diff --git a/binder/CMR_queries.ipynb b/binder/CMR_queries.ipynb
index dbdb6b2..2ce55ba 100644
--- a/binder/CMR_queries.ipynb
+++ b/binder/CMR_queries.ipynb
@@ -5,110 +5,66 @@
    "id": "d0e31245-c554-4985-af28-705abe764ece",
    "metadata": {},
    "source": [
-    "<span style='color:#0066cc'> <span style='font-family:serif'> <font size=\"13\"> **Using CMR to Find OPeNDAP URLs (Earthdata specific)**<span style='color:#0066cc'>\n",
+    "# Discovering OPeNDAP URLs from NASA's Earthdata\n",
     "\n",
-    "<font size=\"3\"><span style='color:Black'>  The Common Metadata Repository (CMR) is a high-performance, high-quality, continuously evolving metadata system that catalogs Earth Science data and associated service metadata records. These metadata records are registered, modified, discovered, and accessed through programmatic interfaces leveraging standard protocols and APIs.\n",
+    "This tutorial demonstrates how to find OPeNDAP URLs from the [Common Metadata Repository](https://cmr.earthdata.nasa.gov/search) (CMR). The CMR is NASA's Earthdata API to query datasets available through many download and subset services, including OPeNDAP. The [CMR API](https://cmr.earthdata.nasa.gov/search/site/docs/search/api.html) is complex and broad in scope, and with `pydap.client.get_cmr_urls` users can query and retrieve OPeNDAP urls.\n",
     "\n",
-    "<font size=\"3\"><span style='color:Black'> For more information about the CMR API go to: https://cmr.earthdata.nasa.gov/search/site/docs/search/api.html\n"
+    "**Requirements to run this notebook**\n",
+    "1. Have an Earth Data Login account\n",
+    "2. Knowledge of the Collection Concept ID (CCID), or Digital Object Identifier (DOI) of the collection of interest. \n",
+    "\n",
+    "**Objectives**\n",
+    " \n",
+    "Use [PyDAP](https://pydap.github.io/pydap/) to **discover all opendap urls in two simple case studies**\n",
+    "\n",
+    "1. Discover all possible OPeNDAP urls associated with a specific Collection Concept ID (and DOI).\n",
+    "2. Discover all possible OPeNDAP urls from a collection, that **match a time range and spatial bounding box of interest**. These parameters, and others, are widely used by the CMR (and Earthdata search) to filter the number of possible returns from querying the CMR, therefore narrowing the search.\n",
+    "\n",
+    "\n",
+    "`Author`: Miguel Jimenez-Urias, '25"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "adc54031-dc9f-4858-83be-a84c6ee4eef0",
    "metadata": {},
    "outputs": [],
    "source": [
     "from pydap.net import create_session\n",
-    "from pydap.client import get_cmr_urls"
+    "from pydap.client import get_cmr_urls\n",
+    "import pydap\n",
+    "import datetime as dt"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "f764fa2b-acc4-43ba-8762-b606dc4a96e4",
    "metadata": {},
    "outputs": [],
    "source": [
-    "ecostress_ccid = \"C2076114664-LPCLOUD\""
+    "ecostress_ccid = \"C2076114664-LPCLOUD\"\n",
+    "time_range = [dt.datetime(2025, 3, 1), dt.datetime(2025, 3, 31)]\n",
+    "return_limit = 500 # How many urls to return. Default is 50"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "c4306a74-3848-42ec-9e57-e396f9f47b80",
    "metadata": {},
    "outputs": [],
    "source": [
-    "urls = get_cmr_urls(ccid=ecostress_ccid, bounding_box=list((-130.8, 41, -124, 45)))"
+    "urls = get_cmr_urls(ccid=ecostress_ccid, bounding_box=list((-130.8, 41, -124, 45)), time_range=time_range, limit=return_limit)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "68aa026d-51d5-4bab-a642-2a5c04258712",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['https://opendap.earthdata.nasa.gov/collections/C2076114664-LPCLOUD/granules/ECOv002_L2_LSTE_00152_003_20180716T130457_0712_04',\n",
-       " 'https://opendap.earthdata.nasa.gov/collections/C2076114664-LPCLOUD/granules/ECOv002_L2_LSTE_00258_001_20180723T101233_0712_04',\n",
-       " 'https://opendap.earthdata.nasa.gov/collections/C2076114664-LPCLOUD/granules/ECOv002_L2_LSTE_00289_001_20180725T100502_0712_04',\n",
-       " 'https://opendap.earthdata.nasa.gov/collections/C2076114664-LPCLOUD/granules/ECOv002_L2_LSTE_00346_001_20180729T014458_0712_04',\n",
-       " 'https://opendap.earthdata.nasa.gov/collections/C2076114664-LPCLOUD/granules/ECOv002_L2_LSTE_00346_002_20180729T014550_0712_04',\n",
-       " 'https://opendap.earthdata.nasa.gov/collections/C2076114664-LPCLOUD/granules/ECOv002_L2_LSTE_00392_002_20180801T004543_0712_04',\n",
-       " 'https://opendap.earthdata.nasa.gov/collections/C2076114664-LPCLOUD/granules/ECOv002_L2_LSTE_00392_003_20180801T004635_0712_04',\n",
-       " 'https://opendap.earthdata.nasa.gov/collections/C2076114664-LPCLOUD/granules/ECOv002_L2_LSTE_00408_005_20180802T013113_0712_04',\n",
-       " 'https://opendap.earthdata.nasa.gov/collections/C2076114664-LPCLOUD/granules/ECOv002_L2_LSTE_00423_001_20180803T003844_0712_04',\n",
-       " 'https://opendap.earthdata.nasa.gov/collections/C2076114664-LPCLOUD/granules/ECOv002_L2_LSTE_00423_002_20180803T003936_0712_04',\n",
-       " 'https://opendap.earthdata.nasa.gov/collections/C2076114664-LPCLOUD/granules/ECOv002_L2_LSTE_00438_002_20180803T234626_0712_04',\n",
-       " 'https://opendap.earthdata.nasa.gov/collections/C2076114664-LPCLOUD/granules/ECOv002_L2_LSTE_00438_003_20180803T234718_0712_04',\n",
-       " 'https://opendap.earthdata.nasa.gov/collections/C2076114664-LPCLOUD/granules/ECOv002_L2_LSTE_00441_002_20180804T043829_0712_04',\n",
-       " 'https://opendap.earthdata.nasa.gov/collections/C2076114664-LPCLOUD/granules/ECOv002_L2_LSTE_00453_003_20180804T225449_0712_04',\n",
-       " 'https://opendap.earthdata.nasa.gov/collections/C2076114664-LPCLOUD/granules/ECOv002_L2_LSTE_00457_005_20180805T052214_0712_04',\n",
-       " 'https://opendap.earthdata.nasa.gov/collections/C2076114664-LPCLOUD/granules/ECOv002_L2_LSTE_00457_005_20180805T052214_0712_05',\n",
-       " 'https://opendap.earthdata.nasa.gov/collections/C2076114664-LPCLOUD/granules/ECOv002_L2_LSTE_00457_006_20180805T052306_0712_04',\n",
-       " 'https://opendap.earthdata.nasa.gov/collections/C2076114664-LPCLOUD/granules/ECOv002_L2_LSTE_00457_007_20180805T052358_0712_04',\n",
-       " 'https://opendap.earthdata.nasa.gov/collections/C2076114664-LPCLOUD/granules/ECOv002_L2_LSTE_00469_004_20180805T233925_0712_04',\n",
-       " 'https://opendap.earthdata.nasa.gov/collections/C2076114664-LPCLOUD/granules/ECOv002_L2_LSTE_00469_004_20180805T233925_0712_05',\n",
-       " 'https://opendap.earthdata.nasa.gov/collections/C2076114664-LPCLOUD/granules/ECOv002_L2_LSTE_00469_005_20180805T234017_0712_04',\n",
-       " 'https://opendap.earthdata.nasa.gov/collections/C2076114664-LPCLOUD/granules/ECOv002_L2_LSTE_00472_001_20180806T043039_0712_04',\n",
-       " 'https://opendap.earthdata.nasa.gov/collections/C2076114664-LPCLOUD/granules/ECOv002_L2_LSTE_00472_002_20180806T043131_0712_04',\n",
-       " 'https://opendap.earthdata.nasa.gov/collections/C2076114664-LPCLOUD/granules/ECOv002_L2_LSTE_00484_004_20180806T224658_0712_04',\n",
-       " 'https://opendap.earthdata.nasa.gov/collections/C2076114664-LPCLOUD/granules/ECOv002_L2_LSTE_00484_004_20180806T224658_0712_05',\n",
-       " 'https://opendap.earthdata.nasa.gov/collections/C2076114664-LPCLOUD/granules/ECOv002_L2_LSTE_00484_005_20180806T224750_0712_04',\n",
-       " 'https://opendap.earthdata.nasa.gov/collections/C2076114664-LPCLOUD/granules/ECOv002_L2_LSTE_00484_006_20180806T224842_0712_04',\n",
-       " 'https://opendap.earthdata.nasa.gov/collections/C2076114664-LPCLOUD/granules/ECOv002_L2_LSTE_00487_002_20180807T033902_0712_04',\n",
-       " 'https://opendap.earthdata.nasa.gov/collections/C2076114664-LPCLOUD/granules/ECOv002_L2_LSTE_00499_003_20180807T215521_0712_04',\n",
-       " 'https://opendap.earthdata.nasa.gov/collections/C2076114664-LPCLOUD/granules/ECOv002_L2_LSTE_00499_004_20180807T215613_0712_04',\n",
-       " 'https://opendap.earthdata.nasa.gov/collections/C2076114664-LPCLOUD/granules/ECOv002_L2_LSTE_00503_007_20180808T042338_0712_04',\n",
-       " 'https://opendap.earthdata.nasa.gov/collections/C2076114664-LPCLOUD/granules/ECOv002_L2_LSTE_00503_008_20180808T042430_0712_04',\n",
-       " 'https://opendap.earthdata.nasa.gov/collections/C2076114664-LPCLOUD/granules/ECOv002_L2_LSTE_00518_007_20180809T033122_0712_04',\n",
-       " 'https://opendap.earthdata.nasa.gov/collections/C2076114664-LPCLOUD/granules/ECOv002_L2_LSTE_00518_008_20180809T033214_0712_04',\n",
-       " 'https://opendap.earthdata.nasa.gov/collections/C2076114664-LPCLOUD/granules/ECOv002_L2_LSTE_00530_004_20180809T214742_0712_05',\n",
-       " 'https://opendap.earthdata.nasa.gov/collections/C2076114664-LPCLOUD/granules/ECOv002_L2_LSTE_00530_004_20180809T214742_0712_04',\n",
-       " 'https://opendap.earthdata.nasa.gov/collections/C2076114664-LPCLOUD/granules/ECOv002_L2_LSTE_00530_005_20180809T214834_0712_04',\n",
-       " 'https://opendap.earthdata.nasa.gov/collections/C2076114664-LPCLOUD/granules/ECOv002_L2_LSTE_00530_006_20180809T214926_0712_04',\n",
-       " 'https://opendap.earthdata.nasa.gov/collections/C2076114664-LPCLOUD/granules/ECOv002_L2_LSTE_00533_005_20180810T023944_0712_04',\n",
-       " 'https://opendap.earthdata.nasa.gov/collections/C2076114664-LPCLOUD/granules/ECOv002_L2_LSTE_00545_002_20180810T205606_0712_04',\n",
-       " 'https://opendap.earthdata.nasa.gov/collections/C2076114664-LPCLOUD/granules/ECOv002_L2_LSTE_00545_003_20180810T205658_0712_04',\n",
-       " 'https://opendap.earthdata.nasa.gov/collections/C2076114664-LPCLOUD/granules/ECOv002_L2_LSTE_00549_011_20180811T032420_0712_04',\n",
-       " 'https://opendap.earthdata.nasa.gov/collections/C2076114664-LPCLOUD/granules/ECOv002_L2_LSTE_00549_011_20180811T032420_0712_03',\n",
-       " 'https://opendap.earthdata.nasa.gov/collections/C2076114664-LPCLOUD/granules/ECOv002_L2_LSTE_00576_004_20180812T204906_0712_04',\n",
-       " 'https://opendap.earthdata.nasa.gov/collections/C2076114664-LPCLOUD/granules/ECOv002_L2_LSTE_00576_005_20180812T204958_0712_04',\n",
-       " 'https://opendap.earthdata.nasa.gov/collections/C2076114664-LPCLOUD/granules/ECOv002_L2_LSTE_00579_008_20180813T013925_0712_03',\n",
-       " 'https://opendap.earthdata.nasa.gov/collections/C2076114664-LPCLOUD/granules/ECOv002_L2_LSTE_00579_008_20180813T013925_0712_04',\n",
-       " 'https://opendap.earthdata.nasa.gov/collections/C2076114664-LPCLOUD/granules/ECOv002_L2_LSTE_00579_009_20180813T014017_0712_03',\n",
-       " 'https://opendap.earthdata.nasa.gov/collections/C2076114664-LPCLOUD/granules/ECOv002_L2_LSTE_00591_001_20180813T195545_0712_04',\n",
-       " 'https://opendap.earthdata.nasa.gov/collections/C2076114664-LPCLOUD/granules/ECOv002_L2_LSTE_00591_001_20180813T195545_0712_05']"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "urls"
    ]
@@ -138,7 +94,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.13"
+   "version": "3.12.11"
   }
  },
  "nbformat": 4,
diff --git a/binder/ECCO.ipynb b/binder/ECCO.ipynb
index a5b1dab..255869d 100644
--- a/binder/ECCO.ipynb
+++ b/binder/ECCO.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "dd69f13e-1576-46cc-88bf-8484bf53788f",
    "metadata": {},
    "outputs": [],
@@ -10,7 +10,6 @@
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "from pydap.net import create_session\n",
-    "import json\n",
     "import cartopy.crs as ccrs\n",
     "import xarray as xr\n",
     "import datetime as dt\n",
@@ -21,18 +20,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "315ae4f8-dde2-4095-93c8-2d23ec754f70",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "pydap version:  3.5.7.dev2+gafd8e4c\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(\"pydap version: \", pydap.__version__)"
    ]
@@ -60,51 +51,6 @@
     "\n"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "941feb00-5526-4aba-a80f-2f1706b74fca",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "session = requests.Session()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "f77714f7-6446-4323-bf8e-1cae2c87d95e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# CMR API base url\n",
-    "cmrurl='https://cmr.earthdata.nasa.gov/search/'\n",
-    "doi = '10.5067/ECL5M-OTS44'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "82158ccc-ead9-4e3c-8fcf-450f20ddfdeb",
-   "metadata": {},
-   "source": [
-    "<span style='font-family:serif'> <font size=\"5.5\"><span style='color:#0066cc'> **Specify time range**\n",
-    "\n",
-    "<font size=\"3\"><span style='color:Black'> This dataset covers `01-01-1992` to `01-18-2018`. \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "7886174c-77b2-48bf-a823-d9f761e98274",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "start_date =  dt.datetime(1992, 1, 1)\n",
-    "end_date = dt.datetime(2017, 12, 31)\n",
-    "\n",
-    "time_range=[start_date,end_date] # One month of data\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "75ede2ce-5c11-40e9-8f83-a8f7bceb0147",
@@ -114,60 +60,33 @@
     "\n",
     "The cell below will search/find all OPeNDAP URLs associated with the Collection concept ID.\n",
     "\n",
-    "The results wll be stored in the variable `granules_urls`.\n",
+    "The results will be stored in the variable `granules_urls`.\n",
+    "\n",
+    "We are interested in two collections: That of Oceanic Temperature and Salinity, and that of the Grid both on the Native LLC90 grid.\n",
+    "\n",
     "    "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "id": "75b72f62-98bc-44c1-b87d-dba29c67e37d",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 16.6 ms, sys: 6.82 ms, total: 23.4 ms\n",
-      "Wall time: 1.39 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "granules_urls = get_cmr_urls(doi=doi, time_range=time_range, limit=100)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "592016ce-36cf-4298-b017-17fee64b5c19",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WE found:  100  total Cloud OPeNDAP URLS associated with this collection!\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"WE found: \", len(granules_urls), \" total Cloud OPeNDAP URLS associated with this collection!\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6ba694bc-4613-4689-a1aa-0d03b1dad47f",
-   "metadata": {},
+   "outputs": [],
    "source": [
-    "<span style='font-family:serif'> <font size=\"5.5\"><span style='color:#0066cc'> **Pydap Approach**\n",
+    "ecco_ts_ccid = \"C1991543728-POCLOUD\" # \n",
+    "grid_ccid = \"C2013557893-POCLOUD\"\n",
+    "\n",
+    "# get 10 years of data\n",
+    "time_range = [dt.datetime(2007, 1, 1), dt.datetime(2017, 12, 31)]\n",
     "\n",
-    "<span style='font-family:serif'> <font size=\"3.5\"> We can use <span style='color:#ff6666'>**PyDAP**<span style='color:black'> to inspect the metadata associated with each of the urls.\n",
+    "cmr_urls = get_cmr_urls(ccid=ecco_ts_ccid, time_range=time_range, limit=1000) # you can incread the limit of results\n",
+    "print(\"################################################ \\n We found a total of \", len(cmr_urls), \"OPeNDAP URLS!!!\\n################################################\")\n",
     "\n",
-    "<span style='font-family:serif'> <font size=\"3.5\">Below we illustrate the use of <span style='color:#ff6666'>**PyDAP**<span style='color:black'> with Token authentication to access OPeNDAP metadata.\n",
     "\n",
-    "<span style='font-family:serif'> <font size=\"3.5\"> This will be useful when accessing OPeNDAP URLs via xarray.\n"
+    "# Get the grid data and\n",
+    "grid_url = get_cmr_urls(ccid=grid_ccid)[0] # only one element\n",
+    "\n"
    ]
   },
   {
@@ -182,7 +101,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "id": "322889ad-dc54-429c-bf48-35476d53bf8a",
    "metadata": {},
    "outputs": [],
@@ -190,7 +109,7 @@
     "auth = earthaccess.login(strategy=\"interactive\", persist=True) # you will be promted to add your EDL credentials\n",
     "\n",
     "# pass Token Authorization to a new Session.\n",
-    "cache_kwargs={'cache_name':'ECCOv4'}\n",
+    "cache_kwargs={'cache_name':'data/ECCOv4'}\n",
     "my_session = create_session(use_cache=True, session=auth.get_session(), cache_kwargs=cache_kwargs)\n",
     "my_session.cache.clear()"
    ]
@@ -200,1349 +119,249 @@
    "id": "fa0042cb-053d-424b-971c-ed8b0d325725",
    "metadata": {},
    "source": [
-    "<span style='font-family:serif'> <font size=\"5.5\"><span style='color:#0066cc'> **Lazy access to remote data via pydap's client API**\n",
+    "<span style='font-family:serif'> <font size=\"5.5\"><span style='color:#0066cc'> **Construct DAP4 URLs and use Constraint Expressions!**\n",
+    "\n",
+    "<font size=\"3\"> Consider that we only want\n",
+    "- `THETA`\n",
+    "- `SALT`\n",
     "\n",
-    "<font size=\"3\"> <span style='color:#ff6666'>**PyDAP**<span style='color:black'> exploits the OPeNDAP's separation between metadata and data, to create lazy dataset objects that point to the data. These lazy objects contain all the attributes detailed in OPeNDAP's metadata files (DMR)"
+    "<font size=\"3\">  and their `dimensions`. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "id": "5ced1396-be08-4a0b-b487-c430781bd247",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 50.1 ms, sys: 10.4 ms, total: 60.5 ms\n",
-      "Wall time: 3.63 s\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "%%time\n",
-    "pyds = open_url(granules_urls[0], session=my_session, protocol='dap4')"
+    "CE = \"dap4.ce=/time;/k;/tile;/j;/i;/THETA;/SALT\"\n",
+    "\n",
+    "# from grid, get the Depth, Z, and Coordinates XC and YC at scalar points\n",
+    "Grid_url = grid_url.replace(\"https\", \"dap4\") + \"?dap4.ce=/tile;/j;/i;/Z;/Depth;/XC;/YC\"\n",
+    "\n",
+    "dap4_urls = [url.replace(\"https\",\"dap4\") + \"?\" + CE for url in cmr_urls]\n",
+    "\n",
+    "dap4_urls[:2]"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "1bb158d1-7184-4d07-b152-bfb9f6300ffc",
+   "cell_type": "markdown",
+   "id": "b8b87a23-0730-4f08-b757-038200fa54ae",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      ".OCEAN_TEMPERATURE_SALINITY_mon_mean_1992-01_ECCO_V4r4_native_llc0090.nc\n",
-      "├──XG\n",
-      "├──Zp1\n",
-      "├──Zl\n",
-      "├──YC\n",
-      "├──XC\n",
-      "├──SALT\n",
-      "├──YG\n",
-      "├──XC_bnds\n",
-      "├──Zu\n",
-      "├──THETA\n",
-      "├──Z_bnds\n",
-      "├──YC_bnds\n",
-      "├──time_bnds\n",
-      "├──Z\n",
-      "├──i\n",
-      "├──i_g\n",
-      "├──j\n",
-      "├──j_g\n",
-      "├──k\n",
-      "├──k_l\n",
-      "├──k_p1\n",
-      "├──k_u\n",
-      "├──nb\n",
-      "├──nv\n",
-      "├──tile\n",
-      "└──time\n"
-     ]
-    }
-   ],
    "source": [
-    "pyds.tree()"
+    "<span style='color:#0066cc'><font size=\"5\"> **Consolidate all URL Metadata Associated with the Data URL of cloud OPeNDAP URLs**\n",
+    "\n",
+    "<font size=\"3\"> You can construct a persistent reference to all Cloud OPeNDAP urls for later use!!!! \n"
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "e438a621-f061-4904-a719-af9be89c1dc7",
