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+   "id": "bcf23fc8-ae63-4efe-84bb-c67acdffb973",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "title: \"Part 2 of the In-cloud Science Workflow: Data subsetting and plotting with earthaccess, xarray, and harmony\"\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a28d9430-1a3e-480c-bf15-c35f938b4210",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "Welcome to Part 2 of the In-cloud Science Workflow workshop.\n",
+    "\n",
+    "In these examples we will use the [xarray](https://xarray.dev/), [earthaccess](https://nsidc.github.io/earthaccess/), and [harmony-py](https://github.com/nasa/harmony-py) libraries to subset data and make figures using `cartopy`, `matplotlib`, and `geoviews`.\n",
+    "\n",
+    "We will go through **two examples of subsetting and plotting data in the Earthdata Cloud:** \n",
+    "\n",
+    "1. Example 1 - `earthaccess` and `xarray` for precipitation estimates from [IMERG, Daily Level 3 data](https://doi.org/10.5067/GPM/IMERGDF/DAY/07)\n",
+    "2. Example 2 - `harmony-py` for direct cloud subsetting of precipitable water data from the [DSCOVR EPIC Composite](https://doi.org/10.5067/EPIC/DSCOVR/L2_COMPOSITE_01).\n",
+    "3. Appendix 1 - Snow cover data from [MODIS/Terra, Daily Level 3 data](https://doi.org/10.5067/MODIS/MOD10C1.061) with `rioxarray`\n",
+    "4. Appendix 2 - Snow mass data from [SMAP, 3-hourly Level 4 data](https://doi.org/10.5067/EVKPQZ4AFC4D)\n",
+    "    \n",
+    "In the first example, we will be accessing data directly from Amazon Web Services (AWS), specifically in the us-west-2 region, which is where all cloud-hosted NASA Earthdata reside. This shared compute environment (JupyterHub) is also running in the same location. We will then load the data into Python as an `xarray` dataset and use `xarray` to subset.\n",
+    "\n",
+    "In the second example, we will demonstrate an example of pulling data via the cloud from an existing on-premise data server.  In this example, the data are subsetted using one of the data transformation services provided in the NASA Earthdata system. Both `xarray` and `harmony-py` can be run outside of AWS as well.\n",
+    "\n",
+    "See the bottom of the notebook for additional resources, including several tutorials that that served as a foundation for this clinic. Includes: https://github.com/rupesh2/atmospheric_rivers/tree/main\n",
+    "\n",
+    "Note: \"direct cloud access\" is also called \"direct S3 access\" or simply \"direct access\".\n",
+    "\n",
+    "## Learning Objectives\n",
+    "\n",
+    "1. Extract variables, temporal slices, and spatial slices from an `xarray` dataset \n",
+    "2. Plot data and exclude data points via boolean conditions, using `xarray`, `cartopy`, `matplotlib`, and `rasterio`\n",
+    "3. Plot a polygon geojson file with a basemap using `geoviews` \n",
+    "4. Conceptualize data subsetting services provided by NASA Earthdata, including Harmony\n",
+    "5. Utilize the `harmony-py` library to request data over the Bay of San Francisco"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "66d78efc-2d62-428a-a813-e58f949ee1bf",
+   "metadata": {},
+   "source": [
+    "### Import Required Packages"
+   ]
+  },
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+   "execution_count": 1,
+   "id": "f04a653d-b9e5-4cfe-a198-b5b612389742",
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+       "}(window));"
+      ],
+      "application/vnd.holoviews_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n  var py_version = '3.2.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n  var is_dev = py_version.indexOf(\"+\") !== -1 || py_version.indexOf(\"-\") !== -1;\n  var reloading = false;\n  var Bokeh = root.Bokeh;\n  var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks;\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n    if (js_modules == null) js_modules = [];\n    if (js_exports == null) js_exports = {};\n\n    root._bokeh_onload_callbacks.push(callback);\n\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n      run_callbacks();\n      return null;\n    }\n    if (!reloading) {\n      console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    }\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n    window._bokeh_on_load = on_load\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    var skip = [];\n    if (window.requirejs) {\n      window.requirejs.config({'packages': {}, 'paths': {'jspanel': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/jspanel', 'jspanel-modal': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal', 'jspanel-tooltip': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip', 'jspanel-hint': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint', 'jspanel-layout': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout', 'jspanel-contextmenu': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu', 'jspanel-dock': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock', 'gridstack': 'https://cdn.jsdelivr.net/npm/gridstack@7.2.3/dist/gridstack-all', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'jspanel': {'exports': 'jsPanel'}, 'gridstack': {'exports': 'GridStack'}}});\n      require([\"jspanel\"], function(jsPanel) {\n\twindow.jsPanel = jsPanel\n\ton_load()\n      })\n      require([\"jspanel-modal\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-tooltip\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-hint\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-layout\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-contextmenu\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-dock\"], function() {\n\ton_load()\n      })\n      require([\"gridstack\"], function(GridStack) {\n\twindow.GridStack = GridStack\n\ton_load()\n      })\n      require([\"notyf\"], function() {\n\ton_load()\n      })\n      root._bokeh_is_loading = css_urls.length + 9;\n    } else {\n      root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n    }\n\n    var existing_stylesheets = []\n    var links = document.getElementsByTagName('link')\n    for (var i = 0; i < links.length; i++) {\n      var link = links[i]\n      if (link.href != null) {\n\texisting_stylesheets.push(link.href)\n      }\n    }\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      if (existing_stylesheets.indexOf(url) !== -1) {\n\ton_load()\n\tcontinue;\n      }\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }    if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    if (((window['GridStack'] !== undefined) && (!(window['GridStack'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/gridstack/gridstack@7.2.3/dist/gridstack-all.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    if (((window['Notyf'] !== undefined) && (!(window['Notyf'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/notificationarea/notyf@3/notyf.min.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    var existing_scripts = []\n    var scripts = document.getElementsByTagName('script')\n    for (var i = 0; i < scripts.length; i++) {\n      var script = scripts[i]\n      if (script.src != null) {\n\texisting_scripts.push(script.src)\n      }\n    }\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (var i = 0; i < js_modules.length; i++) {\n      var url = js_modules[i];\n      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (const name in js_exports) {\n      var url = js_exports[name];\n      if (skip.indexOf(url) >= 0 || root[name] != null) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onerror = on_error;\n      element.async = false;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      element.textContent = `\n      import ${name} from \"${url}\"\n      window.${name} = ${name}\n      window._bokeh_on_load()\n      `\n      document.head.appendChild(element);\n    }\n    if (!js_urls.length && !js_modules.length) {\n      on_load()\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.2.2.min.js\", \"https://cdn.holoviz.org/panel/1.2.3/dist/panel.min.js\", \"https://cdn.jsdelivr.net/npm/@holoviz/geoviews@1.10.1/dist/geoviews.min.js\"];\n  var js_modules = [];\n  var js_exports = {};\n  var css_urls = [];\n  var inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n  ];\n\n  function run_inline_js() {\n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }\n      // Cache old bokeh versions\n      if (Bokeh != undefined && !reloading) {\n\tvar NewBokeh = root.Bokeh;\n\tif (Bokeh.versions === undefined) {\n\t  Bokeh.versions = new Map();\n\t}\n\tif (NewBokeh.version !== Bokeh.version) {\n\t  Bokeh.versions.set(NewBokeh.version, NewBokeh)\n\t}\n\troot.Bokeh = Bokeh;\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    }\n    root._bokeh_is_initializing = false\n  }\n\n  function load_or_wait() {\n    // Implement a backoff loop that tries to ensure we do not load multiple\n    // versions of Bokeh and its dependencies at the same time.\n    // In recent versions we use the root._bokeh_is_initializing flag\n    // to determine whether there is an ongoing attempt to initialize\n    // bokeh, however for backward compatibility we also try to ensure\n    // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n    // before older versions are fully initialized.\n    if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n      root._bokeh_is_initializing = false;\n      root._bokeh_onload_callbacks = undefined;\n      console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n      load_or_wait();\n    } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n      setTimeout(load_or_wait, 100);\n    } else {\n      Bokeh = root.Bokeh;\n      bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n      root._bokeh_is_initializing = true\n      root._bokeh_onload_callbacks = []\n      if (!reloading && (!bokeh_loaded || is_dev)) {\n\troot.Bokeh = undefined;\n      }\n      load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n\trun_inline_js();\n      });\n    }\n  }\n  // Give older versions of the autoload script a head-start to ensure\n  // they initialize before we start loading newer version.\n  setTimeout(load_or_wait, 100)\n}(window));"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/javascript": [
