06-20&#x27;,\n",
+              "               &#x27;2020-06-21&#x27;, &#x27;2020-06-22&#x27;, &#x27;2020-06-23&#x27;, &#x27;2020-06-24&#x27;,\n",
+              "               &#x27;2020-06-25&#x27;, &#x27;2020-06-26&#x27;, &#x27;2020-06-27&#x27;, &#x27;2020-06-28&#x27;,\n",
+              "               &#x27;2020-06-29&#x27;, &#x27;2020-06-30&#x27;],\n",
+              "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=&#x27;D&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3bfe5e98-0dc1-498b-82bf-f81a26d82e74' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3bfe5e98-0dc1-498b-82bf-f81a26d82e74' class='xr-section-summary' >Attributes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>COARDS</dd><dt><span>title :</span></dt><dd>4x daily NMC reanalysis (2014)</dd><dt><span>history :</span></dt><dd>created 2017/12 by Hoop (netCDF2.3)</dd><dt><span>description :</span></dt><dd>Data is from NMC initialized reanalysis\n",
+              "(4x/day).  These are the 0.9950 sigma level values.</dd><dt><span>platform :</span></dt><dd>Model</dd><dt><span>dataset_title :</span></dt><dd>NCEP-NCAR Reanalysis 1</dd><dt><span>_NCProperties :</span></dt><dd>version=2,netcdf=4.6.3,hdf5=1.10.5</dd><dt><span>References :</span></dt><dd>http://www.psl.noaa.gov/data/gridded/data.ncep.reanalysis.html</dd><dt><span>DODS_EXTRA.Unlimited_Dimension :</span></dt><dd>time</dd></dl></div></li></ul></div></div>"
+            ],
+            "text/plain": [
+              "<xarray.Dataset> Size: 46kB\n",
+              "Dimensions:  (time: 30, lat: 13, lon: 29)\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",
+              "Data variables:\n",
+              "    air      (time, lat, lon) float32 45kB dask.array<chunksize=(1, 13, 29), meta=np.ndarray>\n",
+              "Attributes:\n",
+              "    Conventions:                     COARDS\n",
+              "    title:                           4x daily NMC reanalysis (2014)\n",
+              "    history:                         created 2017/12 by Hoop (netCDF2.3)\n",
+              "    description:                     Data is from NMC initialized reanalysis\\...\n",
+              "    platform:                        Model\n",
+              "    dataset_title:                   NCEP-NCAR Reanalysis 1\n",
+              "    _NCProperties:                   version=2,netcdf=4.6.3,hdf5=1.10.5\n",
+              "    References:                      http://www.psl.noaa.gov/data/gridded/dat...\n",
+              "    DODS_EXTRA.Unlimited_Dimension:  time"
+            ]
+          },
+          "execution_count": 11,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
       "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",
+        "air_daily"
       ]
     },
     {
@@ -146,7 +1411,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 14,
       "id": "52eb7321",
       "metadata": {
         "tags": [
@@ -155,8 +1420,7 @@
       },
       "outputs": [],
       "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)"
       ]
     },
     {
@@ -171,7 +1435,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 15,
       "id": "b766aff5",
       "metadata": {
         "tags": [
@@ -180,8 +1444,10 @@
       },
       "outputs": [],
       "source": [
-        "# TODO: build datasets and merge them into one object named `merged`.\n",
-        "raise NotImplementedError(\"Create `merged`.\")\n"
+        "merged = xr.merge([\n",
+        "    air_interp.rename({'air': 'air_temperature'}),\n",
+        "    precip_us.rename({'precip': 'daily_precip'})\n",
+        "])"
       ]
     },
     {
@@ -196,14 +1462,40 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 16,
       "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=(30, 60, 50), meta=np.ndarray>\n",
+            "Attributes:\n",
+            "    Conventions:                     COARDS\n",
+            "    title:                           4x daily NMC reanalysis (2014)\n",
+            "    history:                         created 2017/12 by Hoop (netCDF2.3)\n",
+            "    description:                     Data is from NMC initialized reanalysis\\...\n",
+            "    platform:                        Model\n",
+            "    dataset_title:                   NCEP-NCAR Reanalysis 1\n",
+            "    _NCProperties:                   version=2,netcdf=4.6.3,hdf5=1.10.5\n",
+            "    References:                      http://www.psl.noaa.gov/data/gridded/dat...\n",
+            "    DODS_EXTRA.Unlimited_Dimension:  time\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 +1516,67 @@
       ]
     },
     {
-      "cell_type": "markdown",
+      "cell_type": "code",
+      "execution_count": 35,
       "id": "d1c320a1",
       "metadata": {},
-      "source": []
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1600x600 with 4 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Setup plot\n",
+        "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 6), \n",
+        "                               subplot_kw={'projection': ccrs.PlateCarree()})\n",
+        "\n",
+        "# Add features\n",
+        "for ax in [ax1, ax2]:\n",
+        "    ax.add_feature(cfeature.COASTLINE, linewidth=0.8)\n",
+        "    ax.add_feature(cfeature.BORDERS, linewidth=0.5)\n",
+        "    ax.add_feature(cfeature.STATES, linewidth=0.3)\n",
+        "    ax.add_feature(cfeature.OCEAN, color='lightblue', alpha=0.3)\n",
+        "    ax.set_extent([LON_RANGE_180[0], LON_RANGE_180[1], LAT_RANGE[0], LAT_RANGE[1]], \n",
+        "                  crs=ccrs.PlateCarree())\n",
+        "\n",
+        "# Grab the first timestep\n",
+        "first_timestep = merged.isel(time=0)\n",
+        "first_date = first_timestep.time.dt.strftime('%Y-%m-%d').values\n",
+        "\n",
+        "# Plot air temperature\n",
+        "air_temp_plot = first_timestep.air_temperature.plot(\n",
+        "    ax=ax1, \n",
+        "    transform=ccrs.PlateCarree(),\n",
+        "    cmap='RdBu_r',\n",
+        "    add_colorbar=True,\n",
+        "    cbar_kwargs={'orientation': 'horizontal', 'label': 'Air Temperature at 0.995 sigma level (K)'}\n",
+        ")\n",
+        "ax1.set_title(f'Average Near-Surface Air Temperature\\n{first_date}')\n",
+        "\n",
+        "# Plot precipitation\n",
+        "precip_plot = first_timestep.daily_precip.plot(\n",
+        "    ax=ax2, \n",
+        "    transform=ccrs.PlateCarree(),\n",
+        "    cmap='Blues',\n",
+        "    add_colorbar=True,\n",
+        "    cbar_kwargs={'orientation': 'horizontal', 'label': 'Precipitation (mm)'}\n",
+        ")\n",
+        "ax2.set_title(f'Average Precipitation\\n{first_date}')\n",
+        "\n",
+        "plt.show()"
+      ]
     }
   ],
   "metadata": {
     "kernelspec": {
-      "display_name": "xarray-climate",
+      "display_name": "Python 3",
       "language": "python",
       "name": "python3"
     },