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "920caf59-adce-4e9c-bcbf-87ce4a194c13",
    "metadata": {},
+   "outputs": [],
    "source": [
-    "<span style='font-family:serif'> <font size=\"5.5\"><span style='color:#0066cc'> **Not all Variables are of interest. Lets use Constraint Expressions!**\n",
-    "\n",
-    "<font size=\"3\">  Consider that we only want\n",
-    "- `THETA`\n",
-    "- `SALT`\n",
-    "\n",
-    "<font size=\"3\">  and their `dimensions`. "
+    "# clear just in case\n",
+    "my_session.cache.clear()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "id": "935e5a1c-a039-402a-95ea-9891ce021371",
+   "execution_count": null,
+   "id": "9841212a-5bf6-4e17-984e-4248a88604e3",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "dimension of THETA: ['/time', '/k', '/tile', '/j', '/i']\n",
-      "dimension of SALT: ['/time', '/k', '/tile', '/j', '/i']\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "print(\"dimension of THETA:\" , pyds['THETA'].dims)\n",
-    "print(\"dimension of SALT:\" , pyds['SALT'].dims)"
+    "%%time\n",
+    "consolidate_metadata(dap4_urls, my_session, concat_dim='time')"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "30604933-d567-4562-976f-c0f29da4b178",
+   "id": "67694cc9-a99d-477d-b2ca-c49c8ef9fc66",
    "metadata": {},
    "source": [
-    "<span style='color:#0066cc'><font size=\"5\"> **Construct Constraint Expression**\n",
-    "\n",
-    "<font size=\"3\"> That will instruct the Hyrax Data Server to only give use our desired variables.\n",
+    "## What happened?\n",
     "\n",
-    "<font size=\"3\">  This variable will be named `CE`. We will add it to each (granule) cloud OPeNDAP URL. THis will allow us to construct a `Data Cube`\n"
+    "<font size=\"3\"> All necessary metadata was fetch from opendap servers, and it can be used and reused by xarray. \n"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "2f7c2bf1-7c58-4c04-9238-bc166151c3f8",
+   "cell_type": "markdown",
+   "id": "c8faf11a-f0a0-4a04-b4e3-2df3b5bbdf08",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "constraint expression:  ?dap4.ce=/THETA;/SALT;/time;/k;/tile;/j;/i\n"
-     ]
-    }
-   ],
    "source": [
-    "dims = pyds['SALT'].dims\n",
-    "Vars = ['/THETA', '/SALT'] + dims\n",
-    "\n",
-    "# Below construct Contraint Expression\n",
-    "CE = \"?dap4.ce=\"+(\";\").join(Vars)\n",
-    "print(\"constraint expression: \", CE)"
+    "## Create a datacube with xarray and pydap as an engine!\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "id": "0b2b4dc7-08b0-4cfd-8d50-de940384aee4",
+   "execution_count": null,
+   "id": "34f9bc4c-b691-48e4-9be5-4019927aeac2",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " Each Cloud OPeNDAP URL will look like: \n",
-      " https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/ECCO%20Ocean%20Temperature%20and%20Salinity%20-%20Monthly%20Mean%20llc90%20Grid%20(Version%204%20Release%204)/granules/OCEAN_TEMPERATURE_SALINITY_mon_mean_1992-01_ECCO_V4r4_native_llc0090?dap4.ce=/THETA;/SALT;/time;/k;/tile;/j;/i\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "print(\" Each Cloud OPeNDAP URL will look like: \\n\", granules_urls[0]+CE)"
+    "%%time\n",
+    "ds = xr.open_mfdataset(\n",
+    "    dap4_urls, \n",
+    "    engine='pydap', \n",
+    "    session=my_session, \n",
+    "    parallel=True, \n",
+    "    combine='nested', \n",
+    "    concat_dim='time', \n",
+    "    chunks={'tile':1, 'k':1})\n",
+    "ds"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "af091d8f-1a3f-452a-95f7-6f59dca948c6",
+   "id": "3f4c4416-2513-4509-a53e-42731c90637a",
    "metadata": {},
    "source": [
-    "<span style='color:#0066cc'><font size=\"5\"> **Construct DAP4 URLS:**\n",
-    " \n",
+    "## Aggregate data\n",
     "\n",
-    "<font size=\"3\"> A DAP4 url begins with `dap4` as a scheme. \n",
+    "The field variables, and the Grid variables.\n",
     "\n",
-    "<font size=\"3\"> **NOTE**: This is only for xarray and <span style='color:#ff6666'>**PyDAP**<span style='color:black'>.\n"
+    "NOTE: When accessing/streaming Grid data from a separate collection, use a different session object.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "id": "e87755fb-790e-472f-aeac-1f8d7840875a",
+   "execution_count": null,
+   "id": "eda49c00-22f2-462c-9276-cf496a7c63cb",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['dap4://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/ECCO%20Ocean%20Temperature%20and%20Salinity%20-%20Monthly%20Mean%20llc90%20Grid%20(Version%204%20Release%204)/granules/OCEAN_TEMPERATURE_SALINITY_mon_mean_1992-01_ECCO_V4r4_native_llc0090?dap4.ce=/THETA;/SALT;/time;/k;/tile;/j;/i',\n",
-       " 'dap4://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/ECCO%20Ocean%20Temperature%20and%20Salinity%20-%20Monthly%20Mean%20llc90%20Grid%20(Version%204%20Release%204)/granules/OCEAN_TEMPERATURE_SALINITY_mon_mean_1992-02_ECCO_V4r4_native_llc0090?dap4.ce=/THETA;/SALT;/time;/k;/tile;/j;/i',\n",
-       " 'dap4://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/ECCO%20Ocean%20Temperature%20and%20Salinity%20-%20Monthly%20Mean%20llc90%20Grid%20(Version%204%20Release%204)/granules/OCEAN_TEMPERATURE_SALINITY_mon_mean_1992-03_ECCO_V4r4_native_llc0090?dap4.ce=/THETA;/SALT;/time;/k;/tile;/j;/i',\n",
-       " 'dap4://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/ECCO%20Ocean%20Temperature%20and%20Salinity%20-%20Monthly%20Mean%20llc90%20Grid%20(Version%204%20Release%204)/granules/OCEAN_TEMPERATURE_SALINITY_mon_mean_1992-04_ECCO_V4r4_native_llc0090?dap4.ce=/THETA;/SALT;/time;/k;/tile;/j;/i']"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "new_urls = [url.replace(\"https\", \"dap4\") + CE for url in granules_urls][:100] # consider only the first 100 urls\n",
-    "new_urls[:4]"
+    "session = create_session(session=auth.get_session())"
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "b8b87a23-0730-4f08-b757-038200fa54ae",
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c4114758-39a6-46b9-809f-b831f4e70b75",
    "metadata": {},
+   "outputs": [],
    "source": [
-    "<span style='color:#0066cc'><font size=\"5\"> **Consolidate all URL Metadata Associated with the Data URL of cloud OPeNDAP URLs**\n",
-    "\n",
-    "<font size=\"3\"> You can construct a persistent reference to all Cloud OPeNDAP urls for later use!!!! \n"
+    "Grid_url"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "id": "920caf59-adce-4e9c-bcbf-87ce4a194c13",
+   "execution_count": null,
+   "id": "eb3337c8-6617-4ea8-bdda-6877bb78f3fa",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# clear just in case\n",
-    "my_session.cache.clear()"
+    "### create an individual dataset with only the variables of interest\n",
+    "grid_ds = xr.open_dataset(Grid_url, engine='pydap', session=session, chunks={\"k\":1, 'tile':1})\n",
+    "\n",
+    "#### Combine the two datasets into a single dataset reference\n",
+    "nds = xr.merge([ds, grid_ds])\n",
+    "nds"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "id": "9841212a-5bf6-4e17-984e-4248a88604e3",
+   "execution_count": null,
+   "id": "8daa6999-0822-4b1a-893d-6b1689ab03fa",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "datacube has dimensions ['i[0:1:89]', 'j[0:1:89]', 'k[0:1:49]', 'tile[0:1:12]'] , and concat dim: `time`\n",
-      "CPU times: user 1.25 s, sys: 521 ms, total: 1.77 s\n",
-      "Wall time: 33.5 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "consolidate_metadata(new_urls, my_session, concat_dim='time')"
-   ]
+   "outputs": [],
+   "source": []
   },
   {
    "cell_type": "markdown",
-   "id": "67694cc9-a99d-477d-b2ca-c49c8ef9fc66",
+   "id": "8b072c13-7f99-45dc-818f-91fc7b437e08",
    "metadata": {},
    "source": [
-    "## What happened?\n",
-    "\n",
-    "<font size=\"3\"> All necessary metadata was fetch from opendap servers, and it can be used and reused by xarray. \n"
+    "### Stream a year of data with OPeNDAP, subset tiles, and store data locally with Xarray and PyDAP as engine"
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "c8faf11a-f0a0-4a04-b4e3-2df3b5bbdf08",
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bdf43e0f-8438-4648-ab51-60454f9e3638",
    "metadata": {},
+   "outputs": [],
    "source": [
-    "## Create a datacube with xarray and pydap as an engine!\n"
+    "%%time\n",
+    "nds.isel(time=slice(12), k=0, tile=[2,6,10]).to_netcdf(\"data/ECCOv4_NA.nc4\")"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "34f9bc4c-b691-48e4-9be5-4019927aeac2",
+   "cell_type": "markdown",
+   "id": "9d70f262-3264-4b7a-881f-4a151022d3a7",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 1.38 s, sys: 193 ms, total: 1.58 s\n",
-      "Wall time: 1.45 s\n"
-     ]
-    },
-    {
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-       "  padding-left: 0 !important;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input {\n",
-       "  display: inline-block;\n",
-       "  opacity: 0;\n",
-       "  height: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input + label {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "  border: 2px solid transparent !important;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label {\n",
-       "  cursor: pointer;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:focus + label {\n",
-       "  border: 2px solid var(--xr-font-color0) !important;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
-       "  color: var(--xr-font-color0);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary {\n",
-       "  grid-column: 1;\n",
-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: \"►\";\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: \"▼\";\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: \"(\";\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: \")\";\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: \",\";\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  border-color: var(--xr-background-color-row-odd);\n",
-       "  margin-bottom: 0;\n",
-       "  padding-top: 2px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "  border-color: var(--xr-background-color-row-even);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-index-preview {\n",
-       "  grid-column: 2 / 5;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  display: none;\n",
-       "  border-top: 2px dotted var(--xr-background-color);\n",
-       "  padding-bottom: 20px !important;\n",
-       "  padding-top: 10px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in + label,\n",
-       ".xr-var-data-in + label,\n",
-       ".xr-index-data-in + label {\n",
-       "  padding: 0 1px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data,\n",
-       ".xr-index-data-in:checked ~ .xr-index-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > pre,\n",
-       ".xr-index-data > pre,\n",
-       ".xr-var-data > table > tbody > tr {\n",
-       "  background-color: transparent !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-index-name div,\n",
-       ".xr-index-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
-       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
-       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
-       "  color: var(--xr-font-color0);\n",
-       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
-       "  stroke-width: 0.8px;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 4GB\n",
-       "Dimensions:  (time: 100, k: 50, tile: 13, j: 90, i: 90)\n",
-       "Coordinates:\n",
-       "  * i        (i) int32 360B 0 1 2 3 4 5 6 7 8 9 ... 81 82 83 84 85 86 87 88 89\n",
-       "  * j        (j) int32 360B 0 1 2 3 4 5 6 7 8 9 ... 81 82 83 84 85 86 87 88 89\n",
-       "  * k        (k) int32 200B 0 1 2 3 4 5 6 7 8 9 ... 41 42 43 44 45 46 47 48 49\n",
-       "  * tile     (tile) int32 52B 0 1 2 3 4 5 6 7 8 9 10 11 12\n",
-       "  * time     (time) datetime64[ns] 800B 1992-01-16T18:00:00 ... 2000-04-16\n",
-       "Data variables:\n",
-       "    SALT     (time, k, tile, j, i) float32 2GB dask.array&lt;chunksize=(1, 1, 1, 90, 90), meta=np.ndarray&gt;\n",
-       "    THETA    (time, k, tile, j, i) float32 2GB dask.array&lt;chunksize=(1, 1, 1, 90, 90), meta=np.ndarray&gt;\n",
-       "Attributes: (12/62)\n",
-       "    acknowledgement:                 This research was carried out by the Jet...\n",
-       "    author:                          Ian Fenty and Ou Wang\n",
-       "    cdm_data_type:                   Grid\n",
-       "    comment:                         Fields provided on the curvilinear lat-l...\n",
-       "    Conventions:                     CF-1.8, ACDD-1.3\n",
-       "    coordinates_comment:             Note: the global &#x27;coordinates&#x27; attribute...\n",
-       "    ...                              ...\n",
-       "    time_coverage_duration:          P1M\n",
-       "    time_coverage_end:               1992-02-01T00:00:00\n",
-       "    time_coverage_resolution:        P1M\n",
-       "    time_coverage_start:             1992-01-01T12:00:00\n",
-       "    title:                           ECCO Ocean Temperature and Salinity - Mo...\n",
-       "    uuid:                            f07693e6-4181-11eb-beb3-0cc47a3f44ff</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-501b441b-22ae-4b19-a9fd-896d704467d3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-501b441b-22ae-4b19-a9fd-896d704467d3' 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>: 100</li><li><span class='xr-has-index'>k</span>: 50</li><li><span class='xr-has-index'>tile</span>: 13</li><li><span class='xr-has-index'>j</span>: 90</li><li><span class='xr-has-index'>i</span>: 90</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-10ec1a89-edec-4939-99af-35ade6c38995' class='xr-section-summary-in' type='checkbox'  checked><label for='section-10ec1a89-edec-4939-99af-35ade6c38995' class='xr-section-summary' >Coordinates: <span>(5)</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'>i</span></div><div class='xr-var-dims'>(i)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 84 85 86 87 88 89</div><input id='attrs-6db79ab5-d9ee-464d-9bcc-e15ad0b7f6e8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6db79ab5-d9ee-464d-9bcc-e15ad0b7f6e8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8eef74e7-b294-4098-b84b-454f46cbb27a' class='xr-var-data-in' type='checkbox'><label for='data-8eef74e7-b294-4098-b84b-454f46cbb27a' 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>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>grid index in x for variables at tracer and &#x27;v&#x27; locations</dd><dt><span>swap_dim :</span></dt><dd>XC</dd><dt><span>comment :</span></dt><dd>In the Arakawa C-grid system, tracer (e.g., THETA) and &#x27;v&#x27; variables (e.g., VVEL) have the same x coordinate on the model grid.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>origname :</span></dt><dd>i</dd><dt><span>fullnamepath :</span></dt><dd>/i</dd><dt><span>Maps :</span></dt><dd>()</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
-       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
-       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
-       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
-       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
-       "      dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>j</span></div><div class='xr-var-dims'>(j)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 84 85 86 87 88 89</div><input id='attrs-92edd6d1-407a-4823-b155-57c2608bf51a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-92edd6d1-407a-4823-b155-57c2608bf51a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2050405b-dcdc-4a5f-841b-50c872d0d2ed' class='xr-var-data-in' type='checkbox'><label for='data-2050405b-dcdc-4a5f-841b-50c872d0d2ed' 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>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>grid index in y for variables at tracer and &#x27;u&#x27; locations</dd><dt><span>swap_dim :</span></dt><dd>YC</dd><dt><span>comment :</span></dt><dd>In the Arakawa C-grid system, tracer (e.g., THETA) and &#x27;u&#x27; variables (e.g., UVEL) have the same y coordinate on the model grid.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>origname :</span></dt><dd>j</dd><dt><span>fullnamepath :</span></dt><dd>/j</dd><dt><span>Maps :</span></dt><dd>()</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
-       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
-       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
-       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
-       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
-       "      dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>k</span></div><div class='xr-var-dims'>(k)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 44 45 46 47 48 49</div><input id='attrs-e1e7af80-8537-4301-bcca-24b7798ce1b4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e1e7af80-8537-4301-bcca-24b7798ce1b4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cadb05ef-719c-4482-83ec-21418128deef' class='xr-var-data-in' type='checkbox'><label for='data-cadb05ef-719c-4482-83ec-21418128deef' 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>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>grid index in z for tracer variables</dd><dt><span>swap_dim :</span></dt><dd>Z</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>origname :</span></dt><dd>k</dd><dt><span>fullnamepath :</span></dt><dd>/k</dd><dt><span>Maps :</span></dt><dd>()</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
-       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
-       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>tile</span></div><div class='xr-var-dims'>(tile)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7 8 9 10 11 12</div><input id='attrs-7eb4d581-52be-40c8-8c67-80abd9e01bdb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7eb4d581-52be-40c8-8c67-80abd9e01bdb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1603176b-95b4-4118-aea5-3ed35e30b881' class='xr-var-data-in' type='checkbox'><label for='data-1603176b-95b4-4118-aea5-3ed35e30b881' 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>lat-lon-cap tile index</dd><dt><span>comment :</span></dt><dd>The ECCO V4 horizontal model grid is divided into 13 tiles of 90x90 cells for convenience.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>origname :</span></dt><dd>tile</dd><dt><span>fullnamepath :</span></dt><dd>/tile</dd><dt><span>Maps :</span></dt><dd>()</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12], dtype=int32)</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'>1992-01-16T18:00:00 ... 2000-04-16</div><input id='attrs-44c573c9-3d61-49ed-9ba3-e1f9f51406f7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-44c573c9-3d61-49ed-9ba3-e1f9f51406f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d599c868-f37c-4ca4-80ce-e0b4a5614478' class='xr-var-data-in' type='checkbox'><label for='data-d599c868-f37c-4ca4-80ce-e0b4a5614478' 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>center time of averaging period</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>origname :</span></dt><dd>time</dd><dt><span>fullnamepath :</span></dt><dd>/time</dd><dt><span>Maps :</span></dt><dd>()</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1992-01-16T18:00:00.000000000&#x27;, &#x27;1992-02-15T12:00:00.000000000&#x27;,\n",