+       "\n",
+       "if ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n",
+       "  window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n",
+       "}\n",
+       "\n",
+       "\n",
+       "    function JupyterCommManager() {\n",
+       "    }\n",
+       "\n",
+       "    JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n",
+       "      if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n",
+       "        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n",
+       "        comm_manager.register_target(comm_id, function(comm) {\n",
+       "          comm.on_msg(msg_handler);\n",
+       "        });\n",
+       "      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n",
+       "        window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n",
+       "          comm.onMsg = msg_handler;\n",
+       "        });\n",
+       "      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n",
+       "        google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n",
+       "          var messages = comm.messages[Symbol.asyncIterator]();\n",
+       "          function processIteratorResult(result) {\n",
+       "            var message = result.value;\n",
+       "            console.log(message)\n",
+       "            var content = {data: message.data, comm_id};\n",
+       "            var buffers = []\n",
+       "            for (var buffer of message.buffers || []) {\n",
+       "              buffers.push(new DataView(buffer))\n",
+       "            }\n",
+       "            var metadata = message.metadata || {};\n",
+       "            var msg = {content, buffers, metadata}\n",
+       "            msg_handler(msg);\n",
+       "            return messages.next().then(processIteratorResult);\n",
+       "          }\n",
+       "          return messages.next().then(processIteratorResult);\n",
+       "        })\n",
+       "      }\n",
+       "    }\n",
+       "\n",
+       "    JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n",
+       "      if (comm_id in window.PyViz.comms) {\n",
+       "        return window.PyViz.comms[comm_id];\n",
+       "      } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n",
+       "        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n",
+       "        var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n",
+       "        if (msg_handler) {\n",
+       "          comm.on_msg(msg_handler);\n",
+       "        }\n",
+       "      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n",
+       "        var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n",
+       "        comm.open();\n",
+       "        if (msg_handler) {\n",
+       "          comm.onMsg = msg_handler;\n",
+       "        }\n",
+       "      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n",
+       "        var comm_promise = google.colab.kernel.comms.open(comm_id)\n",
+       "        comm_promise.then((comm) => {\n",
+       "          window.PyViz.comms[comm_id] = comm;\n",
+       "          if (msg_handler) {\n",
+       "            var messages = comm.messages[Symbol.asyncIterator]();\n",
+       "            function processIteratorResult(result) {\n",
+       "              var message = result.value;\n",
+       "              var content = {data: message.data};\n",
+       "              var metadata = message.metadata || {comm_id};\n",
+       "              var msg = {content, metadata}\n",
+       "              msg_handler(msg);\n",
+       "              return messages.next().then(processIteratorResult);\n",
+       "            }\n",
+       "            return messages.next().then(processIteratorResult);\n",
+       "          }\n",
+       "        }) \n",
+       "        var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n",
+       "          return comm_promise.then((comm) => {\n",
+       "            comm.send(data, metadata, buffers, disposeOnDone);\n",
+       "          });\n",
+       "        };\n",
+       "        var comm = {\n",
+       "          send: sendClosure\n",
+       "        };\n",
+       "      }\n",
+       "      window.PyViz.comms[comm_id] = comm;\n",
+       "      return comm;\n",
+       "    }\n",
+       "    window.PyViz.comm_manager = new JupyterCommManager();\n",
+       "    \n",
+       "\n",
+       "\n",
+       "var JS_MIME_TYPE = 'application/javascript';\n",
+       "var HTML_MIME_TYPE = 'text/html';\n",
+       "var EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\n",
+       "var CLASS_NAME = 'output';\n",
+       "\n",
+       "/**\n",
+       " * Render data to the DOM node\n",
+       " */\n",
+       "function render(props, node) {\n",
+       "  var div = document.createElement(\"div\");\n",
+       "  var script = document.createElement(\"script\");\n",
+       "  node.appendChild(div);\n",
+       "  node.appendChild(script);\n",
+       "}\n",
+       "\n",
+       "/**\n",
+       " * Handle when a new output is added\n",
+       " */\n",
+       "function handle_add_output(event, handle) {\n",
+       "  var output_area = handle.output_area;\n",
+       "  var output = handle.output;\n",
+       "  if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
+       "    return\n",
+       "  }\n",
+       "  var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
+       "  var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
+       "  if (id !== undefined) {\n",
+       "    var nchildren = toinsert.length;\n",
+       "    var html_node = toinsert[nchildren-1].children[0];\n",
+       "    html_node.innerHTML = output.data[HTML_MIME_TYPE];\n",
+       "    var scripts = [];\n",
+       "    var nodelist = html_node.querySelectorAll(\"script\");\n",
+       "    for (var i in nodelist) {\n",
+       "      if (nodelist.hasOwnProperty(i)) {\n",
+       "        scripts.push(nodelist[i])\n",
+       "      }\n",
+       "    }\n",
+       "\n",
+       "    scripts.forEach( function (oldScript) {\n",
+       "      var newScript = document.createElement(\"script\");\n",
+       "      var attrs = [];\n",
+       "      var nodemap = oldScript.attributes;\n",
+       "      for (var j in nodemap) {\n",
+       "        if (nodemap.hasOwnProperty(j)) {\n",
+       "          attrs.push(nodemap[j])\n",
+       "        }\n",
+       "      }\n",
+       "      attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n",
+       "      newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n",
+       "      oldScript.parentNode.replaceChild(newScript, oldScript);\n",
+       "    });\n",
+       "    if (JS_MIME_TYPE in output.data) {\n",
+       "      toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n",
+       "    }\n",
+       "    output_area._hv_plot_id = id;\n",
+       "    if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n",
+       "      window.PyViz.plot_index[id] = Bokeh.index[id];\n",
+       "    } else {\n",
+       "      window.PyViz.plot_index[id] = null;\n",
+       "    }\n",
+       "  } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
+       "    var bk_div = document.createElement(\"div\");\n",
+       "    bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
+       "    var script_attrs = bk_div.children[0].attributes;\n",
+       "    for (var i = 0; i < script_attrs.length; i++) {\n",
+       "      toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
+       "    }\n",
+       "    // store reference to server id on output_area\n",
+       "    output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "/**\n",
+       " * Handle when an output is cleared or removed\n",
+       " */\n",
+       "function handle_clear_output(event, handle) {\n",
+       "  var id = handle.cell.output_area._hv_plot_id;\n",