-       "       &#x27;1992-03-16T12:00:00.000000000&#x27;, &#x27;1992-04-16T00:00:00.000000000&#x27;,\n",
-       "       &#x27;1992-05-16T12:00:00.000000000&#x27;, &#x27;1992-06-16T00:00:00.000000000&#x27;,\n",
-       "       &#x27;1992-07-16T12:00:00.000000000&#x27;, &#x27;1992-08-16T12:00:00.000000000&#x27;,\n",
-       "       &#x27;1992-09-16T00:00:00.000000000&#x27;, &#x27;1992-10-16T12:00:00.000000000&#x27;,\n",
-       "       &#x27;1992-11-16T00:00:00.000000000&#x27;, &#x27;1992-12-16T12:00:00.000000000&#x27;,\n",
-       "       &#x27;1993-01-16T12:00:00.000000000&#x27;, &#x27;1993-02-15T00:00:00.000000000&#x27;,\n",
-       "       &#x27;1993-03-16T12:00:00.000000000&#x27;, &#x27;1993-04-16T00:00:00.000000000&#x27;,\n",
-       "       &#x27;1993-05-16T12:00:00.000000000&#x27;, &#x27;1993-06-16T00:00:00.000000000&#x27;,\n",
-       "       &#x27;1993-07-16T12:00:00.000000000&#x27;, &#x27;1993-08-16T12:00:00.000000000&#x27;,\n",
-       "       &#x27;1993-09-16T00:00:00.000000000&#x27;, &#x27;1993-10-16T12:00:00.000000000&#x27;,\n",
-       "       &#x27;1993-11-16T00:00:00.000000000&#x27;, &#x27;1993-12-16T12:00:00.000000000&#x27;,\n",
-       "       &#x27;1994-01-16T12:00:00.000000000&#x27;, &#x27;1994-02-15T00:00:00.000000000&#x27;,\n",
-       "       &#x27;1994-03-16T12:00:00.000000000&#x27;, &#x27;1994-04-16T00:00:00.000000000&#x27;,\n",
-       "       &#x27;1994-05-16T12:00:00.000000000&#x27;, &#x27;1994-06-16T00:00:00.000000000&#x27;,\n",
-       "       &#x27;1994-07-16T12:00:00.000000000&#x27;, &#x27;1994-08-16T12:00:00.000000000&#x27;,\n",
-       "       &#x27;1994-09-16T00:00:00.000000000&#x27;, &#x27;1994-10-16T12:00:00.000000000&#x27;,\n",
-       "       &#x27;1994-11-16T00:00:00.000000000&#x27;, &#x27;1994-12-16T12:00:00.000000000&#x27;,\n",
-       "       &#x27;1995-01-16T12:00:00.000000000&#x27;, &#x27;1995-02-15T00:00:00.000000000&#x27;,\n",
-       "       &#x27;1995-03-16T12:00:00.000000000&#x27;, &#x27;1995-04-16T00:00:00.000000000&#x27;,\n",
-       "       &#x27;1995-05-16T12:00:00.000000000&#x27;, &#x27;1995-06-16T00:00:00.000000000&#x27;,\n",
-       "       &#x27;1995-07-16T12:00:00.000000000&#x27;, &#x27;1995-08-16T12:00:00.000000000&#x27;,\n",
-       "       &#x27;1995-09-16T00:00:00.000000000&#x27;, &#x27;1995-10-16T12:00:00.000000000&#x27;,\n",
-       "       &#x27;1995-11-16T00:00:00.000000000&#x27;, &#x27;1995-12-16T12:00:00.000000000&#x27;,\n",
-       "       &#x27;1996-01-16T12:00:00.000000000&#x27;, &#x27;1996-02-15T12:00:00.000000000&#x27;,\n",
-       "       &#x27;1996-03-16T12:00:00.000000000&#x27;, &#x27;1996-04-16T00:00:00.000000000&#x27;,\n",
-       "       &#x27;1996-05-16T12:00:00.000000000&#x27;, &#x27;1996-06-16T00:00:00.000000000&#x27;,\n",
-       "       &#x27;1996-07-16T12:00:00.000000000&#x27;, &#x27;1996-08-16T12:00:00.000000000&#x27;,\n",
-       "       &#x27;1996-09-16T00:00:00.000000000&#x27;, &#x27;1996-10-16T12:00:00.000000000&#x27;,\n",
-       "       &#x27;1996-11-16T00:00:00.000000000&#x27;, &#x27;1996-12-16T12:00:00.000000000&#x27;,\n",
-       "       &#x27;1997-01-16T12:00:00.000000000&#x27;, &#x27;1997-02-15T00:00:00.000000000&#x27;,\n",
-       "       &#x27;1997-03-16T12:00:00.000000000&#x27;, &#x27;1997-04-16T00:00:00.000000000&#x27;,\n",
-       "       &#x27;1997-05-16T12:00:00.000000000&#x27;, &#x27;1997-06-16T00:00:00.000000000&#x27;,\n",
-       "       &#x27;1997-07-16T12:00:00.000000000&#x27;, &#x27;1997-08-16T12:00:00.000000000&#x27;,\n",
-       "       &#x27;1997-09-16T00:00:00.000000000&#x27;, &#x27;1997-10-16T12:00:00.000000000&#x27;,\n",
-       "       &#x27;1997-11-16T00:00:00.000000000&#x27;, &#x27;1997-12-16T12:00:00.000000000&#x27;,\n",
-       "       &#x27;1998-01-16T12:00:00.000000000&#x27;, &#x27;1998-02-15T00:00:00.000000000&#x27;,\n",
-       "       &#x27;1998-03-16T12:00:00.000000000&#x27;, &#x27;1998-04-16T00:00:00.000000000&#x27;,\n",
-       "       &#x27;1998-05-16T12:00:00.000000000&#x27;, &#x27;1998-06-16T00:00:00.000000000&#x27;,\n",
-       "       &#x27;1998-07-16T12:00:00.000000000&#x27;, &#x27;1998-08-16T12:00:00.000000000&#x27;,\n",
-       "       &#x27;1998-09-16T00:00:00.000000000&#x27;, &#x27;1998-10-16T12:00:00.000000000&#x27;,\n",
-       "       &#x27;1998-11-16T00:00:00.000000000&#x27;, &#x27;1998-12-16T12:00:00.000000000&#x27;,\n",
-       "       &#x27;1999-01-16T12:00:00.000000000&#x27;, &#x27;1999-02-15T00:00:00.000000000&#x27;,\n",
-       "       &#x27;1999-03-16T12:00:00.000000000&#x27;, &#x27;1999-04-16T00:00:00.000000000&#x27;,\n",
-       "       &#x27;1999-05-16T12:00:00.000000000&#x27;, &#x27;1999-06-16T00:00:00.000000000&#x27;,\n",
-       "       &#x27;1999-07-16T12:00:00.000000000&#x27;, &#x27;1999-08-16T12:00:00.000000000&#x27;,\n",
-       "       &#x27;1999-09-16T00:00:00.000000000&#x27;, &#x27;1999-10-16T12:00:00.000000000&#x27;,\n",
-       "       &#x27;1999-11-16T00:00:00.000000000&#x27;, &#x27;1999-12-16T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2000-01-16T12:00:00.000000000&#x27;, &#x27;2000-02-15T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2000-03-16T12:00:00.000000000&#x27;, &#x27;2000-04-16T00:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2bef3bb8-e070-46b9-b206-e1bc96eee1de' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2bef3bb8-e070-46b9-b206-e1bc96eee1de' class='xr-section-summary' >Data variables: <span>(2)</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>SALT</span></div><div class='xr-var-dims'>(time, k, tile, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 1, 90, 90), meta=np.ndarray&gt;</div><input id='attrs-cb9da24e-6cbb-4247-93eb-d14028d5847e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cb9da24e-6cbb-4247-93eb-d14028d5847e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f79fc09-fbed-4f5d-b1ea-ce73eced6a82' class='xr-var-data-in' type='checkbox'><label for='data-8f79fc09-fbed-4f5d-b1ea-ce73eced6a82' 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>Salinity</dd><dt><span>units :</span></dt><dd>1e-3</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>standard_name :</span></dt><dd>sea_water_salinity</dd><dt><span>valid_min :</span></dt><dd>17.106637954711914</dd><dt><span>valid_max :</span></dt><dd>41.26802444458008</dd><dt><span>comment :</span></dt><dd>Defined using CF convention &#x27;Sea water salinity is the salt content of sea water, often on the Practical Salinity Scale of 1978. However, the unqualified term &#x27;salinity&#x27; is generic and does not necessarily imply any particular method of calculation. The units of salinity are dimensionless and the units attribute should normally be given as 1e-3 or 0.001 i.e. parts per thousand.&#x27; see https://cfconventions.org/Data/cf-standard-names/73/build/cf-standard-name-table.html</dd><dt><span>origname :</span></dt><dd>SALT</dd><dt><span>fullnamepath :</span></dt><dd>/SALT</dd><dt><span>Maps :</span></dt><dd>()</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> 1.96 GiB </td>\n",
-       "                        <td> 31.64 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (100, 50, 13, 90, 90) </td>\n",
-       "                        <td> (1, 1, 1, 90, 90) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 65000 chunks in 201 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>THETA</span></div><div class='xr-var-dims'>(time, k, tile, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 1, 90, 90), meta=np.ndarray&gt;</div><input id='attrs-959c3471-e3bd-4356-b973-bdb13b367f15' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-959c3471-e3bd-4356-b973-bdb13b367f15' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ffe86a09-4d51-42c5-95d1-aeba4773bc75' class='xr-var-data-in' type='checkbox'><label for='data-ffe86a09-4d51-42c5-95d1-aeba4773bc75' 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>Potential temperature </dd><dt><span>units :</span></dt><dd>degree_C</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>standard_name :</span></dt><dd>sea_water_potential_temperature</dd><dt><span>comment :</span></dt><dd>Sea water potential temperature is the temperature a parcel of sea water would have if moved adiabatically to sea level pressure. Note: the equation of state is a modified UNESCO formula by Jackett and McDougall (1995), which uses the model variable potential temperature as input assuming a horizontally and temporally constant pressure of $p_0=-g \r",
-       "ho_{0} z$.</dd><dt><span>valid_min :</span></dt><dd>-2.2909388542175293</dd><dt><span>valid_max :</span></dt><dd>36.032955169677734</dd><dt><span>origname :</span></dt><dd>THETA</dd><dt><span>fullnamepath :</span></dt><dd>/THETA</dd><dt><span>Maps :</span></dt><dd>()</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",
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-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 1.96 GiB </td>\n",
-       "                        <td> 31.64 kiB </td>\n",
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-       "                        <th> Shape </th>\n",
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-       "                        <td> (1, 1, 1, 90, 90) </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 65000 chunks in 201 graph layers </td>\n",
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-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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-       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
-       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
-       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
-       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
-       "      dtype=&#x27;int32&#x27;, name=&#x27;i&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>j</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-bf7270ba-601f-46c3-a867-e32128f87d72' class='xr-index-data-in' type='checkbox'/><label for='index-bf7270ba-601f-46c3-a867-e32128f87d72' 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,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
-       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
-       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
-       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
-       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
-       "      dtype=&#x27;int32&#x27;, name=&#x27;j&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>k</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-db10d364-b955-4354-bd6a-cb8af5ee529d' class='xr-index-data-in' type='checkbox'/><label for='index-db10d364-b955-4354-bd6a-cb8af5ee529d' 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,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
-       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
-       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49],\n",
-       "      dtype=&#x27;int32&#x27;, name=&#x27;k&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>tile</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-35225760-25bb-4118-b04f-dd6720dd6b21' class='xr-index-data-in' type='checkbox'/><label for='index-35225760-25bb-4118-b04f-dd6720dd6b21' 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, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype=&#x27;int32&#x27;, name=&#x27;tile&#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-2c5951c2-8a34-4984-877f-e53c3dc3ce3e' class='xr-index-data-in' type='checkbox'/><label for='index-2c5951c2-8a34-4984-877f-e53c3dc3ce3e' 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;1992-01-16 18:00:00&#x27;, &#x27;1992-02-15 12:00:00&#x27;,\n",
-       "               &#x27;1992-03-16 12:00:00&#x27;, &#x27;1992-04-16 00:00:00&#x27;,\n",
-       "               &#x27;1992-05-16 12:00:00&#x27;, &#x27;1992-06-16 00:00:00&#x27;,\n",
-       "               &#x27;1992-07-16 12:00:00&#x27;, &#x27;1992-08-16 12:00:00&#x27;,\n",
-       "               &#x27;1992-09-16 00:00:00&#x27;, &#x27;1992-10-16 12:00:00&#x27;,\n",
-       "               &#x27;1992-11-16 00:00:00&#x27;, &#x27;1992-12-16 12:00:00&#x27;,\n",
-       "               &#x27;1993-01-16 12:00:00&#x27;, &#x27;1993-02-15 00:00:00&#x27;,\n",
-       "               &#x27;1993-03-16 12:00:00&#x27;, &#x27;1993-04-16 00:00:00&#x27;,\n",
-       "               &#x27;1993-05-16 12:00:00&#x27;, &#x27;1993-06-16 00:00:00&#x27;,\n",
-       "               &#x27;1993-07-16 12:00:00&#x27;, &#x27;1993-08-16 12:00:00&#x27;,\n",
-       "               &#x27;1993-09-16 00:00:00&#x27;, &#x27;1993-10-16 12:00:00&#x27;,\n",
-       "               &#x27;1993-11-16 00:00:00&#x27;, &#x27;1993-12-16 12:00:00&#x27;,\n",
-       "               &#x27;1994-01-16 12:00:00&#x27;, &#x27;1994-02-15 00:00:00&#x27;,\n",
-       "               &#x27;1994-03-16 12:00:00&#x27;, &#x27;1994-04-16 00:00:00&#x27;,\n",
-       "               &#x27;1994-05-16 12:00:00&#x27;, &#x27;1994-06-16 00:00:00&#x27;,\n",
-       "               &#x27;1994-07-16 12:00:00&#x27;, &#x27;1994-08-16 12:00:00&#x27;,\n",
-       "               &#x27;1994-09-16 00:00:00&#x27;, &#x27;1994-10-16 12:00:00&#x27;,\n",
-       "               &#x27;1994-11-16 00:00:00&#x27;, &#x27;1994-12-16 12:00:00&#x27;,\n",
-       "               &#x27;1995-01-16 12:00:00&#x27;, &#x27;1995-02-15 00:00:00&#x27;,\n",
-       "               &#x27;1995-03-16 12:00:00&#x27;, &#x27;1995-04-16 00:00:00&#x27;,\n",
-       "               &#x27;1995-05-16 12:00:00&#x27;, &#x27;1995-06-16 00:00:00&#x27;,\n",
-       "               &#x27;1995-07-16 12:00:00&#x27;, &#x27;1995-08-16 12:00:00&#x27;,\n",
-       "               &#x27;1995-09-16 00:00:00&#x27;, &#x27;1995-10-16 12:00:00&#x27;,\n",
-       "               &#x27;1995-11-16 00:00:00&#x27;, &#x27;1995-12-16 12:00:00&#x27;,\n",
-       "               &#x27;1996-01-16 12:00:00&#x27;, &#x27;1996-02-15 12:00:00&#x27;,\n",
-       "               &#x27;1996-03-16 12:00:00&#x27;, &#x27;1996-04-16 00:00:00&#x27;,\n",
-       "               &#x27;1996-05-16 12:00:00&#x27;, &#x27;1996-06-16 00:00:00&#x27;,\n",
-       "               &#x27;1996-07-16 12:00:00&#x27;, &#x27;1996-08-16 12:00:00&#x27;,\n",
-       "               &#x27;1996-09-16 00:00:00&#x27;, &#x27;1996-10-16 12:00:00&#x27;,\n",
-       "               &#x27;1996-11-16 00:00:00&#x27;, &#x27;1996-12-16 12:00:00&#x27;,\n",
-       "               &#x27;1997-01-16 12:00:00&#x27;, &#x27;1997-02-15 00:00:00&#x27;,\n",
-       "               &#x27;1997-03-16 12:00:00&#x27;, &#x27;1997-04-16 00:00:00&#x27;,\n",
-       "               &#x27;1997-05-16 12:00:00&#x27;, &#x27;1997-06-16 00:00:00&#x27;,\n",
-       "               &#x27;1997-07-16 12:00:00&#x27;, &#x27;1997-08-16 12:00:00&#x27;,\n",
-       "               &#x27;1997-09-16 00:00:00&#x27;, &#x27;1997-10-16 12:00:00&#x27;,\n",
-       "               &#x27;1997-11-16 00:00:00&#x27;, &#x27;1997-12-16 12:00:00&#x27;,\n",
-       "               &#x27;1998-01-16 12:00:00&#x27;, &#x27;1998-02-15 00:00:00&#x27;,\n",
-       "               &#x27;1998-03-16 12:00:00&#x27;, &#x27;1998-04-16 00:00:00&#x27;,\n",
-       "               &#x27;1998-05-16 12:00:00&#x27;, &#x27;1998-06-16 00:00:00&#x27;,\n",
-       "               &#x27;1998-07-16 12:00:00&#x27;, &#x27;1998-08-16 12:00:00&#x27;,\n",
-       "               &#x27;1998-09-16 00:00:00&#x27;, &#x27;1998-10-16 12:00:00&#x27;,\n",
-       "               &#x27;1998-11-16 00:00:00&#x27;, &#x27;1998-12-16 12:00:00&#x27;,\n",
-       "               &#x27;1999-01-16 12:00:00&#x27;, &#x27;1999-02-15 00:00:00&#x27;,\n",
-       "               &#x27;1999-03-16 12:00:00&#x27;, &#x27;1999-04-16 00:00:00&#x27;,\n",
-       "               &#x27;1999-05-16 12:00:00&#x27;, &#x27;1999-06-16 00:00:00&#x27;,\n",
-       "               &#x27;1999-07-16 12:00:00&#x27;, &#x27;1999-08-16 12:00:00&#x27;,\n",
-       "               &#x27;1999-09-16 00:00:00&#x27;, &#x27;1999-10-16 12:00:00&#x27;,\n",
-       "               &#x27;1999-11-16 00:00:00&#x27;, &#x27;1999-12-16 12:00:00&#x27;,\n",
-       "               &#x27;2000-01-16 12:00:00&#x27;, &#x27;2000-02-15 12:00:00&#x27;,\n",
-       "               &#x27;2000-03-16 12:00:00&#x27;, &#x27;2000-04-16 00:00:00&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-62538bbf-0c4f-4c0d-b300-39d23447a2a4' class='xr-section-summary-in' type='checkbox'  ><label for='section-62538bbf-0c4f-4c0d-b300-39d23447a2a4' class='xr-section-summary' >Attributes: <span>(62)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>acknowledgement :</span></dt><dd>This research was carried out by the Jet Propulsion Laboratory, managed by the California Institute of Technology under a contract with the National Aeronautics and Space Administration.</dd><dt><span>author :</span></dt><dd>Ian Fenty and Ou Wang</dd><dt><span>cdm_data_type :</span></dt><dd>Grid</dd><dt><span>comment :</span></dt><dd>Fields provided on the curvilinear lat-lon-cap 90 (llc90) native grid used in the ECCO model.</dd><dt><span>Conventions :</span></dt><dd>CF-1.8, ACDD-1.3</dd><dt><span>coordinates_comment :</span></dt><dd>Note: the global &#x27;coordinates&#x27; attribute describes auxillary coordinates.</dd><dt><span>creator_email :</span></dt><dd>ecco-group@mit.edu</dd><dt><span>creator_institution :</span></dt><dd>NASA Jet Propulsion Laboratory (JPL)</dd><dt><span>creator_name :</span></dt><dd>ECCO Consortium</dd><dt><span>creator_type :</span></dt><dd>group</dd><dt><span>creator_url :</span></dt><dd>https://ecco-group.org</dd><dt><span>date_created :</span></dt><dd>2020-12-18T14:39:50</dd><dt><span>date_issued :</span></dt><dd>2020-12-18T14:39:50</dd><dt><span>date_metadata_modified :</span></dt><dd>2021-03-15T21:53:43</dd><dt><span>date_modified :</span></dt><dd>2021-03-15T21:53:43</dd><dt><span>geospatial_bounds_crs :</span></dt><dd>EPSG:4326</dd><dt><span>geospatial_lat_max :</span></dt><dd>90.0</dd><dt><span>geospatial_lat_min :</span></dt><dd>-90.0</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>variable</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lon_max :</span></dt><dd>180.0</dd><dt><span>geospatial_lon_min :</span></dt><dd>-180.0</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>variable</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>geospatial_vertical_max :</span></dt><dd>0.0</dd><dt><span>geospatial_vertical_min :</span></dt><dd>-6134.5</dd><dt><span>geospatial_vertical_positive :</span></dt><dd>up</dd><dt><span>geospatial_vertical_resolution :</span></dt><dd>variable</dd><dt><span>geospatial_vertical_units :</span></dt><dd>meter</dd><dt><span>history :</span></dt><dd>Inaugural release of an ECCO Central Estimate solution to PO.DAAC</dd><dt><span>id :</span></dt><dd>10.5067/ECL5M-OTS44</dd><dt><span>institution :</span></dt><dd>NASA Jet Propulsion Laboratory (JPL)</dd><dt><span>instrument_vocabulary :</span></dt><dd>GCMD instrument keywords</dd><dt><span>keywords :</span></dt><dd>EARTH SCIENCE &gt; OCEANS &gt; OCEAN TEMPERATURE &gt; POTENTIAL TEMPERATURE, EARTH SCIENCE &gt; OCEANS &gt; SALINITY/DENSITY &gt; SALINITY, EARTH SCIENCE SERVICES &gt; MODELS &gt; EARTH SCIENCE REANALYSES/ASSIMILATION MODELS</dd><dt><span>keywords_vocabulary :</span></dt><dd>NASA Global Change Master Directory (GCMD) Science Keywords</dd><dt><span>license :</span></dt><dd>Public Domain</dd><dt><span>metadata_link :</span></dt><dd>https://cmr.earthdata.nasa.gov/search/collections.umm_json?ShortName=ECCO_L4_TEMP_SALINITY_LLC0090GRID_MONTHLY_V4R4</dd><dt><span>naming_authority :</span></dt><dd>gov.nasa.jpl</dd><dt><span>platform :</span></dt><dd>ERS-1/2, TOPEX/Poseidon, Geosat Follow-On (GFO), ENVISAT, Jason-1, Jason-2, CryoSat-2, SARAL/AltiKa, Jason-3, AVHRR, Aquarius, SSM/I, SSMIS, GRACE, DTU17MDT, Argo, WOCE, GO-SHIP, MEOP, Ice Tethered Profilers (ITP)</dd><dt><span>platform_vocabulary :</span></dt><dd>GCMD platform keywords</dd><dt><span>processing_level :</span></dt><dd>L4</dd><dt><span>product_name :</span></dt><dd>OCEAN_TEMPERATURE_SALINITY_mon_mean_1992-01_ECCO_V4r4_native_llc0090.nc</dd><dt><span>product_time_coverage_end :</span></dt><dd>2018-01-01T00:00:00</dd><dt><span>product_time_coverage_start :</span></dt><dd>1992-01-01T12:00:00</dd><dt><span>product_version :</span></dt><dd>Version 4, Release 4</dd><dt><span>program :</span></dt><dd>NASA Physical Oceanography, Cryosphere, Modeling, Analysis, and Prediction (MAP)</dd><dt><span>project :</span></dt><dd>Estimating the Circulation and Climate of the Ocean (ECCO)</dd><dt><span>publisher_email :</span></dt><dd>podaac@podaac.jpl.nasa.gov</dd><dt><span>publisher_institution :</span></dt><dd>PO.DAAC</dd><dt><span>publisher_name :</span></dt><dd>Physical Oceanography Distributed Active Archive Center (PO.DAAC)</dd><dt><span>publisher_type :</span></dt><dd>institution</dd><dt><span>publisher_url :</span></dt><dd>https://podaac.jpl.nasa.gov</dd><dt><span>references :</span></dt><dd>ECCO Consortium, Fukumori, I., Wang, O., Fenty, I., Forget, G., Heimbach, P., &amp; Ponte, R. M. 2020. Synopsis of the ECCO Central Production Global Ocean and Sea-Ice State Estimate (Version 4 Release 4). doi:10.5281/zenodo.3765928</dd><dt><span>source :</span></dt><dd>The ECCO V4r4 state estimate was produced by fitting a free-running solution of the MITgcm (checkpoint 66g) to satellite and in situ observational data in a least squares sense using the adjoint method</dd><dt><span>standard_name_vocabulary :</span></dt><dd>NetCDF Climate and Forecast (CF) Metadata Convention</dd><dt><span>summary :</span></dt><dd>This dataset provides monthly-averaged ocean potential temperature and salinity on the lat-lon-cap 90 (llc90) native model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of a global, nominally 1-degree configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea-ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.</dd><dt><span>time_coverage_duration :</span></dt><dd>P1M</dd><dt><span>time_coverage_end :</span></dt><dd>1992-02-01T00:00:00</dd><dt><span>time_coverage_resolution :</span></dt><dd>P1M</dd><dt><span>time_coverage_start :</span></dt><dd>1992-01-01T12:00:00</dd><dt><span>title :</span></dt><dd>ECCO Ocean Temperature and Salinity - Monthly Mean llc90 Grid (Version 4 Release 4)</dd><dt><span>uuid :</span></dt><dd>f07693e6-4181-11eb-beb3-0cc47a3f44ff</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.Dataset> Size: 4GB\n",
-       "Dimensions:  (time: 100, k: 50, tile: 13, j: 90, i: 90)\n",
-       "Coordinates:\n",
-       "  * i        (i) int32 360B 0 1 2 3 4 5 6 7 8 9 ... 81 82 83 84 85 86 87 88 89\n",
-       "  * j        (j) int32 360B 0 1 2 3 4 5 6 7 8 9 ... 81 82 83 84 85 86 87 88 89\n",
-       "  * k        (k) int32 200B 0 1 2 3 4 5 6 7 8 9 ... 41 42 43 44 45 46 47 48 49\n",
-       "  * tile     (tile) int32 52B 0 1 2 3 4 5 6 7 8 9 10 11 12\n",
-       "  * time     (time) datetime64[ns] 800B 1992-01-16T18:00:00 ... 2000-04-16\n",
-       "Data variables:\n",
-       "    SALT     (time, k, tile, j, i) float32 2GB dask.array<chunksize=(1, 1, 1, 90, 90), meta=np.ndarray>\n",
-       "    THETA    (time, k, tile, j, i) float32 2GB dask.array<chunksize=(1, 1, 1, 90, 90), meta=np.ndarray>\n",
-       "Attributes: (12/62)\n",
-       "    acknowledgement:                 This research was carried out by the Jet...\n",
-       "    author:                          Ian Fenty and Ou Wang\n",
-       "    cdm_data_type:                   Grid\n",
-       "    comment:                         Fields provided on the curvilinear lat-l...\n",
-       "    Conventions:                     CF-1.8, ACDD-1.3\n",
-       "    coordinates_comment:             Note: the global 'coordinates' attribute...\n",
-       "    ...                              ...\n",
-       "    time_coverage_duration:          P1M\n",
-       "    time_coverage_end:               1992-02-01T00:00:00\n",
-       "    time_coverage_resolution:        P1M\n",
-       "    time_coverage_start:             1992-01-01T12:00:00\n",
-       "    title:                           ECCO Ocean Temperature and Salinity - Mo...\n",
-       "    uuid:                            f07693e6-4181-11eb-beb3-0cc47a3f44ff"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
-    "%%time\n",
-    "ds = xr.open_mfdataset(new_urls, engine='pydap', session=my_session, parallel=True, combine='nested', concat_dim='time', chunks={'tile':1, 'k':1})\n",
-    "ds"
+    "### Finally, visualized the downloaded data"
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "3f4c4416-2513-4509-a53e-42731c90637a",
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c6e8331b-97d3-446a-871c-eadf8471930e",
    "metadata": {},
+   "outputs": [],
    "source": [
-    "## Download some data\n",
-    "\n",
-    "So far, only metadata has been downloaded. Below we plot some data in the NorthAtlantic ocean\n",
-    "\n",
-    "\n",
-    "\n"
+    "mds = xr.open_dataset(\"data/ECCOv4_NA.nc4\")\n",
+    "mds"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
-   "id": "eb3337c8-6617-4ea8-bdda-6877bb78f3fa",
+   "execution_count": null,
+   "id": "7b2c6fcd-b64c-4572-a46b-f6d167063a15",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 197 ms, sys: 23.2 ms, total: 221 ms\n",
-      "Wall time: 5.51 s\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "%%time\n",
-    "ds['THETA'].isel(time=0, k=0, tile=2).plot(cmap='RdBu_r', vmin=-4, vmax=30);"
+    "Variable = [mds['THETA'][0, i, :, :] for i in range(3)]\n",
+    "clevels = np.linspace(-5, 30, 100)\n",
+    "cMap='RdBu_r'\n",
+    "\n",
+    "ocean_mask = mds[\"Depth\"]>0\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
-   "id": "8daa6999-0822-4b1a-893d-6b1689ab03fa",
+   "execution_count": null,
+   "id": "fe975589-1241-4b1c-a1f8-2589f409cb14",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'long_name': 'Potential temperature ',\n",
-       " 'units': 'degree_C',\n",
-       " 'coverage_content_type': 'modelResult',\n",
-       " 'standard_name': 'sea_water_potential_temperature',\n",
-       " 'comment': 'Sea water potential temperature is the temperature a parcel of sea water would have if moved adiabatically to sea level pressure. Note: the equation of state is a modified UNESCO formula by Jackett and McDougall (1995), which uses the model variable potential temperature as input assuming a horizontally and temporally constant pressure of $p_0=-g \\rho_{0} z$.',\n",
-       " 'valid_min': -2.2909388542175293,\n",
-       " 'valid_max': 36.032955169677734,\n",
-       " 'origname': 'THETA',\n",
-       " 'fullnamepath': '/THETA',\n",
-       " 'Maps': ()}"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "ds['THETA'].isel(time=0, k=0, tile=2).attrs"
+    "fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(8, 8), gridspec_kw={'hspace':0.001, 'wspace':0.001})\n",
+    "AXES_NR = [\n",
+    "    axes[1, 1],\n",
+    "]\n",
+    "AXES_CAP = [axes[0, 1]]\n",
+    "AXES_R = [\n",
+    "    axes[1, 0],\n",
+    "]\n",
+    "for i in range(len(AXES_NR)):\n",
+    "    ocean_mask.isel(tile=0).plot(ax=AXES_NR[i], cmap=\"Greys_r\", add_colorbar=False)\n",
+    "    Variable[0].where(ocean_mask.isel(tile=0)).plot(ax=AXES_NR[i], levels=clevels, cmap=cMap, add_colorbar=False)\n",
+    "\n",
+    "for i in range(len(AXES_CAP)):\n",
+    "    ocean_mask.isel(tile=1).transpose().plot(ax= AXES_CAP[i], cmap=\"Greys_r\", add_colorbar=False, xincrease=False)\n",
+    "    Variable[1].transpose().where(ocean_mask.isel(tile=1)).plot(ax=AXES_CAP[i], levels=clevels, cmap=cMap, add_colorbar=False, xincrease=False)\n",
+    "\n",
+    "\n",
+    "for i in range(len(AXES_R)):\n",
+    "    # AXES_R[i].contourf(Variable[2].transpose()[::-1, :], clevels, cmap=cMap)\n",
+    "    ocean_mask.isel(tile=2).transpose().plot(ax= AXES_R[i], cmap=\"Greys_r\", add_colorbar=False, yincrease=False)\n",
+    "    Variable[2].transpose().where(ocean_mask.isel(tile=2)).plot(ax=AXES_R[i], levels=clevels, cmap=cMap, add_colorbar=False, yincrease=False)\n",
+    "for ax in np.ravel(axes):\n",
+    "    ax.axis('off')\n",
+    "    plt.setp(ax.get_xticklabels(), visible=False)\n",
+    "    plt.setp(ax.get_yticklabels(), visible=False)\n",
+    "    plt.setp(ax.title, visible=False)\n",
+    "\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "29fbbe3d-f4d0-4c73-ab97-15672011fc2a",
+   "id": "0012f5b3-1c1f-4f9f-b579-468a6bbc377d",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -1564,7 +383,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.13"
+   "version": "3.12.11"
   }
  },
  "nbformat": 4,
diff --git a/binder/GetStarted.ipynb b/binder/GetStarted.ipynb
deleted file mode 100644
index c2a0b29..0000000
--- a/binder/GetStarted.ipynb
+++ /dev/null
@@ -1,222 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "9c7cc75e-6d95-4ffe-8227-25810e878b9a",
-   "metadata": {},
-   "source": [
-    "<span style='color:#009999'> <span style='font-family:serif'> <font size=\"15\"> **Getting Started:<span style='color:#0066cc'> Setting Earthdata Access**\n",
-    "    \n",
-    "    \n",
-    "<img src=\"img/EarthData.png\" alt=\"drawing\" width=\"750\"/>    \n",
-    " \n",
-    "\n",
-    "\n",
-    " <span style='color:#ff6666'><font size=\"5\"> **Requirements**\n",
-    "1. <font size=\"3\">Go to the [Login Page](https://urs.earthdata.nasa.gov/home) and set up a Username and Password.\n",
-    "2. <font size=\"3\">Generate a Bearer Token.\n",
-    "3. <font size=\"3\">Copy the Bearer Token onto clipboard.\n",
-    "\n",
-    "<img src=\"img/EDLtoken.png\" alt=\"drawing\" width=\"750\"/>    \n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    " <span style='color:#ff6666'><font size=\"5\"> **Objectives**\n",
-    "- <font size=\"3\"><span style='color:Black'>To demonstrate remote access via token to Earthdata.\n",
-    "- <font size=\"3\"><span style='color:Black'>To store locally the EDL `token` to be used in other notebooks.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1c0ccde6-3f2e-4bef-9d31-92a48064c64a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pydap.client import open_url\n",
-    "import json\n",
-    "from getpass import getpass\n",
-    "from pydap.net import create_session"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "51b2528c-a02f-44cf-b826-408da0307dd7",
-   "metadata": {},
-   "source": [
-    "<span style='color:#ff6666'><font size=\"6.5\"> **EDL Token**:<font size=\"5\">  <span style='color:#0066cc'>\n",
-    "\n",
-    "<font size=\"3\"> The cell below asks to paste your token, taken from your EDL account. No personal information will be displayed"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2a407105-4897-44bf-827a-db7d5bd94e99",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# This gets the EDL token from the users keyboard.\n",
-    "edl_token = getpass(\"EDL Token Value: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8c85a8c2-0c00-4ba1-a729-486762146a2a",
-   "metadata": {},
-   "source": [
-    "<span style='color:#ff6666'><font size=\"5\"> **Approach 1**:<font size=\"5\"> <span style='color:#0066cc'>**Store Token locally to facilitate import**\n",
-    "\n",
-    "<font size=\"3\"> The code below will store the `Token Credentials` needed to access EarthData via pydap locally in a file called `token.json`.\n",
-    "\n",
-    "<font size=\"3\"> Data in `token.json` can now be imported in other notebooks for use when accessing Earthdata via Hyrax in the Cloud / cloud OPeNDAP.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f551a7c5-f51b-4cd6-a9a5-3f775ddae827",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "credentials = {\"token\": edl_token}\n",
-    "\n",
-    "with open('token.json', 'w') as fp:\n",
-    "    json.dump(credentials, fp)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "65dbf552-0996-4fb0-9389-b0945db6352b",
-   "metadata": {},
-   "source": [
-    "<span style='color:#ff6666'><font size=\"5\"> **Approach 2**:<font size=\"5\"> <span style='color:#0066cc'>**Dynamically add your token to session**\n",
-    "\n",
-    "<font size=\"3\"> If you rather not persist your token during the binder session, you can add it to the requests session via <span style='color:#ff6666'>**PyDAP**<span style='color:black'>'s built in session creator. You\n",
-    "will have to do this every time you create a new session. It follows the syntax:\n",
-    "```python\n",
-    "from pydap.net import create_session\n",
-    "\n",
-    "session_kwargs = {\"token\": \"<PasteYourTokenHere>\"}\n",
-    "session = create_session(session_kwargs=session_kwargs)\n",
-    "\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e65d5bb7-6a0b-4306-b6f6-681765f168a7",
-   "metadata": {},
-   "source": [
-    "<span style='color:#ff6666'><font size=\"5\"> **Approach 3**:<font size=\"5\"> <span style='color:#0066cc'>**Username/password**\n",
-    "\n",
-    "<font size=\"3\"> You can also authenticate using your EDL username and password. We recommend creating a `.netrc` document storing your authentication credentials. We do not demonstrate this form of authentication in these tutorials, but you can learn more about these on the official  <span style='color:#ff6666'>**PyDAP**<span style='color:black'> documentation:\n",
-    "\n",
-    "- [How to Authenticate with PyDAP](https://pydap.github.io/pydap/en/notebooks/Authentication.html)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "88226257-eede-4b80-9954-7821bd88fdf3",
-   "metadata": {},
-   "source": [
-    "<span style='font-family:serif'> <font size=\"5.5\"><span style='color:#0066cc'> **Test Access to Verify Authenticated**\n",
-    "\n",
-    "<font size=\"3\"><span style='color:Black'> We now demonstrate how to import the token data and use it to access data via pure <span style='color:#ff6666'>**PyDAP**<span style='color:black'> (one Cloud OPeNDAP URL)\n",
-    "\n",
-    "<font size=\"3\"><span style='color:Black'> For now, lets look into Sea Surface Temperature data from GHRSST for 2022-08-12. The URL is provided below.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "25e602ab-541a-45a9-88f4-8bd9a5e8fa18",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# load token json data\n",
-    "with open('token.json', 'r') as fp:\n",
-    "    token = json.load(fp)\n",
-    "\n",
-    "# pass Token Authorization to a new Session.\n",
-    "my_session = create_session(session_kwargs=token)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "02376ef7-1e87-4aaf-b708-0d249bcbb558",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dataset_url = \"https://opendap.earthdata.nasa.gov/collections/C2036877806-POCLOUD/granules/20220812010000-OSISAF-L3C_GHRSST-SSTsubskin-GOES16-ssteqc_goes16_20220812_010000-v02.0-fv01.0\"\n",
-    "print (\"dataset_url: \", dataset_url)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fa983632-de6f-4355-a1ea-9c08477d8714",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%time\n",
-    "dataset = open_url(dataset_url, session=my_session, protocol=\"dap4\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "36079c1f-8ecc-4f4d-a0a2-6c2ea0e1589d",
-   "metadata": {},
-   "source": [
-    "<span style='font-family:serif'> <font size=\"5.5\"><span style='color:#0066cc'> **Inspect data without downloading**\n",
-    "\n",
-    "<font size=\"3.5\"> The `tree` method from <span style='color:#ff6666'>**PyDAP**<span style='color:black'> allows user to inspect all variables available from the dataset, without actually openning the dataset or downloading it into your machine.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "28551462-d8e5-4eed-877a-0905ef7f8470",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dataset.tree()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "334c32ae-ad14-4d35-96e6-db736d69f0ca",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "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": "3.11.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/binder/Iceberg_drift.ipynb b/binder/Iceberg_drift.ipynb
index 11c8a5a..0d4c5ab 100644
--- a/binder/Iceberg_drift.ipynb
+++ b/binder/Iceberg_drift.ipynb
@@ -27,7 +27,6 @@
     "from pydap.client import get_cmr_urls, consolidate_metadata, open_url\n",
     "import xarray as xr\n",
     "import datetime as dt\n",
-    "import json\n",
     "import glob\n",
     "import os\n",
     "import numpy as np\n",
@@ -60,19 +59,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "auth = earthaccess.login(strategy=\"interactive\")\n",
-    "fs = earthaccess.get_fsspec_https_session()\n",
-    "session_kwargs = {'token': fs.storage_options['client_kwargs']['headers']['Authorization'][7:]}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "95feefc9-b239-4c75-8212-8810be0263c4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "my_session = create_session(use_cache=True, session_kwargs=session_kwargs)\n",
+    "auth = earthaccess.login(strategy=\"interactive\", persist=True) # you will be promted to add your EDL credentials\n",
+    "\n",
+    "# pass Token Authorization to a new Session.\n",
+    "cache_kwargs={'cache_name':'data/Iceberg'}\n",
+    "my_session = create_session(use_cache=True, session=auth.get_session(), cache_kwargs=cache_kwargs)\n",
     "my_session.cache.clear()"
    ]
   },
@@ -87,110 +78,124 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6c31d252-3e4a-4b53-95f3-7ec940bcee5e",
+   "id": "36507ddc-4811-4498-a474-faafaa0a13e6",
    "metadata": {},
    "outputs": [],
    "source": [
-    "oscar_ccid = \"C2098858642-POCLOUD\" # https://podaac.jpl.nasa.gov/dataset/OSCAR_L4_OC_FINAL_V2.0"
+    "oscar_ccid = \"C2098858642-POCLOUD\" # https://podaac.jpl.nasa.gov/dataset/OSCAR_L4_OC_FINAL_V2.0\n",