+       "  var server_id = handle.cell.output_area._bokeh_server_id;\n",
+       "  if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n",
+       "  var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n",
+       "  if (server_id !== null) {\n",
+       "    comm.send({event_type: 'server_delete', 'id': server_id});\n",
+       "    return;\n",
+       "  } else if (comm !== null) {\n",
+       "    comm.send({event_type: 'delete', 'id': id});\n",
+       "  }\n",
+       "  delete PyViz.plot_index[id];\n",
+       "  if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n",
+       "    var doc = window.Bokeh.index[id].model.document\n",
+       "    doc.clear();\n",
+       "    const i = window.Bokeh.documents.indexOf(doc);\n",
+       "    if (i > -1) {\n",
+       "      window.Bokeh.documents.splice(i, 1);\n",
+       "    }\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "/**\n",
+       " * Handle kernel restart event\n",
+       " */\n",
+       "function handle_kernel_cleanup(event, handle) {\n",
+       "  delete PyViz.comms[\"hv-extension-comm\"];\n",
+       "  window.PyViz.plot_index = {}\n",
+       "}\n",
+       "\n",
+       "/**\n",
+       " * Handle update_display_data messages\n",
+       " */\n",
+       "function handle_update_output(event, handle) {\n",
+       "  handle_clear_output(event, {cell: {output_area: handle.output_area}})\n",
+       "  handle_add_output(event, handle)\n",
+       "}\n",
+       "\n",
+       "function register_renderer(events, OutputArea) {\n",
+       "  function append_mime(data, metadata, element) {\n",
+       "    // create a DOM node to render to\n",
+       "    var toinsert = this.create_output_subarea(\n",
+       "    metadata,\n",
+       "    CLASS_NAME,\n",
+       "    EXEC_MIME_TYPE\n",
+       "    );\n",
+       "    this.keyboard_manager.register_events(toinsert);\n",
+       "    // Render to node\n",
+       "    var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
+       "    render(props, toinsert[0]);\n",
+       "    element.append(toinsert);\n",
+       "    return toinsert\n",
+       "  }\n",
+       "\n",
+       "  events.on('output_added.OutputArea', handle_add_output);\n",
+       "  events.on('output_updated.OutputArea', handle_update_output);\n",
+       "  events.on('clear_output.CodeCell', handle_clear_output);\n",
+       "  events.on('delete.Cell', handle_clear_output);\n",
+       "  events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n",
+       "\n",
+       "  OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
+       "    safe: true,\n",
+       "    index: 0\n",
+       "  });\n",
+       "}\n",
+       "\n",
+       "if (window.Jupyter !== undefined) {\n",
+       "  try {\n",
+       "    var events = require('base/js/events');\n",
+       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
+       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
+       "      register_renderer(events, OutputArea);\n",
+       "    }\n",
+       "  } catch(err) {\n",
+       "  }\n",
+       "}\n"
+      ],
+      "application/vnd.holoviews_load.v0+json": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n  window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n    function JupyterCommManager() {\n    }\n\n    JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n      if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n        comm_manager.register_target(comm_id, function(comm) {\n          comm.on_msg(msg_handler);\n        });\n      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n        window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n          comm.onMsg = msg_handler;\n        });\n      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n        google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n          var messages = comm.messages[Symbol.asyncIterator]();\n          function processIteratorResult(result) {\n            var message = result.value;\n            console.log(message)\n            var content = {data: message.data, comm_id};\n            var buffers = []\n            for (var buffer of message.buffers || []) {\n              buffers.push(new DataView(buffer))\n            }\n            var metadata = message.metadata || {};\n            var msg = {content, buffers, metadata}\n            msg_handler(msg);\n            return messages.next().then(processIteratorResult);\n          }\n          return messages.next().then(processIteratorResult);\n        })\n      }\n    }\n\n    JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n      if (comm_id in window.PyViz.comms) {\n        return window.PyViz.comms[comm_id];\n      } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n        var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n        if (msg_handler) {\n          comm.on_msg(msg_handler);\n        }\n      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n        var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n        comm.open();\n        if (msg_handler) {\n          comm.onMsg = msg_handler;\n        }\n      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n        var comm_promise = google.colab.kernel.comms.open(comm_id)\n        comm_promise.then((comm) => {\n          window.PyViz.comms[comm_id] = comm;\n          if (msg_handler) {\n            var messages = comm.messages[Symbol.asyncIterator]();\n            function processIteratorResult(result) {\n              var message = 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the DOM node\n */\nfunction render(props, node) {\n  var div = document.createElement(\"div\");\n  var script = document.createElement(\"script\");\n  node.appendChild(div);\n  node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n  var output_area = handle.output_area;\n  var output = handle.output;\n  if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n    return\n  }\n  var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n  var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n  if (id !== undefined) {\n    var nchildren = toinsert.length;\n    var html_node = toinsert[nchildren-1].children[0];\n    html_node.innerHTML = output.data[HTML_MIME_TYPE];\n    var scripts = [];\n    var nodelist = html_node.querySelectorAll(\"script\");\n    for (var i in nodelist) {\n      if (nodelist.hasOwnProperty(i)) {\n        scripts.push(nodelist[i])\n      }\n    }\n\n    scripts.forEach( function (oldScript) {\n      var newScript = document.createElement(\"script\");\n      var attrs = [];\n      var nodemap = oldScript.attributes;\n      for (var j in nodemap) {\n        if (nodemap.hasOwnProperty(j)) {\n          attrs.push(nodemap[j])\n        }\n      }\n      attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n      newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n      oldScript.parentNode.replaceChild(newScript, oldScript);\n    });\n    if (JS_MIME_TYPE in output.data) {\n      toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n    }\n    output_area._hv_plot_id = id;\n    if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n      window.PyViz.plot_index[id] = Bokeh.index[id];\n    } else {\n      window.PyViz.plot_index[id] = null;\n    }\n  } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n    var bk_div = document.createElement(\"div\");\n    bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n    var script_attrs = bk_div.children[0].attributes;\n    for (var i = 0; i < script_attrs.length; i++) {\n      toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n    }\n    // store reference to server id on output_area\n    output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n  }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n  var id = handle.cell.output_area._hv_plot_id;\n  var server_id = handle.cell.output_area._bokeh_server_id;\n  if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n  var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n  if (server_id !== null) {\n    comm.send({event_type: 'server_delete', 'id': server_id});\n    return;\n  } else if (comm !== null) {\n    comm.send({event_type: 'delete', 'id': id});\n  }\n  delete PyViz.plot_index[id];\n  if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n    var doc = window.Bokeh.index[id].model.document\n    doc.clear();\n    const i = window.Bokeh.documents.indexOf(doc);\n    if (i > -1) {\n      window.Bokeh.documents.splice(i, 1);\n    }\n  }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n  delete PyViz.comms[\"hv-extension-comm\"];\n  window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n  handle_clear_output(event, {cell: {output_area: handle.output_area}})\n  handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n  function append_mime(data, metadata, element) {\n    // create a DOM node to render to\n    var toinsert = this.create_output_subarea(\n    metadata,\n    CLASS_NAME,\n    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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>*[data-root-id],\n",
+       "*[data-root-id] > * {\n",
+       "  box-sizing: border-box;\n",
+       "  font-family: var(--jp-ui-font-family);\n",
+       "  font-size: var(--jp-ui-font-size1);\n",
+       "  color: var(--vscode-editor-foreground, var(--jp-ui-font-color1));\n",
+       "}\n",
+       "\n",
+       "/* Override VSCode background color */\n",
+       ".cell-output-ipywidget-background:has(\n",
+       "    > .cell-output-ipywidget-background > .lm-Widget > *[data-root-id]\n",
+       "  ),\n",
+       ".cell-output-ipywidget-background:has(> .lm-Widget > *[data-root-id]) {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "</style>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Suppress warnings\n",
+    "import warnings\n",
+    "warnings.simplefilter('ignore')\n",
+    "warnings.filterwarnings('ignore')\n",
+    "from pprint import pprint\n",
+    "\n",
+    "# Example 1 imports\n",
+    "import earthaccess\n",
+    "import xarray as xr\n",
+    "xr.set_options(display_expand_attrs=False)\n",
+    "import matplotlib.pyplot as plt\n",
+    "import cartopy.crs as ccrs\n",
+    "import cartopy.feature as cfeature\n",
+    "\n",
+    "# Example 2 imports (Example 1 imports plus these...)\n",
+    "import datetime as dt\n",
+    "import json\n",
+    "from cartopy.mpl.ticker import LatitudeFormatter, LongitudeFormatter\n",
+    "import geopandas as gpd\n",
+    "import geoviews as gv\n",
+    "gv.extension('bokeh', 'matplotlib', logo=False)\n",
+    "from harmony import Client, Collection, Request, CapabilitiesRequest\n",
+    "\n",
+    "# Appendix 1 imports\n",
+    "from pathlib import Path\n",
+    "import rioxarray as rxr\n",
+    "\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "442bd92a-8f2d-4448-a59e-da4567710730",
+   "metadata": {},
+   "source": [
+    "## Picking up where we left off\n",
+    "\n",
+    "We will authenticate our Earthaccess session, and then open the results like we did in the Search & Discovery section."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "0fe0002f-c759-4611-8dd7-861b8bd38971",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "EARTHDATA_USERNAME and EARTHDATA_PASSWORD are not set in the current environment, try setting them or use a different strategy (netrc, interactive)\n",
+      "You're now authenticated with NASA Earthdata Login\n",
+      "Using token with expiration date: 01/26/2024\n",
+      "Using .netrc file for EDL\n"
+     ]
+    }
+   ],
+   "source": [
+    "auth = earthaccess.login()\n",
+    "# are we authenticated?\n",
+    "if not auth.authenticated:\n",
+    "    # ask for credentials and persist them in a .netrc file\n",
+    "    auth.login(strategy=\"interactive\", persist=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a6a3cb10-6988-401e-a618-59e2f5ac3228",
+   "metadata": {},
+   "source": [
+    "## Example 1 - Xarray Subsetting - Precipitation estimates from IMERG, Daily Level 3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b5b794c8-a100-46f0-8020-e2341ff2b201",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Dataset\n",
+    "We will use the GPM IMERG Final Precipitation L3 Daily dataset for this tutorial.  The IMERG Precipitation Rate provides the rain and snow rates in millimeters per hour (mm/hr). It is estimated by the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) (IMERG) algorithm. The IMERG algorithm uses passive-microwave data from the GPM constellation of satellites and infrared data from geosynchronous satellites. IMERG “morphs” observations to earlier or later times using wind from weather-model analyses. The daily IMERG dataset is derived from the half-hourly GPM_3IMERGHH. The derived result represents the final estimate of the daily mean precipitation rate in mm/day.\n",
+    "\n",
+    "The IMERG data has 0.1 x 0.1 degree latitude-longitude resolution (approximately 11 by 11 km at the Equator). The grid covers the globe, although precipitation cannot always be estimated near the Poles. The dataset and algorithm are described in the [data user guide](https://gpm1.gesdisc.eosdis.nasa.gov/data/GPM_L3/doc/README.GPM.pdf) and the [Algorithm Theoretical Basis Document (ATBD)](https://arthurhou.pps.eosdis.nasa.gov/Documents/IMERG_V07_ATBD_final.pdf). \n",
+    "\n",
+    "Please cite the dataset as:\n",
+    "> Huffman, G.J., E.F. Stocker, D.T. Bolvin, E.J. Nelkin, Jackson Tan (2023), GPM IMERG Final Precipitation L3 1 day 0.1 degree x 0.1 degree V07, Edited by Andrey Savtchenko, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC), https://doi.org/10.5067/GPM/IMERGDF/DAY/07"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "3dbe9828-37e9-4949-846f-297057e5b0d5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Granules found: 3\n",
+      " Opening 3 granules, approx size: 0.08 GB\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fd179bfbc37043628d1382e7274a4e3b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | : 0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0364418fcc1345d5b23f8f2eee4e716e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4c7161e8136d4a5ca98e622563769647",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "collection_id = 'C2723754864-GES_DISC'  # GPM IMERG Final Precipitation L3 1 day 0.1 degree x 0.1 degree V07 (GPM_3IMERGDF)\n",
+    "\n",
+    "# Bounds within which we search for data granules\n",
+    "# min lon, min lat, max lon, max lat\n",
+    "\n",
+    "# For reference (e.g., to visualize in https://geojson.io/), here is a GeoJSON representing the above bounding box:\n",
+    "# {\"type\": \"FeatureCollection\", \"features\": [{\"type\": \"Feature\", \"properties\": {}, \"geometry\": {\"type\": \"LineString\", \"bbox\": [-127.0761, 31.6444, -113.9039, 42.631], \"coordinates\": [[-113.9039, 42.631], [-127.0761,42.631], [-127.0761, 31.6444], [-113.9039, 31.6444], [-113.9039, 42.631]]}}]}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7de0326f-8c8e-4ae1-b8b2-ae0a73f594cb",
+   "metadata": {},
+   "source": [
+    "Note that `xarray` works with \"lazy\" computation whenever possible. In this case, the metadata are loaded into JupyterHub memory, but the data arrays and their values are not — until there is a need for them."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f72eb32d-8421-4f54-a2bd-7b8bc3dc531a",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "Let's print out all the variable names."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0f8beea9-bc30-4d02-9401-5b8605ad6847",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "383e376f-149f-4d5f-aa2e-f292e951170f",
+   "metadata": {},
+   "source": [
+    "Of the variables listed above, we are interested in three variables: `precipitation`, `precipitation_cnt_cond`, and `probabilityLiquidPrecipitation`. Let's print their attributes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f0241c11-ac86-418a-92e4-bb289468cc16",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "91434dd4-8aad-4b95-9ee4-b5dce04c60a0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "635f9e55-aa5e-4c4a-a571-2f29917e360e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f480119f-777f-42b6-ae2c-d9a5de1cb8a0",
+   "metadata": {},
+   "source": [
+    "### Subsetting\n",
+    "\n",
+    "In addition to directly accessing the files archived and distributed by each of the NASA DAACs, many datasets also support services that allow us to customize the data via subsetting, reformatting, reprojection/regridding, and file aggregation. What does subsetting mean? To **subset** means to extract only the portions of a dataset that are needed for a given purpose. Here's a generalized graphic of what we mean. \n",
+    "\n",
+    "![](https://github.com/NASA-Openscapes/earthdata-cloud-cookbook/blob/main/examples/images/subsetting_diagram.png?raw=true){fig-alt=\"Three maps of the United States are present, with a red bounding box over the state of Colorado. Filtering and subsetting are demonstrated by overlaying SMAP L2 data, with data overlapping and cropping the rectangle, respectively.\"  width=60%}\n",
+    "\n",
+    "There are three primary types of subsetting that we will walk through: \n",
+    "1. Temporal\n",
+    "2. Spatial\n",
+    "3. Variable\n",
+    "\n",
+    "In each case, we will be excluding parts of the dataset that are not wanted using `xarray`. Note that \"subsetting\" is also called a data \"transformation\"."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a228b00d-9659-4766-b25b-d9ba82506006",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "23fc1e31-656b-4a02-a80f-b6ea11712068",
+   "metadata": {},
+   "source": [
+    "We start with a subset that represents the U.S. state of California.  Notice the dimensions of the Dataset and each variable — time, lon, lat, and 'nv' (number of vertices) for the bounds variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "29355579-79f2-4ebf-b7d7-5a5c60a59d2e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Display the full dataset's metadata\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8641b5ae-043e-4233-bdfe-dada8370dfd5",