+    "time_range = ['2019-08-16T00:00:00Z', '2020-09-16T00:00:00Z'] # 1 year of data\n",
+    "\n",
+    "ocean_urls = get_cmr_urls(ccid=oscar_ccid, time_range=time_range, session=my_session, limit=500)\n",
+    "print(\"found: \",len(ocean_urls), \"OSCAR urls\")\n",
+    "\n",
+    "# Turn urls into DAP4 urls\n",
+    "# This dataset has dfimensions lon and coordinates lat.\n",
+    "\n",
+    "CEs = \"?dap4.ce=/u;/v\"\n",
+    "\n",
+    "opendap_OSCAR_urls = [url.replace(\"https\", \"dap4\")+CEs for url in ocean_urls] # \n",
+    "\n",
+    "opendap_OSCAR_urls[:2]"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "770dbf34-f0e4-4aad-95d1-42d3cd1a44e4",
+   "id": "70dc38d3-301d-4a4c-b704-9309b395d5a4",
    "metadata": {},
    "source": [
-    "<span style='font-family:serif'> <font size=\"5.5\"><span style='color:#0066cc'> **Filter data via Temporal Searches**\n"
+    "<span style='font-family:serif'> <font size=\"5.5\"><span style='color:#0066cc'> **Consolidate metadata**\n",
+    "\n",
+    "<font size=\"3.5\"> All URLs belonging to the same Collection share many identical variables and metadata. The following function\n",
+    "reduces redundant metadata\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "36507ddc-4811-4498-a474-faafaa0a13e6",
+   "id": "78af6e01-ea53-4338-bfc3-eda8568a8a81",
    "metadata": {},
    "outputs": [],
    "source": [
-    "time_range = ['2019-08-16T00:00:00Z', '2020-09-16T00:00:00Z'] # 1 year of data\n",
-    "time_range"
+    "my_session.cache.clear()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "33bc012d-7713-43b6-8ce2-6a2e2becc245",
+   "id": "789554d1-0536-4b85-89b7-98e3e928213c",
    "metadata": {},
    "outputs": [],
    "source": [
-    "ocean_urls = get_cmr_urls(ccid=oscar_ccid, time_range=time_range, session=my_session, limit=500)\n",
-    "print(\"found: \",len(ocean_urls), \"OSCAR urls\")\n",
-    "ocean_urls[-1]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e99107c6-6d04-48ef-a51a-8bc1818700b1",
-   "metadata": {},
-   "source": [
-    "<span style='font-family:serif'> <font size=\"5.5\"><span style='color:#0066cc'> **OSCAR data**\n"
+    "%%time\n",
+    "consolidate_metadata(opendap_OSCAR_urls, concat_dim='time', session=my_session)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "541c1e09-9865-4160-8e55-f949c02e2628",
+   "id": "609ea526-516e-493d-baeb-85575ab50b48",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Turn urls into DAP4 urls\n",
-    "opendap_OSCAR_urls = [url.replace(\"https\", \"dap4\") for url in ocean_urls] # \n",
+    "## Create\n",
+    "new_session = create_session(session=auth.get_session())\n",
     "\n",
-    "opendap_OSCAR_urls[:2]"
+    "ds_coords = xr.open_dataset(\n",
+    "    opendap_OSCAR_urls[0]+\";/lat;/lon\",    \n",
+    "    engine='pydap',\n",
+    "    session=new_session,\n",
+    "    chunks={'latitude': 300},\n",
+    ").drop_vars([\"u\", \"v\"])\n",
+    "ds_coords"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "70dc38d3-301d-4a4c-b704-9309b395d5a4",
+   "id": "1fe91987-06fc-4a60-92de-05f9e52509af",
    "metadata": {},
    "source": [
-    "<span style='font-family:serif'> <font size=\"5.5\"><span style='color:#0066cc'> **Consolidate metadata**\n",
+    "<span style='font-family:serif'> <font size=\"5.5\"><span style='color:#0066cc'> **Create Virtually Aggregated Dataset with Xarray**\n",
     "\n",
-    "<font size=\"3.5\"> All URLs belonging to the same Collection share many identical variables and metadata. The following function\n",
-    "reduces redundant metadata\n"
+    "<font size=\"3.5\"> Now, you can create a virtually aggregated view of the dataset that is ready to analyze with Xarray and Pydap as an engine.\n",
+    "\n",
+    "`ds_oscar` will contain all relevant ocean data.\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "789554d1-0536-4b85-89b7-98e3e928213c",
+   "id": "e418db05-17ab-4888-b09c-6418edcd4fe6",
    "metadata": {},
    "outputs": [],
    "source": [
     "%%time\n",
-    "consolidate_metadata(opendap_OSCAR_urls, concat_dim='time', set_maps=True, session=my_session)"
+    "ds_ = xr.open_mfdataset(\n",
+    "    opendap_OSCAR_urls, \n",
+    "    engine='pydap', \n",
+    "    session=my_session, \n",
+    "    combine='nested', \n",
+    "    concat_dim=\"time\", \n",
+    "    chunks={'latitude': 300},\n",
+    ")\n"
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "1fe91987-06fc-4a60-92de-05f9e52509af",
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3c4bf19a-5328-42d4-9def-4b7c00b68e95",
    "metadata": {},
+   "outputs": [],
    "source": [
-    "<span style='font-family:serif'> <font size=\"5.5\"><span style='color:#0066cc'> **Create Virtually Aggregated Dataset with Xarray**\n",
-    "\n",
-    "<font size=\"3.5\"> Now, you can create a virtually aggregated view of the dataset that is ready to analyze with Xarray and Pydap as an engine.\n",
-    "\n",
-    "`ds_oscar` will contain all relevant ocean data.\n"
+    "ds_oscar = xr.merge([ds_, ds_coords])"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e418db05-17ab-4888-b09c-6418edcd4fe6",
+   "id": "c2f24afd-dd46-46b7-ad56-91761268b374",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "%%time\n",
-    "ds_oscar = xr.open_mfdataset(opendap_OSCAR_urls, engine='pydap', session=my_session, combine='nested', concat_dim=\"time\", chunks={'latitude': 300})\n",
-    "ds_oscar"
-   ]
+   "source": []
   },
   {
    "cell_type": "markdown",
@@ -215,7 +220,7 @@
    "outputs": [],
    "source": [
     "ds_oscar['lon'], ds_oscar['lat'] = ds_oscar['lon'].load(), ds_oscar['lat'].load()\n",
-    "ds_oscar = ds_oscar.rename_vars({'lon':'longitude', 'lat':'latitude'}).set_index(longitude='longitude').set_index(latitude='latitude').drop_vars(['ug', 'vg'])"
+    "ds_oscar = ds_oscar.rename_vars({'lon':'longitude', 'lat':'latitude'}).set_index(longitude='longitude').set_index(latitude='latitude')"
    ]
   },
   {
@@ -269,7 +274,7 @@
    "outputs": [],
    "source": [
     "%%time\n",
-    "ds_oscar.isel(latitude=slice(40, 300)).to_netcdf(\"./data/Oscar_data.nc\")"
+    "ds_oscar.isel(latitude=slice(40, 300)).to_netcdf(\"./data/Oscar_data.nc4\")"
    ]
   },
   {
@@ -321,7 +326,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "oscar_path = './data/Oscar_data.nc'"
+    "oscar_path = './data/Oscar_data.nc4'"
    ]
   },
   {
@@ -511,7 +516,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.13"
+   "version": "3.12.11"
   }
  },
  "nbformat": 4,
diff --git a/binder/MERRA-2_Access.ipynb b/binder/MERRA-2_Access.ipynb
index 391d779..560c0a7 100644
--- a/binder/MERRA-2_Access.ipynb
+++ b/binder/MERRA-2_Access.ipynb
@@ -9,24 +9,26 @@
     "\n",
     "\n",
     "<span style='color:#0066cc'><font size=\"5\"> **About the \"Modern-Era Retrospective analysis for Research and Applications\" Version 2 [MERRA-2](https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/docs/) data**\n",
-    "1. <font size=\"3\"><span style='color:Black'> Assimilates observation types not available to its predecessor, MERRA, and includes updates to the Goddard Earth Observing System (GEOS) model and analysis scheme so as to provide a viable ongoing climate analysis beyond MERRA’s terminus.\n",
-    "2. <font size=\"3\"><span style='color:Black'>The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), is the latest atmospheric reanalysis of the modern satellite era produced by NASA’s Global Modeling and Assimilation Office (GMAO).\n",
-    "3. <font size=\"3\"><span style='color:Black'> Other improvements in the quality of MERRA-2 compared with MERRA include the reduction of some spurious trends and jumps related to changes in the observing system and reduced biases and imbalances in aspects of the water cycle.\n",
+    "1. <font size=\"3\"> Assimilates observation types not available to its predecessor, MERRA, and includes updates to the Goddard Earth Observing System (GEOS) model and analysis scheme so as to provide a viable ongoing climate analysis beyond MERRA’s terminus.\n",
+    "2. <font size=\"3\"> The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), is the latest atmospheric reanalysis of the modern satellite era produced by NASA’s Global Modeling and Assimilation Office (GMAO).\n",
+    "3. <font size=\"3\"> Other improvements in the quality of MERRA-2 compared with MERRA include the reduction of some spurious trends and jumps related to changes in the observing system and reduced biases and imbalances in aspects of the water cycle.\n",
     "\n",
     "**Source**: https://doi.org/10.1175/JCLI-D-16-0758.1\n",
     "\n",
     "\n",
     "\n",
     "<span style='color:#ff6666'><font size=\"5\">**Requirements**\n",
-    "1. <font size=\"3\"><span style='color:Black'> Have a Bearer Token for EarthData in the Cloud (See `GetStarted` Notebook)\n",
-    "2. <font size=\"3\"><span style='color:Black'> Upload the Bearer Token from local file `token.json`\n",
+    "1. <font size=\"3\"> Have a Bearer Token for EarthData in the Cloud \n",
+    "2.  <font size=\"3\"> Knowledge of the Collection Concept ID\n",
     "\n",
-    "1. <font size=\"3\"><span style='color:Black'>  Or alternatively, use earthaccess to recover the token interactively.\n"
+    "\n",
+    "\n",
+    "`Author`: Miguel Jimenez-Urias, '25"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "0cb3a702-a107-4915-8073-70f888457d60",
    "metadata": {},
    "outputs": [],
@@ -45,17 +47,13 @@
    "id": "1483a0af-00db-4e55-bdd8-6421f092819c",
    "metadata": {},
    "source": [
-    "<span style='font-family:serif'> <font size=\"5.5\"><span style='color:#0066cc'> **Import Token Authorization and create Session**\n",
-    " \n",
-    "\n",
-    "\n",
-    "<font size=\"3.5\"> Here we use the Bearer Token to create an authenticated session. The Bearer token can be defined and stored locally, as described in the `GetStarted` Notebook. In the following scenario, we will make of earthaccess\n",
+    "<span style='font-family:serif'> <font size=\"5.5\"><span style='color:#0066cc'> **Authenticate**\n",
     "\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "1d34b47b-ee24-4156-8b50-6921c930f3b2",
    "metadata": {},
    "outputs": [],
@@ -63,7 +61,7 @@
     "auth = earthaccess.login(strategy=\"interactive\", persist=True) # you will be promted to add your EDL credentials\n",
     "\n",
     "# pass Token Authorization to a new Session.\n",
-    "cache_kwargs={'cache_name':'MERRA2'}\n",
+    "cache_kwargs={'cache_name':'data/MERRA2'}\n",
     "my_session = create_session(use_cache=True, session=auth.get_session(), cache_kwargs=cache_kwargs)\n",
     "my_session.cache.clear()"
    ]
@@ -78,7 +76,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "2df161ab-be74-495c-abe4-68162b0bb309",
    "metadata": {},
    "outputs": [],
@@ -104,44 +102,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "c34dccba-837c-4cd9-9edd-5d7c78883d3b",
    "metadata": {},
    "outputs": [],
    "source": [
-    "time_range=[dt.datetime(2023, 1, 1), dt.datetime(2023, 1, 31)] # One month of data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "85e8921a-8a93-4866-8eb8-9a55c88622fc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "url_limits = 100 # controls the max number of urls returns. Default is 50"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "8dd2e5a7-34f3-4465-8222-03220c611c93",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "31"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
+    "time_range=[dt.datetime(2023, 1, 1), dt.datetime(2023, 1, 31)] # One month of data\n",
+    "\n",
+    "url_limits = 100 # controls the max number of urls returns. Default is 50\n",
+    "\n",
     "urls = get_cmr_urls(doi=merra2_doi,time_range=time_range, limit=url_limits) # you can incread the limit of results\n",
-    "len(urls)"
+    "print(\"We found: \", len(urls), \" total Cloud OPeNDAP URLS associated with this collection!\")"
    ]
   },
   {
@@ -156,22 +127,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "0d810290-fe77-47f8-a3e7-e921fcdc6a4d",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['https://opendap.earthdata.nasa.gov/collections/C1276812863-GES_DISC/granules/M2T1NXSLV.5.12.4%3AMERRA2_400.tavg1_2d_slv_Nx.20230101.nc4',\n",
-       " 'https://opendap.earthdata.nasa.gov/collections/C1276812863-GES_DISC/granules/M2T1NXSLV.5.12.4%3AMERRA2_400.tavg1_2d_slv_Nx.20230102.nc4']"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "urls[:2]"
    ]
@@ -188,46 +147,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "593be3c9-79d1-4e64-9bda-a086594ad6d4",
    "metadata": {},
    "outputs": [],
    "source": [
-    "new_urls = [url.replace(\"https\", \"dap4\") for url in urls] # "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "49553a6e-4a56-4157-add7-99c5207a4d45",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pyds = open_url(new_urls[0], session=my_session)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "14e9a463-2516-4608-b75a-4bed21647955",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "All variables within dataset: \n",
-      " ['lon', 'time', 'lat', 'TROPPB', 'T2M', 'TQL', 'T500', 'TOX', 'U2M', 'U850', 'PS', 'V850', 'OMEGA500', 'H250', 'Q250', 'T2MDEW', 'PBLTOP', 'V250', 'CLDPRS', 'V50M', 'Q500', 'DISPH', 'H1000', 'TO3', 'TS', 'T10M', 'TROPPT', 'TQI', 'SLP', 'TROPT', 'U250', 'Q850', 'ZLCL', 'TQV', 'V2M', 'T250', 'TROPQ', 'V10M', 'H850', 'T850', 'U50M', 'U10M', 'QV2M', 'CLDTMP', 'TROPPV', 'H500', 'V500', 'T2MWET', 'U500', 'QV10M']\n"
-     ]
-    }
-   ],
-   "source": [
+    "# Make URL a DAP4 URL\n",
+    "\n",
+    "new_urls = [url.replace(\"https\", \"dap4\") for url in urls] \n",
+    "\n",
+    "pyds = open_url(new_urls[0], session=my_session)\n",
+    "\n",
     "print(\"All variables within dataset: \\n\", list(pyds.variables()))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "d73f6cc3-702f-4a73-b9f9-001979b324b7",
    "metadata": {},
    "outputs": [],
@@ -235,28 +171,8 @@
     "Keep_vars = [\"/T2M\", \"/U2M\", \"/V2M\", \"/SLP\"] # this are the variables we want\n",
     "dims = list(set([dim for var in Keep_vars for dim in pyds[var].dims]))  # retain their dimensions\n",
     "Keep_vars += dims\n",
-    "CE=\"?dap4.ce=\" + (';').join(Keep_vars) # need to add this to each url"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "c124d538-ff65-4ae5-9888-075795205cb2",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['dap4://opendap.earthdata.nasa.gov/collections/C1276812863-GES_DISC/granules/M2T1NXSLV.5.12.4%3AMERRA2_400.tavg1_2d_slv_Nx.20230101.nc4?dap4.ce=/T2M;/U2M;/V2M;/SLP;/lon;/time;/lat',\n",
-       " 'dap4://opendap.earthdata.nasa.gov/collections/C1276812863-GES_DISC/granules/M2T1NXSLV.5.12.4%3AMERRA2_400.tavg1_2d_slv_Nx.20230102.nc4?dap4.ce=/T2M;/U2M;/V2M;/SLP;/lon;/time;/lat']"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
+    "CE=\"?dap4.ce=\" + (';').join(Keep_vars) # need to add this to each url\n",
+    "\n",
     "opendap_urls = [url + CE for url in new_urls]\n",
     "opendap_urls[:2]"
    ]
@@ -274,7 +190,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "72b63320-37b3-417b-8694-ea975b014982",
    "metadata": {},
    "outputs": [],
@@ -284,68 +200,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "7d0296c5-0d1a-4af1-8e14-32d1e26acd0a",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "datacube has dimensions ['lat[0:1:360]', 'lon[0:1:575]'] , and concat dim: `time`\n",
-      "CPU times: user 577 ms, sys: 239 ms, total: 816 ms\n",
-      "Wall time: 22.8 s\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%time\n",
-    "consolidate_metadata(opendap_urls, concat_dim='time', session=my_session, set_maps=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "9e79d70e-6859-4a0c-a016-03da3dd80667",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "63"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(my_session.cache.urls())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "8b273280-1a45-4af1-ab91-5f25ba4496ce",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['https://opendap.earthdata.nasa.gov/collections/C1276812863-GES_DISC/granules/M2T1NXSLV.5.12.4%3AMERRA2_400.tavg1_2d_slv_Nx.20230101.nc4.dap?dap4.ce=lat%5B0%3A1%3A360%5D%3Blon%5B0%3A1%3A575%5D&dap4.checksum=true',\n",
-       " 'https://opendap.earthdata.nasa.gov/collections/C1276812863-GES_DISC/granules/M2T1NXSLV.5.12.4%3AMERRA2_400.tavg1_2d_slv_Nx.20230101.nc4.dap?dap4.ce=time%5B0%3A1%3A23%5D&dap4.checksum=true',\n",
-       " 'https://opendap.earthdata.nasa.gov/collections/C1276812863-GES_DISC/granules/M2T1NXSLV.5.12.4%3AMERRA2_400.tavg1_2d_slv_Nx.20230101.nc4.dmr?dap4.ce=%2FT2M%3B%2FU2M%3B%2FV2M%3B%2FSLP%3B%2Flon%3B%2Ftime%3B%2Flat',\n",
-       " 'https://opendap.earthdata.nasa.gov/collections/C1276812863-GES_DISC/granules/M2T1NXSLV.5.12.4%3AMERRA2_400.tavg1_2d_slv_Nx.20230102.nc4.dap?dap4.ce=time%5B0%3A1%3A23%5D&dap4.checksum=true']"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_session.cache.urls()[:4]"
+    "consolidate_metadata(opendap_urls, concat_dim='time', session=my_session)"
    ]
   },
   {
@@ -360,1005 +221,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "id": "6bddeee4-564d-4cea-ac12-74a0f7a5ea60",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 412 ms, sys: 68.1 ms, total: 480 ms\n",
-      "Wall time: 563 ms\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
-       "<defs>\n",
-       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
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-       "</defs>\n",
-       "</svg>\n",
-       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
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-       "    --jp-content-font-color0,\n",
-       "    var(--pst-color-text-base rgba(0, 0, 0, 1))\n",
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-       "  --xr-font-color2: var(\n",
-       "    --jp-content-font-color2,\n",
-       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.54))\n",
-       "  );\n",
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-       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))\n",
-       "  );\n",
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-       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\n",
-       "  );\n",
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-       "    --jp-layout-color3,\n",
-       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\n",
-       "  );\n",
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-       "    --jp-layout-color0,\n",
-       "    var(--pst-color-on-background, white)\n",
-       "  );\n",
-       "  --xr-background-color-row-even: var(\n",
-       "    --jp-layout-color1,\n",
-       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
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-       "    --jp-layout-color2,\n",
-       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
-       "  );\n",
-       "}\n",
-       "\n",
-       "html[theme=\"dark\"],\n",
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-       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
-       "  );\n",