+   "metadata": {},
+   "source": [
+    "Now we will prepare a subset. We're using essentially the same spatial bounds as above; however, as opposed to the `earthaccess` inputs above, here we must provide inputs in the formats expected by `xarray`. Instead of a single, four-element, bounding box, we use Python `slice` objects, which are defined by starting and ending numbers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e3b5b217-246c-4549-8e7d-773dec50bf40",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "af9214c2-ef33-4d27-acca-511380b565db",
+   "metadata": {},
+   "source": [
+    "Notice the differences?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "210377ad-4dd0-401d-aad8-0d1760941daf",
+   "metadata": {},
+   "source": [
+    "### Plotting\n",
+    "\n",
+    "We will first plot using the methods built-in to the `xarray` package.\n",
+    "\n",
+    "Note that, as opposed to the \"lazy\" loading of metadata previously, this will now perform \"eager\" computation, pulling the required data chunks."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d97b5757-eb9e-4816-b2c8-5516823080ae",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "566d6593-7d61-4a8a-8873-e313b2474b5a",
+   "metadata": {},
+   "source": [
+    "Now let's utilize the \"Probability of liquid precipitation phase\" (`probabilityLiquidPrecipitation`) variable to split apart the snow precipitation from everything else. From the variable's description attribute, we can see that \"0=missing values; 1=likely solid; 100=likely liquid or no precipitation\".\n",
+    "\n",
+    "Moreover, we'll utilize `precipitation_cnt_cond` to filter out data points that had less than 0.01 mm/hr preciptation amounts."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5c8222e6-df93-496b-ac47-0dcff427fb33",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d0834c9e-653e-4d3d-9114-bf927d6c984b",
+   "metadata": {},
+   "source": [
+    "In the following plotting commands, we utilize `cartopy` and `matplotlib` to generate a more customized figure. \n",
+    "\n",
+    "`cartopy` is used to set the map projection (to PlateCarree) and to add U.S. state boundary lines to the figure. `matplotlib`'s pcolormesh is used to generate the color plot, with colors determined by the third argument's value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "62e9dcc1-c311-4bef-8a65-08beb54f0453",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# create the plot\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5ff73775-de33-4033-af1d-1c999e5f5faa",
+   "metadata": {},
+   "source": [
+    "Notice the enhancements?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "49fc0a27-2fbe-4cca-a3d1-095fa6a2b60c",
+   "metadata": {},
+   "source": [
+    "## Example 2 - Harmony-py Subsetting - Precipitable Water from DSCOVR-EPIC Composite"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a1d10aeb-a770-4988-bd1e-0d8b02f818c5",
+   "metadata": {},
+   "source": [
+    "### Dataset\n",
+    "The NASA Earth Polychromatic Imaging Camera (EPIC)-view Multi-Sensor Global Cloud and Radiance Composites are generated by optimally merging together multiple imagers on low Earth orbit (LEO) satellites (including MODIS, VIIRS, and AVHRR) and geostationary (GEO) satellites (including GOES-13 and -15, METEOSAT-7 and -10, MTSAT-2, and Himawari-8). These provide a seamless global composite product at 5-km resolution by using an aggregated rating that considers five parameters (nominal satellite resolution, pixel time relative to the Earth Polychromatic Imaging Camera (EPIC) observation time, viewing zenith angle, distance from day/night terminator, and sun glint factor) and selects the best observation at the time nearest to the EPIC measurements. The global composite data are then remapped into the EPIC Field of View (FOV) by convolving the high-resolution cloud properties with the EPIC point spread function (PSF) defined with a half-pixel accuracy to produce the EPIC composite. PSF-weighted radiances and cloud properties averages are computed separately for each cloud phase. Ancillary data (i.e., surface type, snow and ice map, skin temperature, precipitable water, etc.) needed for anisotropic factor selections are also included in the composite. These composite images are produced for each observation time of the EPIC instrument (typically 300 to 600 composites per month, or every 1 to 3 hours).\n",
+    "\n",
+    "\n",
+    "The dataset and development of the composite product is described in the [Khlopenkov et al., 2017](https://doi.org/10.1117/12.2278645) and the [Product Description page](http://doi.org/10.5067/EPIC/DSCOVR/L2_COMPOSITE_01). This dataset can also be viewed in [Earthdata Search](https://cmr.earthdata.nasa.gov/search/concepts/C1576365803-LARC_ASDC.html). \n",
+    "\n",
+    "Please cite the dataset as:\n",
+    "> NASA/LARC/SD/ASDC. (2017). EPIC-view satellite composites for DSCOVR, Version 1 [Data set]. NASA Langley Atmospheric Science Data Center DAAC. Retrieved from https://doi.org/10.5067/EPIC/DSCOVR/L2_COMPOSITE_01."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0df8ec4-6c65-4746-8c06-c6b8e42eaadb",
+   "metadata": {},
+   "source": [
+    "### Harmony\n",
+    "\n",
+    "[Harmony](https://www.earthdata.nasa.gov/learn/articles/harmony-in-the-cloud) is the behind-the-scenes orchestrator for much of the cloud-based transformations happening on NASA's [Earthdata Search](https://search.earthdata.nasa.gov/search) interface. However, requests can also be sent directly to Harmony in a programmatic fashion, either through use of the `harmony-py` Python library or through transmitting underlying HTTP requests.  In this example, we demonstrate the use of `harmony-py`, which was created as an alternative to Harmony's RESTful Application Programming Interface (API) and to make it more convenient to invoke Harmony directly from a Python environment, such as Jupyter notebooks or larger Python applications.\n",
+    "\n",
+    "Note that additional examples can be found on the `harmony-py` GitHub page [here](https://github.com/nasa/harmony-py/tree/main)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b88e0741-db91-4d6a-95bf-82554eeb8a96",
+   "metadata": {},
+   "source": [
+    "First we need to instantiate a `Client` object, with which we will be able to interact with Harmony."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "583e324b-88c9-4726-af33-ff07367dcf28",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "28bb3263-7a60-4e17-b2d0-2f9189badceb",
+   "metadata": {},
+   "source": [
+    "#### Inspecting a data collection for its capabilities and variables\n",
+    "\n",
+    "With harmony-py, you can request a report of the capabilities that are configured for a given collection. We use that function here to inspect the DSCOVR EPIC-view Composite collection."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "db162b89-de86-4bb0-b620-4257d9af6b92",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "844bdc84-4eeb-4cba-bb66-41ff8cc9ba51",
+   "metadata": {},
+   "source": [
+    "This data collection has one \"service\" associated with it, which provides several subsetting capabilities."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4b59af1b-7338-40f9-8965-80339436f6a1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "958edbe7-d4ab-4df7-aefa-ecf717ccb9d1",
+   "metadata": {},
+   "source": [
+    "We can also see the list of variables associated with this data collection."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3fbe4b39-df2c-4189-9eb9-aadeb1b492c0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ef12d0b6-0fac-4f9c-bbb0-0c199cf53e70",
+   "metadata": {},
+   "source": [
+    "The subsetter service capabilities told us what the service is capable of \"in general\". How about the capabilities reported for this data collection in particular?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "90865d24-6bbc-46ff-97a4-05f7d8fb4f3b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4184b164-2ba1-4180-a338-263302bc29a6",
+   "metadata": {},
+   "source": [
+    "Notice the `True`s and the `False`s?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "60f65119-5436-4701-ac48-bf64d6d163cc",
+   "metadata": {},
+   "source": [
+    "### Subsetting"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03b8f6a3-5278-4ca7-87e5-75f10deec65c",
+   "metadata": {},
+   "source": [
+    "#### Define an area of interest\n",
+    "\n",
+    "For this example, we will use a GeoJSON to specify a non-rectangular region instead of a simpler, rectangular bounding box.  We will use the GeoJSON that defines a region around San Francisco."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "33249e74-603d-4030-a2df-1b19df50fce1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Read the GeoJSON into a GeoDataFrame\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7e72238-dea5-400a-b09e-74a75ecf46ad",
+   "metadata": {},
+   "source": [
+    "Here we illustrate the use of GeoViews, which is another open source data visualization library, like `matplotlib`. GeoViews is designed to work well with netCDF data, as well as Geopandas dataframes.  The syntax for Geoviews is different in several ways — e.g., the dataset is often specified as the first argument and different components are combined using the `*` symbol."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "58c13872-3ca2-436e-ae53-3c22ce07e60c",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'gv' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[4], line 5\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# We define a Geoview Point so we can visualize the area of interest in relation to San Francisco\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \n\u001b[1;32m      3\u001b[0m \n\u001b[1;32m      4\u001b[0m \u001b[38;5;66;03m# Generate an image\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m base \u001b[38;5;241m=\u001b[39m \u001b[43mgv\u001b[49m\u001b[38;5;241m.\u001b[39mtile_sources\u001b[38;5;241m.\u001b[39mEsriImagery\u001b[38;5;241m.\u001b[39mopts(width\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m650\u001b[39m, height\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m500\u001b[39m)\n\u001b[1;32m      6\u001b[0m ocean_map \u001b[38;5;241m=\u001b[39m gv\u001b[38;5;241m.\u001b[39mPolygons(gdf)\u001b[38;5;241m.\u001b[39mopts(line_color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124myellow\u001b[39m\u001b[38;5;124m'\u001b[39m, line_width\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m, color\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m      8\u001b[0m base \u001b[38;5;241m*\u001b[39m ocean_map \u001b[38;5;241m*\u001b[39m cities_lonlat\u001b[38;5;241m.\u001b[39moptions(size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m20\u001b[39m, color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mred\u001b[39m\u001b[38;5;124m'\u001b[39m, marker\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mx\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'gv' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "# We define a Geoview Point so we can visualize the area of interest in relation to San Francisco\n",
+    "\n",
+    "\n",
+    "# Generate an image\n",
+    "base = gv.tile_sources.EsriImagery.opts(width=650, height=500)\n",
+    "ocean_map = gv.Polygons(gdf).opts(line_color='yellow', line_width=5, color=None)\n",
+    "\n",
+    "base * ocean_map * cities_lonlat.options(size=20, color='red', marker='x')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9e01ba7d-e499-4869-a835-6e2349303e21",
+   "metadata": {},
+   "source": [
+    "#### Build a Harmony subsetting request\n",
+    "\n",
+    "A Harmony request can include spatial, temporal, and variable subsetting all in the same request.  Here we will request all three types of subsetting to be performed on the EPIC-view Composite dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f02d190d-81f3-4fd2-b92e-5345769b85ca",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2ca89302-42f0-4017-ba0f-a6e348b8dc1d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "044b95c6-4312-4d99-acfc-6eea0e28056d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2abe3ac3-5172-4218-ba44-0b063abdbac1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "da982b31-5fad-4202-8b86-09d98e83859b",
+   "metadata": {},
+   "source": [
+    "While this processes, we can discuss the harmony job in some more detail. First, note that this request is identical to what can be achieved through NASA's Earthdata Search interface, such as this URL: [https://search.earthdata.nasa.gov/search/granules?p=C1576365803-LARC_ASDC!C1576365803-LARC_ASDC&pg[1][a]=1576368528!1576368575!LARC_ASDC&pg[1][v]=t&pg[1][gsk]=-start_date&pg[1][m]=harmony0&pg[1][of]=application/x-netcdf4&pg[1][ets]=t&pg[1][ess]=t&q=C1576365803-LARC_ASDC&sb[0]=-123.99609%2C37.19991%2C-120.44531%2C38.78263&qt=2016-02-24T12%3A00%3A00.000Z%2C2016-02-24T23%3A00%3A00.000Z&tl=1702228562!3!!&lat=37.8270894268111&long=-130.67578125&zoom=4](https://search.earthdata.nasa.gov/search/granules?p=C1576365803-LARC_ASDC!C1576365803-LARC_ASDC&pg[1][a]=1576368528!1576368575!LARC_ASDC&pg[1][v]=t&pg[1][gsk]=-start_date&pg[1][m]=harmony0&pg[1][of]=application/x-netcdf4&pg[1][ets]=t&pg[1][ess]=t&q=C1576365803-LARC_ASDC&sb[0]=-123.99609%2C37.19991%2C-120.44531%2C38.78263&qt=2016-02-24T12%3A00%3A00.000Z%2C2016-02-24T23%3A00%3A00.000Z&tl=1702228562!3!!&lat=37.8270894268111&long=-130.67578125&zoom=4)\n",
+    "\n",
+    "(Futher information and examples can be found in the `harmony-py` repository, such as [this introductory notebook](https://github.com/nasa/harmony-py/blob/main/examples/intro_tutorial.ipynb).)\n",
+    "\n",
+    "#### Request Parameters\n",
+    "\n",
+    "In addition to the above request parameters, other advanced parameters may be of interest. Note that many reformatting or advanced projection options may not be available for your requested dataset. See the documentation for details on how to construct these parameters.\n",
+    "\n",
+    "- `crs`: Reproject the output coverage to the given CRS. Recognizes CRS types that can be inferred by gdal, including EPSG codes, Proj4 strings, and OGC URLs (http://www.opengis.net/def/crs/%E2%80%A6)\n",
+    "- `interpolation`: specify the interpolation method used during reprojection and scaling\n",
+    "- `scale_extent`: scale the resulting coverage either among one axis to a given extent\n",
+    "- `scale_size`: scale the resulting coverage either among one axis to a given size\n",
+    "- `granule_id`: The CMR Granule ID for the granule (file) which should be retrieved\n",
+    "- `width`: number of columns to return in the output coverage\n",
+    "- `height`: number of rows to return in the output coverage\n",
+    "- `format`: the output mime type to return\n",
+    "- `max_results`: limits the number of input files processed in the request\n",
+    "\n",
+    "#### Harmony Client\n",
+    "\n",
+    "There are four options for providing your Earthdata Login token or username and password when creating a Harmony Client:\n",
+    "\n",
+    "1. Provide EDL token using environment variable, e.g.:\n",
+    "\n",
+    "> `$ export EDL_TOKEN='my_eld_token'`\n",
+    "\n",
+    "2. Provide your username and password directly when creating the client:\n",
+    "\n",
+    "> `harmony_client = Client(auth=('captainmarvel', 'marve10u5'))`\n",
+    "\n",
+    "3. Set your credentials using environment variables:\n",
+    "\n",
+    "You can either export these directly:\n",
+    "\n",
+    "> `$ export EDL_USERNAME='captainmarvel'`\n",
+    "\n",
+    "> `$ export EDL_PASSWORD='marve10u5'`\n",
+    "\n",
+    "Or by storing them in a .env file, which operates in a similar fashion to .netrc. You will need to store the file in your current working directory and it must be named .env with the following format:\n",
+    "\n",
+    "> `EDL_USERNAME=myusername`\n",
+    "\n",
+    "> `EDL_PASSWORD=mypass`\n",
+    "\n",
+    "4. Use a .netrc file:\n",
+    "\n",
+    "> ```\n",
+    "machine urs.earthdata.nasa.gov\n",
+    "login captainmarvel\n",
+    "password marve10u5\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dcdf99d8-74c1-49cf-b65d-38ea1ef0e5e4",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4d996a53-6cf4-40c3-a5c5-06d932956609",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "80f432ab-17aa-4fe8-b99a-831dd59725ed",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ef2b5dea-e0ba-4574-90b7-f087c94605b7",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c6fc0c90-8a6d-4248-ab94-4b8e6afdedc2",
+   "metadata": {},
+   "source": [
+    "### Plotting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "629911d9-8b17-44b0-8e3d-2e630a521495",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "1c01834c-1ce1-4993-8b43-9a8bc9a862b9",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create figure\n",
+    "proj = ccrs.PlateCarree()\n",
+    "fig, ax = plt.subplots(figsize=(6, 4), facecolor=\"w\", subplot_kw=dict(projection=proj))\n",
+    "\n",
+    "ax_handle = ax.contourf(new_ds_general['lon'], new_ds_general['lat'], new_ds_general['precipitable_water'], cmap=\"turbo\")\n",
+    "plt.colorbar(\n",
+    "    ax_handle, ax=ax, \n",
+    "    label=f\"{precip_attrs['long_name']} ({precip_attrs['units']})\"\n",
+    ")\n",
+    "\n",
+    "ax.add_feature(cfeature.STATES)\n",
+    "ax.set_extent([-125, -113.0, 31.0, 43.0], crs=proj)\n",
+    "\n",
+    "ax.set_xticks([-125, -120, -115, -110], crs=proj)\n",
+    "ax.set_yticks([32, 34, 36, 38, 40, 42], crs=proj)\n",
+    "lon_formatter = LongitudeFormatter(number_format='.1f',\n",
+    "                                   degree_symbol='',\n",
+    "                                   dateline_direction_label=True)\n",
+    "lat_formatter = LatitudeFormatter(number_format='.1f',\n",