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-       "    --jp-content-font-color2,\n",
-       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
-       "  );\n",
-       "  --xr-font-color3: var(\n",
-       "    --jp-content-font-color3,\n",
-       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
-       "  );\n",
-       "  --xr-border-color: var(\n",
-       "    --jp-border-color2,\n",
-       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
-       "  );\n",
-       "  --xr-disabled-color: var(\n",
-       "    --jp-layout-color3,\n",
-       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
-       "  );\n",
-       "  --xr-background-color: var(\n",
-       "    --jp-layout-color0,\n",
-       "    var(--pst-color-on-background, #111111)\n",
-       "  );\n",
-       "  --xr-background-color-row-even: var(\n",
-       "    --jp-layout-color1,\n",
-       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
-       "  );\n",
-       "  --xr-background-color-row-odd: var(\n",
-       "    --jp-layout-color2,\n",
-       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
-       "  );\n",
-       "}\n",
-       "\n",
-       ".xr-wrap {\n",
-       "  display: block !important;\n",
-       "  min-width: 300px;\n",
-       "  max-width: 700px;\n",
-       "}\n",
-       "\n",
-       ".xr-text-repr-fallback {\n",
-       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-header {\n",
-       "  padding-top: 6px;\n",
-       "  padding-bottom: 6px;\n",
-       "  margin-bottom: 4px;\n",
-       "  border-bottom: solid 1px var(--xr-border-color);\n",
-       "}\n",
-       "\n",
-       ".xr-header > div,\n",
-       ".xr-header > ul {\n",
-       "  display: inline;\n",
-       "  margin-top: 0;\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type,\n",
-       ".xr-array-name {\n",
-       "  margin-left: 2px;\n",
-       "  margin-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-sections {\n",
-       "  padding-left: 0 !important;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input {\n",
-       "  display: inline-block;\n",
-       "  opacity: 0;\n",
-       "  height: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input + label {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "  border: 2px solid transparent !important;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label {\n",
-       "  cursor: pointer;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:focus + label {\n",
-       "  border: 2px solid var(--xr-font-color0) !important;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
-       "  color: var(--xr-font-color0);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary {\n",
-       "  grid-column: 1;\n",
-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: \"►\";\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: \"▼\";\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: \"(\";\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: \")\";\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: \",\";\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  border-color: var(--xr-background-color-row-odd);\n",
-       "  margin-bottom: 0;\n",
-       "  padding-top: 2px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "  border-color: var(--xr-background-color-row-even);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-index-preview {\n",
-       "  grid-column: 2 / 5;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  display: none;\n",
-       "  border-top: 2px dotted var(--xr-background-color);\n",
-       "  padding-bottom: 20px !important;\n",
-       "  padding-top: 10px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in + label,\n",
-       ".xr-var-data-in + label,\n",
-       ".xr-index-data-in + label {\n",
-       "  padding: 0 1px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data,\n",
-       ".xr-index-data-in:checked ~ .xr-index-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > pre,\n",
-       ".xr-index-data > pre,\n",
-       ".xr-var-data > table > tbody > tr {\n",
-       "  background-color: transparent !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-index-name div,\n",
-       ".xr-index-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
-       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
-       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
-       "  color: var(--xr-font-color0);\n",
-       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
-       "  stroke-width: 0.8px;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 5GB\n",
-       "Dimensions:  (time: 744, lat: 361, lon: 576)\n",
-       "Coordinates:\n",
-       "  * lon      (lon) float64 5kB -180.0 -179.4 -178.8 -178.1 ... 178.1 178.8 179.4\n",
-       "  * time     (time) datetime64[ns] 6kB 2023-01-01T00:30:00 ... 2023-01-31T23:...\n",
-       "  * lat      (lat) float64 3kB -90.0 -89.5 -89.0 -88.5 ... 88.5 89.0 89.5 90.0\n",
-       "Data variables:\n",
-       "    T2M      (time, lat, lon) float64 1GB dask.array&lt;chunksize=(1, 361, 576), meta=np.ndarray&gt;\n",
-       "    U2M      (time, lat, lon) float64 1GB dask.array&lt;chunksize=(1, 361, 576), meta=np.ndarray&gt;\n",
-       "    SLP      (time, lat, lon) float64 1GB dask.array&lt;chunksize=(1, 361, 576), meta=np.ndarray&gt;\n",
-       "    V2M      (time, lat, lon) float64 1GB dask.array&lt;chunksize=(1, 361, 576), meta=np.ndarray&gt;\n",
-       "Attributes: (12/31)\n",
-       "    History:                           Original file generated: Wed Jan 11 21...\n",
-       "    Comment:                           GMAO filename: d5124_m2_jan10.tavg1_2d...\n",
-       "    Filename:                          MERRA2_400.tavg1_2d_slv_Nx.20230101.nc4\n",
-       "    Conventions:                       CF-1\n",
-       "    Institution:                       NASA Global Modeling and Assimilation ...\n",
-       "    References:                        http://gmao.gsfc.nasa.gov\n",
-       "    ...                                ...\n",
-       "    identifier_product_doi:            10.5067/VJAFPLI1CSIV\n",
-       "    RangeBeginningDate:                2023-01-01\n",
-       "    RangeBeginningTime:                00:00:00.000000\n",
-       "    RangeEndingDate:                   2023-01-01\n",
-       "    RangeEndingTime:                   23:59:59.000000\n",
-       "    created:                           2025-01-07T19:36:33Z</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-3118e65a-c21a-4c0d-9510-8f25c0649974' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3118e65a-c21a-4c0d-9510-8f25c0649974' 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>: 744</li><li><span class='xr-has-index'>lat</span>: 361</li><li><span class='xr-has-index'>lon</span>: 576</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b486518e-fb91-4703-b582-8fdaedd761af' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b486518e-fb91-4703-b582-8fdaedd761af' 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'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-180.0 -179.4 ... 178.8 179.4</div><input id='attrs-ce387768-8da2-4eab-bc2c-0fb579ff0884' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ce387768-8da2-4eab-bc2c-0fb579ff0884' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc0badf0-6a1a-48c4-8142-6b64e6255271' class='xr-var-data-in' type='checkbox'><label for='data-bc0badf0-6a1a-48c4-8142-6b64e6255271' 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>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>vmax :</span></dt><dd>999999987000000.0</dd><dt><span>vmin :</span></dt><dd>-999999987000000.0</dd><dt><span>valid_range :</span></dt><dd>[-999999987000000.0, 999999987000000.0]</dd><dt><span>Maps :</span></dt><dd>()</dd></dl></div><div class='xr-var-data'><pre>array([-180.   , -179.375, -178.75 , ...,  178.125,  178.75 ,  179.375],\n",
-       "      shape=(576,))</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'>2023-01-01T00:30:00 ... 2023-01-...</div><input id='attrs-88546e1f-9dfe-4983-ae3f-14a55365f836' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-88546e1f-9dfe-4983-ae3f-14a55365f836' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a49a8d60-bb5c-4efd-a060-37a371936511' class='xr-var-data-in' type='checkbox'><label for='data-a49a8d60-bb5c-4efd-a060-37a371936511' 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>time_increment :</span></dt><dd>10000</dd><dt><span>begin_date :</span></dt><dd>20230101</dd><dt><span>begin_time :</span></dt><dd>3000</dd><dt><span>vmax :</span></dt><dd>999999987000000.0</dd><dt><span>vmin :</span></dt><dd>-999999987000000.0</dd><dt><span>valid_range :</span></dt><dd>[-999999987000000.0, 999999987000000.0]</dd><dt><span>Maps :</span></dt><dd>()</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2023-01-01T00:30:00.000000000&#x27;, &#x27;2023-01-01T01:30:00.000000000&#x27;,\n",
-       "       &#x27;2023-01-01T02:30:00.000000000&#x27;, ..., &#x27;2023-01-31T21:30:00.000000000&#x27;,\n",
-       "       &#x27;2023-01-31T22:30:00.000000000&#x27;, &#x27;2023-01-31T23:30:00.000000000&#x27;],\n",
-       "      shape=(744,), dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><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'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -89.5 -89.0 ... 89.5 90.0</div><input id='attrs-6ea2dfb2-be24-4089-ab55-9b4a44d4d1fa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6ea2dfb2-be24-4089-ab55-9b4a44d4d1fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10762034-11ad-40a6-bf9d-3b0def1d97f7' class='xr-var-data-in' type='checkbox'><label for='data-10762034-11ad-40a6-bf9d-3b0def1d97f7' 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>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>vmax :</span></dt><dd>999999987000000.0</dd><dt><span>vmin :</span></dt><dd>-999999987000000.0</dd><dt><span>valid_range :</span></dt><dd>[-999999987000000.0, 999999987000000.0]</dd><dt><span>Maps :</span></dt><dd>()</dd></dl></div><div class='xr-var-data'><pre>array([-90. , -89.5, -89. , ...,  89. ,  89.5,  90. ], shape=(361,))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-04b9082a-ddb4-4758-adca-82fc044ff049' class='xr-section-summary-in' type='checkbox'  checked><label for='section-04b9082a-ddb4-4758-adca-82fc044ff049' class='xr-section-summary' >Data variables: <span>(4)</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>T2M</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 361, 576), meta=np.ndarray&gt;</div><input id='attrs-258420ee-604b-4723-b7e2-0314aa33d7d5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-258420ee-604b-4723-b7e2-0314aa33d7d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-388d7174-6cd1-4b0e-8778-566fc60b5273' class='xr-var-data-in' type='checkbox'><label for='data-388d7174-6cd1-4b0e-8778-566fc60b5273' 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>2-meter_air_temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>fmissing_value :</span></dt><dd>999999987000000.0</dd><dt><span>standard_name :</span></dt><dd>2-meter_air_temperature</dd><dt><span>vmax :</span></dt><dd>999999987000000.0</dd><dt><span>vmin :</span></dt><dd>-999999987000000.0</dd><dt><span>valid_range :</span></dt><dd>[-999999987000000.0, 999999987000000.0]</dd><dt><span>Maps :</span></dt><dd>(&#x27;/time&#x27;, &#x27;/lat&#x27;, &#x27;/lon&#x27;)</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> 1.15 GiB </td>\n",
-       "                        <td> 1.59 MiB </td>\n",
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-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (744, 361, 576) </td>\n",
-       "                        <td> (1, 361, 576) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 744 chunks in 63 graph layers </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>U2M</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 361, 576), meta=np.ndarray&gt;</div><input id='attrs-524950f1-2de8-4c96-8727-0d89ec411026' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-524950f1-2de8-4c96-8727-0d89ec411026' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4ca542e4-102d-4b72-a723-93cecd132325' class='xr-var-data-in' type='checkbox'><label for='data-4ca542e4-102d-4b72-a723-93cecd132325' 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>2-meter_eastward_wind</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>fmissing_value :</span></dt><dd>999999987000000.0</dd><dt><span>standard_name :</span></dt><dd>2-meter_eastward_wind</dd><dt><span>vmax :</span></dt><dd>999999987000000.0</dd><dt><span>vmin :</span></dt><dd>-999999987000000.0</dd><dt><span>valid_range :</span></dt><dd>[-999999987000000.0, 999999987000000.0]</dd><dt><span>Maps :</span></dt><dd>(&#x27;/time&#x27;, &#x27;/lat&#x27;, &#x27;/lon&#x27;)</dd></dl></div><div class='xr-var-data'><table>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SLP</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 361, 576), meta=np.ndarray&gt;</div><input id='attrs-b346cccf-ce47-4eb3-b073-8bff0d07f669' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b346cccf-ce47-4eb3-b073-8bff0d07f669' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d89858d2-e2d4-4883-9009-bb667600f125' class='xr-var-data-in' type='checkbox'><label for='data-d89858d2-e2d4-4883-9009-bb667600f125' 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>sea_level_pressure</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>fmissing_value :</span></dt><dd>999999987000000.0</dd><dt><span>standard_name :</span></dt><dd>sea_level_pressure</dd><dt><span>vmax :</span></dt><dd>999999987000000.0</dd><dt><span>vmin :</span></dt><dd>-999999987000000.0</dd><dt><span>valid_range :</span></dt><dd>[-999999987000000.0, 999999987000000.0]</dd><dt><span>Maps :</span></dt><dd>(&#x27;/time&#x27;, &#x27;/lat&#x27;, &#x27;/lon&#x27;)</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> 1.15 GiB </td>\n",
-       "                        <td> 1.59 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (744, 361, 576) </td>\n",
-       "                        <td> (1, 361, 576) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 744 chunks in 63 graph layers </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>V2M</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 361, 576), meta=np.ndarray&gt;</div><input id='attrs-0aeb5a09-5d35-42f3-a7af-8b795c7b3ef1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0aeb5a09-5d35-42f3-a7af-8b795c7b3ef1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0a32da66-1c73-419b-a0d3-4157e8f85703' class='xr-var-data-in' type='checkbox'><label for='data-0a32da66-1c73-419b-a0d3-4157e8f85703' 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>2-meter_northward_wind</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>fmissing_value :</span></dt><dd>999999987000000.0</dd><dt><span>standard_name :</span></dt><dd>2-meter_northward_wind</dd><dt><span>vmax :</span></dt><dd>999999987000000.0</dd><dt><span>vmin :</span></dt><dd>-999999987000000.0</dd><dt><span>valid_range :</span></dt><dd>[-999999987000000.0, 999999987000000.0]</dd><dt><span>Maps :</span></dt><dd>(&#x27;/time&#x27;, &#x27;/lat&#x27;, &#x27;/lon&#x27;)</dd></dl></div><div class='xr-var-data'><table>\n",
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-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 1.15 GiB </td>\n",
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-       "                    \n",
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-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-582ad173-ba40-4500-adc8-45444be91dcf' class='xr-section-summary-in' type='checkbox'  ><label for='section-582ad173-ba40-4500-adc8-45444be91dcf' 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>lon</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-e49c1133-42ff-41b4-861a-3f14394f9338' class='xr-index-data-in' type='checkbox'/><label for='index-e49c1133-42ff-41b4-861a-3f14394f9338' 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([  -180.0, -179.375,  -178.75, -178.125,   -177.5, -176.875,  -176.25,\n",
-       "       -175.625,   -175.0, -174.375,\n",
-       "       ...\n",
-       "         173.75,  174.375,    175.0,  175.625,   176.25,  176.875,    177.5,\n",
-       "        178.125,   178.75,  179.375],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=576))</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-dd45ad57-f724-443d-8f47-4a00ab973a20' class='xr-index-data-in' type='checkbox'/><label for='index-dd45ad57-f724-443d-8f47-4a00ab973a20' 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;2023-01-01 00:30:00&#x27;, &#x27;2023-01-01 01:30:00&#x27;,\n",
-       "               &#x27;2023-01-01 02:30:00&#x27;, &#x27;2023-01-01 03:30:00&#x27;,\n",
-       "               &#x27;2023-01-01 04:30:00&#x27;, &#x27;2023-01-01 05:30:00&#x27;,\n",
-       "               &#x27;2023-01-01 06:30:00&#x27;, &#x27;2023-01-01 07:30:00&#x27;,\n",
-       "               &#x27;2023-01-01 08:30:00&#x27;, &#x27;2023-01-01 09:30:00&#x27;,\n",
-       "               ...\n",
-       "               &#x27;2023-01-31 14:30:00&#x27;, &#x27;2023-01-31 15:30:00&#x27;,\n",
-       "               &#x27;2023-01-31 16:30:00&#x27;, &#x27;2023-01-31 17:30:00&#x27;,\n",
-       "               &#x27;2023-01-31 18:30:00&#x27;, &#x27;2023-01-31 19:30:00&#x27;,\n",
-       "               &#x27;2023-01-31 20:30:00&#x27;, &#x27;2023-01-31 21:30:00&#x27;,\n",
-       "               &#x27;2023-01-31 22:30:00&#x27;, &#x27;2023-01-31 23:30:00&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=744, freq=None))</pre></div></li><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-bb1e0c02-540f-4701-9f6f-fcbb7cad97e7' class='xr-index-data-in' type='checkbox'/><label for='index-bb1e0c02-540f-4701-9f6f-fcbb7cad97e7' 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, -89.5, -89.0, -88.5, -88.0, -87.5, -87.0, -86.5, -86.0, -85.5,\n",
-       "       ...\n",