+    "                                  degree_symbol='')\n",
+    "ax.xaxis.set_major_formatter(lon_formatter)\n",
+    "ax.yaxis.set_major_formatter(lat_formatter)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "777d0a2b-9746-42b0-9c65-56076663da1c",
+   "metadata": {},
+   "source": [
+    "A final note about `harmony-py`: As more transformation services are added and configured to work with existing and new NASA data collections, the capabilities you will be able to harness with `harmony-py` will also grow!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9863e4fc-85f2-44a1-87e0-4fb2691b5f3a",
+   "metadata": {},
+   "source": [
+    "## Appendix 1 - Snow Cover from MODIS/Terra, Daily Level3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5600f542-98fc-422c-9c1b-186863051fc8",
+   "metadata": {},
+   "source": [
+    "### Dataset\n",
+    "We will use MODIS/Terra Snow Cover Daily L3 Global 0.05Deg CMG. The Moderate Resolution Imaging Spectroradiometer (MODIS) global Level-3 (L3) data set provides the percentage of snow-covered land and cloud-covered land observed daily, within 0.05° (approx. 5 km) MODIS Climate Modeling Grid (CMG) cells. \n",
+    "\n",
+    "The dataset and algorithm is described in the [data user guide](https://nsidc.org/sites/default/files/mod10c1-v061-userguide_0.pdf) and the [Product Specific Document](https://nsidc.org/sites/default/files/c61_modis_snow_user_guide.pdf). \n",
+    "\n",
+    "Please cite the dataset as:\n",
+    "> Hall, D. K. and G. A. Riggs. (2021). MODIS/Terra Snow Cover Daily L3 Global 0.05Deg CMG, Version 61. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/MODIS/MOD10C1.061."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9866b6a-5eb9-41a4-8e84-95c93f9eb11f",
+   "metadata": {},
+   "source": [
+    "Using the dataset DOI, we will use the earthaccess module to search for dataset granules from February 24, 2023, and March 2, 2023."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "619d7bd6-d47c-463c-a3c7-95821b525bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Granules found: 1\n",
+      "Granules found: 1\n"
+     ]
+    }
+   ],
+   "source": [
+    "doi = '10.5067/MODIS/MOD10C1.061' # MODIS Terra Snowcover\n",
+    "\n",
+    "# search granules from Feb 15, 2023\n",
+    "date1 = \"2023-02-15\"\n",
+    "granules1 = earthaccess.search_data(\n",
+    "    count=-1, # needed to retrieve all granules\n",
+    "    doi=doi,\n",
+    "    temporal=(date1, date1)\n",
+    ")\n",
+    "# search granules from March 02, 2023\n",
+    "date2 = \"2023-03-02\"\n",
+    "granules2 = earthaccess.search_data(\n",
+    "    count=-1, # needed to retrieve all granules\n",
+    "    doi=doi,\n",
+    "    temporal=(date2, date2)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "267e090d-448b-4436-a4b3-830b9218ece0",
+   "metadata": {},
+   "source": [
+    "Let's download the granules to the local environment. This is needed as direct access to HDF4 files that MODIS Collection 6.1 comes as is currently not supported. The `earthaccess` module manages the authentication that is required for accessing data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "3a5b41eb-e646-4d42-96a1-073e2646fcde",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Getting 1 granules, approx download size: 0.0 GB\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dcf968aac8124e899cc4ac23c8d55aef",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "27acc53484d04ad19b813d874058bc61",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6f3dd0cbd4194e9499e0cd41e54794c7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Getting 1 granules, approx download size: 0.0 GB\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d48e66bca1a94a6d8d94f579cadb1f5f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b1becfcffebb4850b33a24e68a003310",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7058efd0330e4752b2279cd3907a835c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "['MOD10C1.A2023061.061.2023070101022.hdf']"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "earthaccess.download(granules1, local_path='.')\n",
+    "earthaccess.download(granules2, local_path='.')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db1be944-32dc-4249-a1fd-b445897e57ce",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Subsetting"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1c8e3ee9-174f-4de6-a4a4-eec2c67a2939",
+   "metadata": {},
+   "source": [
+    "Let's open the downloaded granules into a `rioxarray`. The variable `Day_CMG_Snow_Cover` provides daily percent snow in 5km grids. The variable `Snow_Spatial_QA` provides quality indicator for each grid: 0=best, 1=good, 2=ok, 3=poor, 4=other, 237=inland water, 239=ocean, 250=cloud obscured water 252=Antarctica mask, 253=not mapped, 254=no retrieval, and 255=fill. We will only use the grids with 0, 1, and 2 quality flags."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "f9ae3517-5564-469b-bd28-e08ab20833a2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<xarray.Dataset>\n",
+      "Dimensions:                 (band: 1, x: 7200, y: 3600)\n",
+      "Coordinates:\n",
+      "  * band                    (band) int64 1\n",
+      "  * x                       (x) float64 -180.0 -179.9 -179.9 ... 179.9 180.0\n",
+      "  * y                       (y) float64 89.97 89.92 89.88 ... -89.93 -89.98\n",
+      "    spatial_ref             int64 0\n",
+      "Data variables:\n",
+      "    Day_CMG_Snow_Cover      (band, y, x) uint8 ...\n",
+      "    Day_CMG_Clear_Index     (band, y, x) uint8 ...\n",
+      "    Day_CMG_Cloud_Obscured  (band, y, x) uint8 ...\n",
+      "    Snow_Spatial_QA         (band, y, x) uint8 ...\n",
+      "Attributes: (50)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# open granule from Feb 15, 2023\n",
+    "g_1 = Path(Path(granules1[0].data_links()[0]).name)\n",
+    "if g_1.is_file():\n",
+    "    with rxr.open_rasterio(g_1) as modis:\n",
+    "        print(modis)\n",
+    "        snow_cover1 = modis['Day_CMG_Snow_Cover'][:]\n",
+    "        snow_cover_qa1 = modis['Snow_Spatial_QA'][:]\n",
+    "\n",
+    "# open granules from March 02, 2023\n",
+    "g_2 = Path(Path(granules2[0].data_links()[0]).name)\n",
+    "if g_2.is_file():\n",
+    "    with rxr.open_rasterio(g_2) as modis:\n",
+    "        snow_cover2 = modis['Day_CMG_Snow_Cover'][:]\n",
+    "        snow_cover_qa2 = modis['Snow_Spatial_QA'][:]\n",
+    "\n",
+    "# Spatially subset and keep only good quality cells\n",
+    "snow_cover_good1 = (\n",
+    "    snow_cover1\n",
+    "    .sel(x=slice(-125, -113), y=slice(43, 31))\n",
+    "    .where((snow_cover_qa1 >= 0) & (snow_cover_qa1 <= 2))\n",
+    ")\n",
+    "snow_cover_good2 = (\n",
+    "    snow_cover2\n",
+    "    .sel(x=slice(-125, -113), y=slice(43, 31))\n",
+    "    .where((snow_cover_qa2 >= 0) & (snow_cover_qa2 <= 2))\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "67d881ad-089f-4862-8b7b-03fbdd23f426",
+   "metadata": {},
+   "source": [
+    "### Plotting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "3b458b84-e042-4a9b-94de-96c21d16ffca",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1560x520 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# create the plot\n",
+    "proj = ccrs.PlateCarree()\n",
+    "fig, (ax1, ax2) = plt.subplots(1,2, figsize=(12,4), dpi=130, facecolor=\"w\", subplot_kw=dict(projection=proj))\n",
+    "\n",
+    "snowax1 = ax1.pcolormesh(snow_cover_good1.x.values, snow_cover_good1.y.values, snow_cover_good1.values[0], vmax=100, cmap='Blues')\n",
+    "plt.colorbar(snowax1, ax=ax1, label=\"snow cover (%)\")\n",
+    "ax1.add_feature(cfeature.STATES)\n",
+    "ax1.set_title(f'Snow Cover {date1}')\n",
+    "\n",
+    "snowax2 = ax2.pcolormesh(snow_cover_good2.x.values, snow_cover_good2.y.values, snow_cover_good2.values[0], vmax=100, cmap='Blues')\n",
+    "plt.colorbar(snowax2, ax=ax2, label=\"snow cover (%)\")\n",
+    "ax2.add_feature(cfeature.STATES)\n",
+    "ax2.set_title(f'Snow Cover {date2}')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4b0f811e-19fd-4269-b9e9-4b97baef1f70",
+   "metadata": {},
+   "source": [
+    "## Appendix 2 - Snow Mass from SMAP, 3-hourly Level 4"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7348bfa6-19e2-463e-a60d-6f1287e7b831",
+   "metadata": {},
+   "source": [
+    "### Dataset\n",
+    "The Soil Moisture Active Passive (SMAP) L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data (SPL4SMGP) provides a model-derived global 3-hr time average of snow mass in kg/m2.  SMAP L-band brightness temperature data from descending and ascending half-orbit satellite passes (approximately 6:00 a.m. and 6:00 p.m. local solar time, respectively) are assimilated into a land surface model that is gridded using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection. Snow mass estimates are based on a snow model component of the NASA Catchment Land Surface Model.\n",
+    "\n",