-       "        85.5,  86.0,  86.5,  87.0,  87.5,  88.0,  88.5,  89.0,  89.5,  90.0],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=361))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7c0fd009-3b84-4f61-aabd-b7ed66d74630' class='xr-section-summary-in' type='checkbox'  ><label for='section-7c0fd009-3b84-4f61-aabd-b7ed66d74630' class='xr-section-summary' >Attributes: <span>(31)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>History :</span></dt><dd>Original file generated: Wed Jan 11 21:58:01 2023 GMT</dd><dt><span>Comment :</span></dt><dd>GMAO filename: d5124_m2_jan10.tavg1_2d_slv_Nx.20230101.nc4</dd><dt><span>Filename :</span></dt><dd>MERRA2_400.tavg1_2d_slv_Nx.20230101.nc4</dd><dt><span>Conventions :</span></dt><dd>CF-1</dd><dt><span>Institution :</span></dt><dd>NASA Global Modeling and Assimilation Office</dd><dt><span>References :</span></dt><dd>http://gmao.gsfc.nasa.gov</dd><dt><span>Format :</span></dt><dd>NetCDF-4/HDF-5</dd><dt><span>SpatialCoverage :</span></dt><dd>global</dd><dt><span>VersionID :</span></dt><dd>5.12.4</dd><dt><span>TemporalRange :</span></dt><dd>1980-01-01 -&gt; 2016-12-31</dd><dt><span>identifier_product_doi_authority :</span></dt><dd>http://dx.doi.org/</dd><dt><span>ShortName :</span></dt><dd>M2T1NXSLV</dd><dt><span>GranuleID :</span></dt><dd>MERRA2_400.tavg1_2d_slv_Nx.20230101.nc4</dd><dt><span>ProductionDateTime :</span></dt><dd>Original file generated: Wed Jan 11 21:58:01 2023 GMT</dd><dt><span>LongName :</span></dt><dd>MERRA2 tavg1_2d_slv_Nx: 2d,1-Hourly,Time-Averaged,Single-Level,Assimilation,Single-Level Diagnostics</dd><dt><span>Title :</span></dt><dd>MERRA2 tavg1_2d_slv_Nx: 2d,1-Hourly,Time-Averaged,Single-Level,Assimilation,Single-Level Diagnostics</dd><dt><span>SouthernmostLatitude :</span></dt><dd>-90.0</dd><dt><span>NorthernmostLatitude :</span></dt><dd>90.0</dd><dt><span>WesternmostLongitude :</span></dt><dd>-180.0</dd><dt><span>EasternmostLongitude :</span></dt><dd>179.375</dd><dt><span>LatitudeResolution :</span></dt><dd>0.5</dd><dt><span>LongitudeResolution :</span></dt><dd>0.625</dd><dt><span>DataResolution :</span></dt><dd>0.5 x 0.625</dd><dt><span>Source :</span></dt><dd>CVS tag: GEOSadas-5_12_4_p36_SLES12_M2-OPS</dd><dt><span>Contact :</span></dt><dd>http://gmao.gsfc.nasa.gov</dd><dt><span>identifier_product_doi :</span></dt><dd>10.5067/VJAFPLI1CSIV</dd><dt><span>RangeBeginningDate :</span></dt><dd>2023-01-01</dd><dt><span>RangeBeginningTime :</span></dt><dd>00:00:00.000000</dd><dt><span>RangeEndingDate :</span></dt><dd>2023-01-01</dd><dt><span>RangeEndingTime :</span></dt><dd>23:59:59.000000</dd><dt><span>created :</span></dt><dd>2025-01-07T19:36:33Z</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.Dataset> Size: 5GB\n",
-       "Dimensions:  (time: 744, lat: 361, lon: 576)\n",
-       "Coordinates:\n",
-       "  * lon      (lon) float64 5kB -180.0 -179.4 -178.8 -178.1 ... 178.1 178.8 179.4\n",
-       "  * time     (time) datetime64[ns] 6kB 2023-01-01T00:30:00 ... 2023-01-31T23:...\n",
-       "  * lat      (lat) float64 3kB -90.0 -89.5 -89.0 -88.5 ... 88.5 89.0 89.5 90.0\n",
-       "Data variables:\n",
-       "    T2M      (time, lat, lon) float64 1GB dask.array<chunksize=(1, 361, 576), meta=np.ndarray>\n",
-       "    U2M      (time, lat, lon) float64 1GB dask.array<chunksize=(1, 361, 576), meta=np.ndarray>\n",
-       "    SLP      (time, lat, lon) float64 1GB dask.array<chunksize=(1, 361, 576), meta=np.ndarray>\n",
-       "    V2M      (time, lat, lon) float64 1GB dask.array<chunksize=(1, 361, 576), meta=np.ndarray>\n",
-       "Attributes: (12/31)\n",
-       "    History:                           Original file generated: Wed Jan 11 21...\n",
-       "    Comment:                           GMAO filename: d5124_m2_jan10.tavg1_2d...\n",
-       "    Filename:                          MERRA2_400.tavg1_2d_slv_Nx.20230101.nc4\n",
-       "    Conventions:                       CF-1\n",
-       "    Institution:                       NASA Global Modeling and Assimilation ...\n",
-       "    References:                        http://gmao.gsfc.nasa.gov\n",
-       "    ...                                ...\n",
-       "    identifier_product_doi:            10.5067/VJAFPLI1CSIV\n",
-       "    RangeBeginningDate:                2023-01-01\n",
-       "    RangeBeginningTime:                00:00:00.000000\n",
-       "    RangeEndingDate:                   2023-01-01\n",
-       "    RangeEndingTime:                   23:59:59.000000\n",
-       "    created:                           2025-01-07T19:36:33Z"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%time\n",
-    "ds = xr.open_mfdataset(opendap_urls, engine='pydap', session=my_session, combine='nested', concat_dim=\"time\", chunks={\"time\":1})\n",
+    "ds = xr.open_mfdataset(\n",
+    "    opendap_urls, \n",
+    "    engine='pydap', \n",
+    "    session=my_session, \n",
+    "    combine='nested', \n",
+    "    concat_dim=\"time\", \n",
+    "    chunks={\"time\":1}\n",
+    ")\n",
     "ds"
    ]
   },
@@ -1374,29 +250,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "id": "a6f39ca9-89a6-422d-ae3d-ad32a3f89933",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 182 ms, sys: 30.7 ms, total: 213 ms\n",
-      "Wall time: 5.54 s\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%time\n",
     "fig, ax = plt.subplots(figsize=(12, 6))\n",
@@ -1464,7 +321,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.13"
+   "version": "3.12.11"
   }
  },
  "nbformat": 4,
diff --git a/binder/OSCAR.ipynb b/binder/OSCAR.ipynb
index 9fe2df9..bd681fa 100644
--- a/binder/OSCAR.ipynb
+++ b/binder/OSCAR.ipynb
@@ -38,7 +38,8 @@
     "import datetime as dt\n",
     "import numpy as np\n",
     "import json\n",
-    "import matplotlib.pyplot as plt"
+    "import matplotlib.pyplot as plt\n",
+    "import earthaccess"
    ]
   },
   {
@@ -46,11 +47,11 @@
    "id": "b766c90b-d699-4963-8c10-686b9b8cb6be",
    "metadata": {},
    "source": [
-    "<span style='font-family:serif'> <font size=\"5.5\"><span style='color:#0066cc'> **Import Token Authorization and create Session**\n",
+    "<span style='font-family:serif'> <font size=\"5.5\"><span style='color:#0066cc'> **Authenticate and create Session**\n",
     " \n",
     "\n",
     "\n",
-    "<font size=\"3.5\"> Here we use the Bearer Token to create an authenticated session. The Bearer token should be stored on a local json file, after completed the `GetStarted` Notebook.\n",
+    "<font size=\"3.5\"> Here we use the Bearer Token from our EDL account.\n",
     "\n"
    ]
   },
@@ -61,12 +62,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# load token json data\n",
-    "with open('token.json', 'r') as fp:\n",
-    "    token = json.load(fp)\n",
+    "auth = earthaccess.login(strategy=\"interactive\", persist=True) # you will be promted to add your EDL credentials\n",
     "\n",
     "# pass Token Authorization to a new Session.\n",
-    "my_session = create_session(use_cache=True, session_kwargs=token)\n",
+    "cache_kwargs={'cache_name':'data/OSCAR'}\n",
+    "my_session = create_session(use_cache=True, session=auth.get_session(), cache_kwargs=cache_kwargs)\n",
     "my_session.cache.clear()"
    ]
   },
@@ -81,67 +81,19 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5f18fa09-19c5-4f5d-9465-1f2150956a25",
+   "id": "4446cb20-bb1b-403f-ac7c-d63c27f67fcb",
    "metadata": {},
    "outputs": [],
    "source": [
-    "oscar_ccid = \"C2098858642-POCLOUD\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "77c0b18a-f7c0-409b-bc70-6a73c4592ac8",
-   "metadata": {},
-   "source": [
-    "<span style='font-family:serif'> <font size=\"5.5\"><span style='color:#0066cc'> **Filter data via Temporal Searches**\n",
+    "oscar_ccid = \"C2098858642-POCLOUD\"\n",
     "\n",
-    "<font size=\"3.5\"> Users can specify date ranges  NASA's CMR can \n",
+    "time_range=[dt.datetime(2020, 1, 1), dt.datetime(2020, 1, 31)] # One month of data\n",
     "\n",
-    "<font size=\"3.5\"> There are two ways to specify formats.\n",
+    "url_limits = 100 # controls the max number of urls returns. Default is 50\n",
     "\n",
-    "    1. Using `python`'s datetime package. It follows the `year-month-day` formatting\n",
-    "    2. A string with the following format: YYYY-MM-DDTHH:MM:SSZ\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4446cb20-bb1b-403f-ac7c-d63c27f67fcb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "time_range=[dt.datetime(2020, 1, 1), dt.datetime(2020, 1, 31)] # One month of data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "65d9242d-b7c3-4068-b38b-c1d9232cbfcd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "url_limits = 100 # controls the max number of urls returns. Default is 50"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "77302c47-af7d-4376-aba5-463f5547dfbb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
     "urls = get_cmr_urls(ccid=oscar_ccid,time_range=time_range, limit=url_limits) # you can incread the limit of results\n",
-    "len(urls)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b9b0af05-20ef-4028-a435-e13836606d57",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dap4_urls = [url.replace(\"https\", \"dap4\") for url in urls]"
+    "dap4_urls = [url.replace(\"https\", \"dap4\") for url in urls]\n",
+    "print(\"We found: \", len(dap4_urls), \" total Cloud OPeNDAP URLS associated with this collection!\")"
    ]
   },
   {
@@ -166,7 +118,7 @@
    "outputs": [],
    "source": [
     "%%time\n",
-    "consolidate_metadata(dap4_urls, concat_dim='time', safe_mode=False, set_maps=True, session=my_session)"
+    "consolidate_metadata(dap4_urls, concat_dim='time', set_maps=True, session=my_session)"
    ]
   },
   {
@@ -187,7 +139,13 @@
    "outputs": [],
    "source": [
     "%%time\n",
-    "ds = xr.open_mfdataset(dap4_urls, engine='pydap', session=my_session, combine='nested', concat_dim=\"time\")\n",
+    "ds = xr.open_mfdataset(\n",
+    "    dap4_urls, \n",
+    "    engine='pydap', \n",
+    "    session=my_session, \n",
+    "    combine='nested', \n",
+    "    concat_dim=\"time\",\n",
+    ")\n",
     "ds"
    ]
   },
@@ -433,7 +391,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.12"
+   "version": "3.12.11"
   }
  },
  "nbformat": 4,
diff --git a/binder/earthaccess.ipynb b/binder/earthaccess.ipynb
deleted file mode 100644
index 0d98fc0..0000000
--- a/binder/earthaccess.ipynb
+++ /dev/null
@@ -1,355 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "6e4a86e9-cd1d-445b-a7c9-89b2466414d9",
-   "metadata": {},
-   "source": [
-    "<span style='color:#009999'> <span style='font-family:serif'> <font size=\"15\"> **Using Earthaccess for access data via Hyrax's DMR++**<span style='color:#0066cc'> \n",
-    "\n",
-    "\n",
-    "\n",
-    "<span style='color:#ff6666'><font size=\"5\">**Requirements**\n",
-    "1. <font size=\"3\"><span style='color:Black'> An active EDL account.\n",
-    "\n",
-    "<font size=\"3\"><span style='color:Black'> `earthaccess` has their own way to authenticate that makes use of your EDL logging information.\n",
-    "\n",
-    " <span style='color:#ff6666'><font size=\"5\"> **OPeNDAP, DMR++ and VirtualiZarr**\n",
-    "\n",
-    "\n",
-    "<font size=\"3\"><span style='color:Black'> This notebook makes use of [earthacess](https://earthaccess.readthedocs.io/en/latest/), [VirtualiZarr](https://virtualizarr.readthedocs.io/en/latest/) and [xarray](https://docs.xarray.dev/en/stable/) to access NASA's cloud files currently on `S3`. [earthacess](https://earthaccess.readthedocs.io/en/latest/) has `built-in` support for accessing OPeNDAP in the Cloud's `DMR++` metadata directly, as opposed to OPeNDAP's Hyrax data server. DMR++ is then to Zarr metadata via [VirtualiZarr](https://virtualizarr.readthedocs.io/en/latest/), providing a huge performance boost for running both locally, or in a Cloud compute environment.\n",
-    "\n",
-    "<span style='color:#0066cc'><font size=\"3.5\"> **open_virtual_dataset**: \n",
-    "\n",
-    "[earthacess](https://earthaccess.readthedocs.io/en/latest/) allows data users to convert Hyrax's in the Cloud DMR++ metadata into cloud optimized reference files for the data stored in the cloud. THis is done via:\n",
-    "\n",
-    "- `earthaccess.open_virtual_dataset`\n",
-    "- `earthaccess.open_virtual_mfdataset`\n",
-    "\n",
-    "\n",
-    "<span style='color:#0066cc'><font size=\"3.5\"> **access=\"indirect\" vs access=\"direct\"**: \n",
-    "\n",
-    "\n",
-    "<font size=\"3\"><span style='color:Black'> This tutorial loads data over `https` (`access=\"indirect\"`). However, there is a **significant speed improvement** when using these functions in-cloud and enabling `access=\"direct\"`. This is the case when running this notebook over managed cloud JupyterHubs like [NASA VEDA](https://www.earthdata.nasa.gov/dashboard/) or [2i2c Openscapes](https://workshop.openscapes.2i2c.cloud/hub/login?next=%2Fhub%2F). This is because the data is streamed directly from cloud storage to cloud compute.\n",
-    "\n",
-    "<span style='color:#ff6666'><font size=\"5\"> **Objectives**\n",
-    " \n",
-    " \n",
-    "- <font size=\"3\"><span style='color:Black'> Demonstrate how to use [earthacess](https://earthaccess.readthedocs.io/en/latest/) to query datasets that are aviable via `OPeNDAP` in the Cloud.\n",
-    "- <font size=\"3\"><span style='color:Black'> Demonstrate the use of [earthacess](https://earthaccess.readthedocs.io/en/latest/) to create a virtually aggregated xarray data cube, making use of the Zarr metadata created from DMR++.\n",
-    "- <font size=\"3\"><span style='color:Black'> Demonstrate an advanced workflow for storing virtual reference as a Kerchunk object, for later use.\n",
-    "\n",
-    "\n",
-    "<span style='color:#0066cc'><font size=\"3.5\"> **WARNING**: \n",
-    "\n",
-    "<font size=\"3\"><span style='color:Black'> This feature is current experimental and may change in the future. This feature relies on `NASA` / `OPeNDAP` **DMR++** metadata files which may not always be present for your dataset and you may get a `FileNotFoundError`.\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "<span style='color:#0066cc'><font size=\"3.5\"> **Additional References**: \n",
-    "\n",
-    "\n",
-    "* This tutorial largely follows: [Cloud optimized access to NASA data with earthaccess and virtualizarr](https://earthaccess.readthedocs.io/en/latest/tutorials/dmrpp-virtualizarr/), available on [earthacess](https://earthaccess.readthedocs.io/en/latest/)'s documentation.\n",
-    "\n",
-    "* [Nag, Ayush, Gallagher, James. (August, 2024). VirtualiZarr and DMR++. Zenodo. https://doi.org/10.5281/zenodo.13176038](https://doi.org/10.5281/zenodo.13176038).\n",
-    "\n",
-    "* [Gallagher, James, Yang, Kent, Lee, Hyokyung. (November, 2024). High-Performance Access to Archival Data Stored in HDF4 and HDF5 on Cloud Object Stores Without Reformatting the Files. Zenodo. https://doi.org/10.5281/zenodo.14232491](https://doi.org/10.5281/zenodo.14232491)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "aac8388c-02aa-4026-93e8-05b3c5cb7c7c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import earthaccess\n",
-    "import xarray as xr"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1e556c85-3316-4cb2-9b37-ba9114d38e8f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(\"`earthaccess` version: \", earthaccess.__version__)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e157efb8-e38f-4bbf-a8a2-0754e2cd3821",
-   "metadata": {},
-   "source": [
-    "### Lets authenticate!\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e2ee4402-3ca4-44f1-9e33-ac91a7674ca4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "auth = earthaccess.login(strategy=\"interactive\", persist=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "39878aed-fb53-49d4-9ea0-ef3bea712f5d",
-   "metadata": {},
-   "source": [
-    "### NASA JPL Multiscale Ultrahigh Resolution (MUR) Sea Surface Temperature (SST) dataset - 0.01 degree resolution\n",
-    "\n",
-    "We now search for NASA JPL MUR SST data. For that we need\n",
-    "- temporal range\n",
-    "- Short Name of collection"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8d235f07-39b9-4736-8500-1c62ada34681",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results = earthaccess.search_data(\n",
-    "    temporal=(\"2010-01-01\", \"2010-01-31\"), short_name=\"MUR-JPL-L4-GLOB-v4.1\"\n",
-    ")\n",
-    "len(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "274c89e7-ea70-4210-b497-e64758511f4b",
-   "metadata": {},
-   "source": [
-    "### access DMR++ and create a virtual xarray object\n",
-    "we set:\n",
-    "- `access=\"indirect\"`: Running this notebook on binder or local machine.\n",
-    "- `access=\"direct\"`. Use this when runnnig this notebook on an EC2 instance to make the best use of DMR++, xarray, and DASK.\n",
-    "\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "46871b1b-575d-49a0-abac-8ecb296ad6de",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%time\n",
-    "mur = earthaccess.open_virtual_mfdataset(\n",
-    "    results,\n",
-    "    access=\"indirect\",\n",
-    "    load=True, # This means Dimensions are loaded into memory\n",
-    "    concat_dim=\"time\",\n",
-    "    coords=\"all\",\n",
-    "    compat=\"override\",\n",
-    "    combine_attrs=\"drop_conflicts\",\n",
-    ")\n",
-    "mur"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7007bc94-a167-490d-bd02-2e40aac9a4c8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(\"This created a virtual reference pointing to \", mur.nbytes/1e9, \"GBs of data on the cloud!\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "35120686-2211-45c6-a355-47e5d17df483",
-   "metadata": {},
-   "source": [
-    "## We now plot some data\n",
-    "\n",
-    "<font size=\"3\"><span style='color:Black'> This will actually trigger download / computation of the selected dataset\n",
-    "\n",
-    "<font size=\"3\"><span style='color:Black'> **NOTE**:\n",
-    "\n",
-    "* The dimensions are loaded into memory. We can manipulate them \n",
-    "* Dimensions are coordinates (not always the case). So we can subset by spatial lat/lon values!!\n",
-    "* We can also subset by time (time is a dimension)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "88f3f96b-7c08-4f8c-977c-f074c05cdc88",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%time\n",
-    "spatial_subset = mur.isel(time=0).sel(lat=slice(20, 45), lon=slice(-95, -50))\n",
-    "spatial_subset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1e549f1b-17d6-48c9-88a5-0636796c1fc2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%time\n",
-    "spatial_subset[\"analysed_sst\"].plot.pcolormesh(x=\"lon\", y=\"lat\", cmap=\"RdBu_r\", figsize=(8, 4));"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "40e1d34b-0c08-4d89-867b-a3089f3d6a16",
-   "metadata": {},
-   "source": [
-    "# A faster workflow:\n",
-    "\n",
-    "- Set `Load=False`\n",
-    "\n",
-    "<font size=\"3\"><span style='color:Black'> This creates a virtual reference with only Chunk Manifets.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8a511969-21c2-4748-a9f5-0d9fdd9706f7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%time\n",
-    "mur_vd = earthaccess.open_virtual_mfdataset(\n",
-    "    results,\n",
-    "    access=\"indirect\",\n",
-    "    load=False,\n",
-    "    concat_dim=\"time\",\n",
-    "    coords=\"all\",\n",
-    "    compat=\"override\",\n",
-    "    combine_attrs=\"drop_conflicts\",\n",
-    ")\n",
-    "mur_vd"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cde97235-d6a3-4df0-a480-c3402d8e6e7f",
-   "metadata": {},
-   "source": [
-    "## Example of what's inside this virtual dataset\n",
-    "\n",
-    "\n",
-    "- `earthaccess` parses OPeNDAP Hyrax's in the Cloud `DMR++`, extracting Chunk References\n",
-    "- Creates, using `VirtualiZarr`'s API, a virtual Zarray.\n",
-    "- Can then store the Kerchunk Reference"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d0b32a5f-3ea6-4ada-b54a-278273fe58d4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(mur_vd.analysed_sst.data.zarray)\n",