+    "The dataset and algorithm are described in the [data user guide](https://nsidc.org/sites/default/files/documents/user-guide/multi_spl4smau-v007-userguide.pdf) and the [Product Specific Document](https://nsidc.org/sites/default/files/documents/technical-reference/reichle1438.pdf). \n",
+    "\n",
+    "Please cite the dataset as:\n",
+    "> Reichle, R., G. De Lannoy, R. D. Koster, W. T. Crow, J. S. Kimball, Q. Liu, and M. Bechtold. (2022). SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data, Version 7. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/EVKPQZ4AFC4D."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "1ad33c72-b484-4687-bc6c-0822804422de",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Granules found: 2\n",
+      "Granules found: 2\n"
+     ]
+    }
+   ],
+   "source": [
+    "# SMAP SPL4SMGP\n",
+    "doi = '10.5067/EVKPQZ4AFC4D'\n",
+    "\n",
+    "# search granules from Feb 15, 2023\n",
+    "date1 = \"2023-02-15\"\n",
+    "granules1 = earthaccess.search_data(\n",
+    "    count=-1, # needed to retrieve all granules\n",
+    "    doi=doi,\n",
+    "    temporal=(date1, date1)\n",
+    ")\n",
+    "\n",
+    "# search granules from March 02, 2023\n",
+    "date2 = \"2023-03-02\"\n",
+    "granules2 = earthaccess.search_data(\n",
+    "    count=-1, # needed to retrieve all granules\n",
+    "    doi=doi,\n",
+    "    temporal=(date2, date2)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "ed96e00d-d1cf-40cd-bd9e-3e620929a05f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Opening 2 granules, approx size: 0.27 GB\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7f57b104d43740cd8b8e3dd15aabfb96",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | : 0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d36439f31d5d4ebca115fe884f3d11fb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/2 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "85f811a7a3694843a3d980afab5bc831",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/2 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# granules from Feb 15, 2023\n",
+    "fh1 = earthaccess.open(granules1)\n",
+    "# open geophysical_data group\n",
+    "ds1 = xr.open_dataset(fh1[0], phony_dims='access', group='Geophysical_Data')\n",
+    "# get location\n",
+    "ds_loc1 = xr.open_dataset(fh1[0], phony_dims='access')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "b3137cff-879c-41dd-bb68-fdf107f00d0a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Opening 2 granules, approx size: 0.27 GB\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7863fbfae2d340bc8ec1b490e89184d9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | : 0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8dedbeb1029e45a6a6d4231b0918222d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/2 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d23ca87097b14e57872df2e8913bddcc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/2 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# granules from March 02, 2023\n",
+    "fh2 = earthaccess.open(granules2)\n",
+    "# open geophysical_data group\n",
+    "ds2 = xr.open_dataset(fh2[0], phony_dims='access', group='Geophysical_Data')\n",
+    "# get location\n",
+    "ds_loc2 = xr.open_dataset(fh2[0], phony_dims='access')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "557bace4-0e08-49ce-8b9a-a72b2fecbada",
+   "metadata": {},
+   "source": [
+    "The \"snow_mass\" variable is within the \"Geophysical_Data\" group. It provides the average snow mass (or snow water equivalent) over a land fraction of the grid cell, excluding areas of open water and permanent ice. Let's print the attributes of the `snow_mass` variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "0adc16f5-9aad-4b51-9d92-c547fbda64b5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'DIMENSION_LABELS': [b'y', b'x'],\n",
+       " 'coordinates': '/cell_lat /cell_lon',\n",
+       " 'fmissing_value': -9999.0,\n",
+       " 'grid_mapping': 'EASE2_global_projection',\n",
+       " 'long_name': 'Average snow mass (or snow water equivalent) over land fraction of grid cell (excluding areas of open water and permanent ice)',\n",
+       " 'units': 'kg m-2',\n",
+       " 'valid_max': 10000.0,\n",
+       " 'valid_min': 0.0}"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds1.variables['snow_mass'].attrs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "f226c00a-ba07-4481-8f24-cac285a26ac1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1560x520 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# create the plot\n",
+    "proj = ccrs.Projection(\"EPSG:6933\") # EASEGRID 2\n",
+    "fig, (ax1, ax2) = plt.subplots(1,2, figsize=(12,4), dpi=130, facecolor=\"w\", subplot_kw=dict(projection=proj))\n",
+    "\n",
+    "ca_bounds = [-12060785, -10902950, 3769089, 4995383]\n",
+    "\n",
+    "snow_mass1 = ds1.snow_mass.where(ds1.snow_mass>9.4)\n",
+    "snowax1 = ax1.pcolormesh(ds_loc1.x, ds_loc1.y, snow_mass1, vmax=200, cmap='Blues')\n",
+    "plt.colorbar(snowax1, ax=ax1, label=\"snow mass (kg/m2)\")\n",
+    "ax1.add_feature(cfeature.STATES)\n",
+    "ax1.set_extent(ca_bounds, crs=proj)\n",
+    "ax1.set_title(f'Snow Mass {date1}')\n",
+    "\n",
+    "snow_mass2 = ds2.snow_mass.where(ds2.snow_mass>9.4)\n",
+    "snowax2 = ax2.pcolormesh(ds_loc2.x, ds_loc2.y, snow_mass2, vmax=200, cmap='Blues')\n",
+    "plt.colorbar(snowax2, ax=ax2, label=\"snow mass (kg/m2)\")\n",
+    "ax2.add_feature(cfeature.STATES)\n",
+    "ax2.set_extent(ca_bounds, crs=proj)\n",
+    "ax2.set_title(f'Snow Mass {date2}')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7ebf0ce-29a4-4b07-ab25-5f842de15d3c",
+   "metadata": {},
+   "source": [
+    "### Now we will remove the saved files from our workspace, to keep it clean for future coding!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "2a92fdcc-8868-4289-812d-3df354aba8ac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from glob import glob\n",
+    "\n",
+    "for f in glob(\"DSCOVR_EPIC_L2*.nc4\"):\n",
+    "    Path(f).unlink()\n",
+    "    \n",
+    "for f in glob(\"MOD10C1.*.hdf\"):\n",
+    "    Path(f).unlink()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4e6b9c23-5ec8-43e0-83fe-bcc7189356b6",
+   "metadata": {},
+   "source": [
+    "## Additional Resources\n",
+    "\n",
+    "### Tutorials\n",
+    "\n",
+    "This clinic was based off of several notebook tutorials including those presented during [past workshop events](https://nasa-openscapes.github.io/earthdata-cloud-cookbook/tutorials/), along with other materials co-created by the NASA Openscapes mentors:\n",
+    "* [2021 Earthdata Cloud Hackathon](https://nasa-openscapes.github.io/2021-Cloud-Hackathon/)\n",
+    "* [2021 AGU Workshop](https://nasa-openscapes.github.io/2021-Cloud-Workshop-AGU/)\n",
+    "* [Accessing and working with ICESat-2 data in the cloud](https://github.com/nsidc/NSIDC-Data-Tutorials/tree/main/notebooks/ICESat-2_Cloud_Access)\n",
+    "* [Analyzing Sea Level Rise Using Earth Data in the Cloud](https://github.com/betolink/earthaccess-gallery/blob/main/notebooks/Sea_Level_Rise/SSL.ipynb)\n",
+    "\n",
+    "### Cloud services\n",
+    "\n",
+    "The examples used in the clinic provide an abbreviated and simplified workflow to explore access and subsetting options available through the Earthdata Cloud. There are several other options that can be used to interact with data in the Earthdata Cloud including: \n",
+    "\n",
+    "* [OPeNDAP](https://opendap.earthdata.nasa.gov/) \n",
+    "    * Hyrax provides direct access to subsetting of NASA data using Python or your favorite analysis tool\n",
+    "    * Tutorial highlighting OPeNDAP usage: https://nasa-openscapes.github.io/earthdata-cloud-cookbook/how-tos/working-locally/Earthdata_Cloud__Data_Access_OPeNDAP_Example.html\n",
+    "* [Zarr-EOSDIS-Store](https://github.com/nasa/zarr-eosdis-store)\n",
+    "    * The zarr-eosdis-store library allows NASA EOSDIS Collections to be accessed efficiently by the Zarr Python library, provided they have a sidecar DMR++ metadata file generated. \n",
+    "    * Tutorial highlighting this library's usage: https://nasa-openscapes.github.io/2021-Cloud-Hackathon/tutorials/09_Zarr_Access.html \n",
+    "\n",
+    "### Support\n",
+    "\n",
+    "* [Earthdata Forum](https://forum.earthdata.nasa.gov/)\n",
+    "    * User Services and community support for all things NASA Earthdata, including Earthdata Cloud\n",
+    "* [Earthdata Webinar series](https://www.earthdata.nasa.gov/learn/webinars-and-tutorials)\n",
+    "    * Webinars from DAACs and other groups across EOSDIS including guidance on working with Earthdata Cloud\n",
+    "    * See the [Earthdata YouTube channel](https://www.youtube.com/@NASAEarthdata/featured) for more videos "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a44f1dfd-9bc2-4086-affe-260b3232642b",
+   "metadata": {},
+   "source": [
+    "END of Notebook."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}