-    "print(\"\\n\")\n",
-    "print(mur_vd.analysed_sst.data.manifest.dict()[\"0.0.1\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dab24577-259b-47d0-8d02-ffa50911ca3d",
-   "metadata": {},
-   "source": [
-    "## Store as Kerchunk Json"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bb391681-951a-4831-81bf-e38148af5ae7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mur_vd.virtualize.to_kerchunk(filepath=\"mur_kerchunk.json\", format=\"json\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e7cb7a1d-6417-40cd-a217-9dc891157934",
-   "metadata": {},
-   "source": [
-    "## Load using xarray and Kerchunk as engine"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a4cf7050-325e-4cbf-8697-198c0cdcdee6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%time\n",
-    "fs = earthaccess.get_fsspec_https_session()\n",
-    "ds = xr.open_dataset(\n",
-    "    \"reference://\",\n",
-    "    engine=\"zarr\",\n",
-    "    chunks={},\n",
-    "    backend_kwargs={\n",
-    "        \"consolidated\": False,\n",
-    "        \"storage_options\": {\n",
-    "            \"fo\": \"mur_kerchunk.json\",\n",
-    "            \"remote_protocol\": fs.protocol,\n",
-    "            \"remote_options\": fs.storage_options,\n",
-    "        },\n",
-    "    },\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8f9e0237-a41f-4895-8017-6649b0c4bab7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ds"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "78da4d72-34d8-40da-b74b-111600954ff7",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "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": "3.11.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/binder/environment.yml b/binder/environment.yml
index 48ff512..2872220 100644
--- a/binder/environment.yml
+++ b/binder/environment.yml
@@ -4,12 +4,12 @@ channels:
 dependencies:
 - pip
 - numpy
-- python = 3.11
+- python = 3.12
 - netCDF4
 - matplotlib
 - jupyterlab
 - cartopy
-# - xarray
+- xarray
 - earthaccess
 - tqdm
 - dask
@@ -18,7 +18,6 @@ dependencies:
 - xoak
 - pip:
   - git+https://github.com/pydap/pydap.git
-  - git+https://github.com/Mikejmnez/xarray.git@pydap4_scale
   - jupyter-contrib-nbextensions
   - ipywidgets 
   - widgetsnbextension
diff --git a/binder/on-premOPeNDAP.ipynb b/binder/on-premOPeNDAP.ipynb
index 86fa6d9..6691665 100644
--- a/binder/on-premOPeNDAP.ipynb
+++ b/binder/on-premOPeNDAP.ipynb
@@ -11,29 +11,31 @@
     "\n",
     "\n",
     "<span style='color:#ff6666'><font size=\"5\">**Requirements**\n",
-    "1. <font size=\"3\"><span style='color:Black'> Have a Bearer Token for EarthData in the Cloud (See `GetStarted` Notebook).\n",
-    "2. <font size=\"3\"><span style='color:Black'> Upload the Bearer Token from local file `token.json`\n",
+    "1. <font size=\"3\"> Have a Bearer Token for EarthData in the Cloud (See `GetStarted` Notebook).\n",
+    "2. <font size=\"3\"> Upload the Bearer Token from local file `token.json`\n",
     "\n",
     "\n",
-    "<font size=\"3\"><span style='color:Black'> For completion, this notebook acessess data from PACE via OPeNDAP on-premisses server. The workflow is identical to accessing data on Hyrax in the Cloud.\n",
+    "<font size=\"3\"> For completion, this notebook acessess data from PACE via OPeNDAP on-premisses server. The workflow is identical to accessing data on Hyrax in the Cloud.\n",
     "\n",
     "\n",
     "<span style='color:#ff6666'><font size=\"5\"> **Objectives**\n",
     " \n",
     " \n",
-    "- <font size=\"3\"><span style='color:Black'> Demostrate how to use NASA's `Common Metadata Repository` ([CMR](https://cmr.earthdata.nasa.gov/search)) to find `OPeNDAP URLS` associated with a collection.\n",
-    "- <font size=\"3\"><span style='color:Black'> Demonstrate the use of `Constraint Expressions` to reduce metadata during Virtual Dataset creation\n",
-    "- <font size=\"3\"><span style='color:Black'> Use <span style='color:#ff6666'>**PyDAP**<span style='color:black'>'s `consolidate_metadata` to accelerate data cube creation via `xarray.open_mfdataset`.\n",
-    "- <font size=\"3\"><span style='color:Black'> Demonstrate an advanced workflow for remote data access and plotting of **Level 3** PACE data concerning surface `chlorophyll a`.\n",
+    "- <font size=\"3\"> Demostrate how to use NASA's `Common Metadata Repository` ([CMR](https://cmr.earthdata.nasa.gov/search)) to find `OPeNDAP URLS` associated with a collection.\n",
+    "- <font size=\"3\"> Demonstrate the use of `Constraint Expressions` to reduce metadata during Virtual Dataset creation\n",
+    "- <font size=\"3\"> Use <span style='color:#ff6666'>**PyDAP**<span style='color:black'>'s `consolidate_metadata` to accelerate data cube creation via `xarray.open_mfdataset`.\n",
+    "- <font size=\"3\"> Demonstrate an advanced workflow for remote data access and plotting of **Level 3** PACE data concerning surface `chlorophyll a`.\n",
     "\n",
     "\n",
     "<span style='color:#ff6666'><font size=\"5\">**Browsing Data**:\n",
     "\n",
-    "<font size=\"3\"><span style='color:Black'> We are interested in PACE OCI data with **doi**: `10.5067/PACE/OCI/L3M/CHL/3.0`.\n",
+    "<font size=\"3\"> We are interested in PACE OCI data with **doi**: `10.5067/PACE/OCI/L3M/CHL/3.0`.\n",
     "\n",
-    "<font size=\"3\"><span style='color:Black'> The **doi** can be found using Earthdata search.\n",
+    "<font size=\"3\"> The **doi** can be found using Earthdata search.\n",
     "\n",
-    "<font size=\"3\"><span style='color:Black'> For more information about PACE, head to https://pace.oceansciences.org/ "
+    "<font size=\"3\"> For more information about PACE, head to https://pace.oceansciences.org/ \n",
+    "\n",
+    "`Author`: Miguel Jimenez-Urias, '25"
    ]
   },
   {
@@ -43,18 +45,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import requests\n",
-    "from pydap.client import open_url\n",
+    "from pydap.client import open_url, consolidate_metadata, get_cmr_urls\n",
     "from pydap.net import create_session\n",
-    "import json\n",
     "import cartopy.crs as ccrs\n",
     "import xarray as xr\n",
     "import datetime as dt\n",
-    "from pydap.client import consolidate_metadata\n",
     "import pydap\n",
-    "import requests_cache"
+    "import earthaccess\n",
+    "import matplotlib.pyplot as plt"
    ]
   },
   {
@@ -64,9 +64,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "print(\"pydap version: \", pydap.__version__)\n",
-    "print(\"requests cache version: \", requests_cache.__version__)\n",
-    "print(\"requests version: \", requests.__version__)"
+    "print(\"pydap version: \", pydap.__version__)"
    ]
   },
   {
@@ -76,14 +74,14 @@
    "source": [
     "<span style='color:#ff6666'><font size=\"5\">**Finding Cloud OPeNDAP URLs with NASA's CMR**:\n",
     "\n",
-    "<span style='font-family:serif'> <font size=\"3\"><span style='color:Black'> Below we illustrate how to find OPeNDAP URLs via the **CMR**\n",
+    "<span style='font-family:serif'> <font size=\"3\">Below we illustrate how to find OPeNDAP URLs via the **CMR**\n",
     "\n",
     "<span style='color:#0066cc'><font size=\"3.5\"> **To find (on-prem) OPeNDAP URLs you will need:**\n",
     "\n",
     "* One of `Collection Concept ID` or `dataset DOI`\n",
     "* Time Range\n",
     "\n",
-    "<span style='font-family:serif'> <font size=\"3\"><span style='color:Black'>  On-prem OPeNDAP URLs look distinct to cloud OPeNDAP URLs. However, the workflow for finding OPeNDAP URLs and accessing OPeNDAP-served data remains identical. \n",
+    "<span style='font-family:serif'> <font size=\"3\"> On-prem OPeNDAP URLs look distinct to cloud OPeNDAP URLs. However, the workflow for finding OPeNDAP URLs and accessing OPeNDAP-served data remains identical. \n",
     "\n",
     "\n"
    ]
@@ -95,7 +93,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "session = requests.Session()"
+    "auth = earthaccess.login(strategy=\"interactive\", persist=True)\n",
+    "session = create_session(use_cache=True, session=earthaccess.get_requests_https_session(), cache_kwargs={'cache_name': 'data/PACE'})\n",
+    "session.cache.clear()"
    ]
   },
   {
@@ -105,265 +105,313 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# CMR API base url\n",
-    "cmrurl='https://cmr.earthdata.nasa.gov/search/'\n",
-    "doi = \"10.5067/PACE/OCI/L3M/CHL/3.0\"\n",
-    "doisearch = cmrurl + 'collections.json?doi=' + doi\n",
-    "print(doisearch)\n",
-    "\n",
-    "concept_id = session.get(doisearch).json()['feed']['entry'][0]['id']\n",
-    "print(concept_id)"
+    "# Version 3.1 of Chlorophyll data\n",
+    "PACE_ccid = \"C3620140255-OB_CLOUD\"\n",
+    "\n",
+    "## Lets look for a year of data\n",
+    "time_range = [dt.datetime(2025, 1, 1), dt.datetime(2025, 7, 31)]\n",
+    "\n",
+    "granules_urls = get_cmr_urls(ccid=PACE_ccid, time_range=time_range)\n",
+    "\n",
+    "print(\"We found: \", len(granules_urls), \" total Non-Cloud OPeNDAP URLS associated with this collection!\")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "7f4ba5c9-7665-4d75-81f5-de4862b87ee6",
+   "id": "0ed35bf4-d33e-464f-8366-dad5f1b88f45",
    "metadata": {},
    "source": [
-    "<span style='font-family:serif'> <font size=\"5.5\"><span style='color:#0066cc'> **Specify time range**\n",
+    "<span style='color:#0066cc'><font size=\"3.5\"> **Further Filter via OPeNDAP Parameters:**\n",
     "\n",
-    "<font size=\"3\"><span style='color:Black'> This dataset covers `March 2024` to present day. \n"
+    "* We want to specify the `DAP4`.\n",
+    "* 4km daily data\n",
+    "* We are only interested in chlo_a variable, and its dimensions (coordinates)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c3de7046-f8f0-4dbd-93f5-f75609b99d7a",
+   "id": "ed8b2b05-64f9-4356-beab-41074fb8a0b2",
    "metadata": {},
    "outputs": [],
    "source": [
-    "start_date =  dt.datetime(2024, 4, 1) \n",
-    "end_date = dt.datetime(2024, 12, 31)\n",
-    "\n",
-    "print(start_date, end_date,sep='\\n')\n",
+    "## Build a constraint expression in DAP4\n",
+    "CEs = \"?dap4.ce=/lat;/lon;/chlor_a\"\n",
     "\n",
-    "dt_format = '%Y-%m-%dT%H:%M:%SZ' # format requirement for datetime search\n",
-    "temporal_str = start_date.strftime(dt_format) + ',' + end_date.strftime(dt_format)\n",
-    "print(temporal_str)"
+    "# Filter the URLs for 4km and DAY in the URL string, and specify DAP4 in the url by replacing http --> dap4\n",
+    "new_urls = [url.replace(\"https\", \"dap4\") + CEs for url in granules_urls if '4km' in url and \"DAY\" in url]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eaafaa7a-9417-43c9-9231-d5e4dc5c1fa3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_urls[:4]"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "b2680c99-1374-41ce-8d79-d2576fce0449",
+   "id": "99f870de-f894-4aa6-a9a3-1061c50d59d6",
    "metadata": {},
    "source": [
-    "<span style='font-family:serif'> <font size=\"5.5\"><span style='color:#0066cc'> **Get all available OPeNDAP URLs via CMR**\n",
+    "## Consolidate all URL Metadata Associated with the Data URL of cloud OPeNDAP URLs\n",
     "\n",
-    "The cell below will search/find all OPeNDAP URLs associated with the Collection concept ID.\n",
+    "<span style='color:#ff6666'>**PyDAP**</span> allows to construct a (cached) reference to all Cloud OPeNDAP urls, and can persist through sessions. Meaning, these Cloud OPenDAP URLS can be stored in your machine\n",
+    "for later use!!!! \n",
     "\n",
-    "The results wll be stored in the variable `granules_urls`.\n",
-    "    "
+    "\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6ce8d678-3f0f-4616-a473-0b682ddf9906",
+   "id": "20e088a5-c314-410e-854f-84e0872f983d",
    "metadata": {},
    "outputs": [],
    "source": [
-    "def get_opendap_urls(concept_id, time_range, _session=None):\n",
-    "    \"\"\"\n",
-    "    Queries NASA's `Common Metadata Repository` to identify all OPeNDAP URLS\n",
-    "    given collection concept ID and temporal time range.\n",
-    "    \"\"\"\n",
-    "    cmr_url = 'https://cmr.earthdata.nasa.gov/search/granules'\n",
-    "    if not _session:\n",
-    "        _session = requests.Session() \n",
-    "    cmr_response = _session.get(cmr_url, params={'concept_id': concept_id,'temporal': time_range,'page_size': 500}, headers={'Accept': 'application/json'})\n",
-    "    granules = cmr_response.json()['feed']['entry']\n",
-    "    granules_urls = []\n",
-    "    \n",
-    "    # Filter and only retain the OPeNDAP URLs\n",
-    "    for granule in granules:\n",
-    "        item = next((item['href'] for item in granule['links'] if \"opendap\" in item[\"href\"]), None)\n",
-    "        if item != None:\n",
-    "            granules_urls.append(item)\n",
-    "    return granules_urls"
+    "%%time\n",
+    "consolidate_metadata(new_urls, session=session)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a406ae4b-64ce-48a1-916c-03ba18fbc8b6",
+   "metadata": {},
+   "source": [
+    "## Create a datacube with xarray and pydap as an engine!\n",
+    "\n",
+    "\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "737eae5a-4263-4f7a-b4f4-6931c01d4812",
+   "id": "74f72bc7-7612-4552-a5ff-61bead9b9088",
    "metadata": {},
    "outputs": [],
    "source": [
     "%%time\n",
-    "granules_urls = get_opendap_urls(concept_id, temporal_str)"
+    "ds = xr.open_mfdataset(\n",
+    "    new_urls, \n",
+    "    engine='pydap', \n",
+    "    session=session, \n",
+    "    parallel=True, \n",
+    "    combine='nested', \n",
+    "    concat_dim='time')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ed8b2b05-64f9-4356-beab-41074fb8a0b2",
+   "id": "6d318916-8819-46fb-9cd8-443bb3ba2e14",
    "metadata": {},
    "outputs": [],
    "source": [
-    "print(\"We found: \", len(granules_urls), \" total Non-Cloud OPeNDAP URLS associated with this collection! However not all these belong to the same datacube. WE need to further filter these\")"
+    "ds"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "eaafaa7a-9417-43c9-9231-d5e4dc5c1fa3",
+   "id": "4dc79946-3c5e-421b-84f7-029188eeaa7b",
    "metadata": {},
    "outputs": [],
    "source": [
-    "granules_urls[:10]"
+    "chlor_a = ds['chlor_a'].isel(time=-1)\n",
+    "chlor_a"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "889e38ce-d5f7-41ce-ac43-2b232a78e166",
+   "id": "acb433a6-23c9-4527-bf54-0df1122c0f90",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "new_urls = [url.replace(\"https\", \"dap4\") for url in granules_urls if '4km' in url and \"DAY\" in url]\n",
-    "print(\"Of the 500 OPeNDAP URLs in the Collection, only \", len(new_urls), \" are associated with the correct data cube. \")"
-   ]
+   "source": []
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "dcea9ae6-29c6-4530-9780-c27b0071975d",
+   "id": "55743fc2-8b50-40cd-b0f7-f6c44db55f64",
    "metadata": {},
    "outputs": [],
    "source": [
-    "new_urls[:10]"
+    "%%time\n",
+    "plt.figure(figsize=(25, 8))\n",
+    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
+    "ax.set_global()\n",
+    "ax.coastlines()\n",
+    "plt.contourf(ds.lon, ds.lat, np.log(chlor_a), 400, cmap='nipy_spectral')\n",
+    "plt.colorbar().set_label(chlor_a.attrs['long_name'] + ' ['+chlor_a.attrs['units']+']')\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "26f37f4e-93e1-45eb-971e-cebf873d1dfd",
+   "id": "ee3ec205-f09d-4d39-893e-9a193603f58c",
    "metadata": {},
    "source": [
-    "### Recover locally stored token for authentication"
+    "\n",
+    "### Identify spatial subset\n",
+    "\n",
+    "In this case, we are interested in a spatial subset. The data is Level 3 data (gridded) so latitude and longitude are uniform. Moreover, these are 1D, and have already been downloaded into memory!"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e003ecd3-f059-4fb2-a71c-e48d603fb073",
+   "id": "f256e1ab-e6fc-4bc6-960c-f3c56863bde6",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# load token json data\n",
-    "with open('token.json', 'r') as fp:\n",
-    "    token = json.load(fp)\n",
-    "\n",
-    "# pass Token Authorization to a new Session.\n",
-    "my_session = create_session(use_cache=True, session_kwargs=token)\n",
-    "# clear just in case\n",
-    "my_session.cache.clear()"
+    "lat, lon = ds['lat'].values, ds['lon'].values \n",
+    "\n",
+    "# Min/max of lon values\n",
+    "minLon, maxLon = -96, 10\n",
+    "\n",
+    "# Min/Max of lat values\n",
+    "minLat, maxLat = 6, 70\n",
+    "\n",
+    "# Find indexes where we want to retain data.\n",
+    "iLon = np.where((lon>minLon)&(lon < maxLon))[0]\n",
+    "iLat= np.where((lat>minLat)&(lat < maxLat))[0]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3de93aa1-456e-42f4-8fec-1e61aa4a3706",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iLon[0], iLon[-1], iLat[0], iLat[-1]"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "99f870de-f894-4aa6-a9a3-1061c50d59d6",
+   "id": "4a872144-a0e6-490a-beaa-bbb87b58f792",
    "metadata": {},
    "source": [
-    "## Consolidate all URL Metadata Associated with the Data URL of cloud OPeNDAP URLs\n",
     "\n",
-    "<span style='color:#ff6666'>**PyDAP**<span style='color:black'> allows to construct a (cached) reference to all Cloud OPeNDAP urls, and can persist through sessions. Meaning, these Cloud OPenDAP URLS can be stored in your machine\n",
-    "for later use!!!! \n",
+    "### Re-open dataset and chunk to match slices\n",
     "\n",
-    "\n"
+    "This is the approach, when opening multiple remote files, to pass the slice to the server so subsetting is done proximate to the data"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "20e088a5-c314-410e-854f-84e0872f983d",
+   "id": "8218d137-380c-40c4-9ae2-23d7a7ab4dfe",
    "metadata": {},
    "outputs": [],
    "source": [
     "%%time\n",
-    "consolidate_metadata(new_urls, my_session)"
+    "ds = xr.open_mfdataset(\n",
+    "    new_urls, \n",
+    "    engine='pydap', \n",
+    "    session=session, \n",
+    "    parallel=True, \n",
+    "    concat_dim='time',  # <------ a time dimension will be created \n",
+    "    combine='nested',\n",
+    "    chunks = {'lon': len(iLon), 'lat':len(iLat)} #  <----------- This instructs the OPeNDAP server to subset in space\n",
+    ")\n",
+    "ds"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "dcc846e9-3b23-4924-b3ad-dd8476b8b9e5",
+   "id": "38e1b99b-e5b5-47fe-bc42-4d0482d6bf52",
    "metadata": {},
    "outputs": [],
    "source": [
-    "my_session.cache.urls()[:10]"
+    "ds[\"chlor_a\"] ## inspect the chunk of the data"
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "a406ae4b-64ce-48a1-916c-03ba18fbc8b6",
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f6b384e6-18cb-4285-acd7-477968b59d9e",
    "metadata": {},
+   "outputs": [],
    "source": [
-    "## Create a datacube with xarray and pydap as an engine!\n",
-    "\n",
-    "\n"
+    "nds = ds.isel(lon=slice(iLon[0], iLon[-1]+1), lat=slice(iLat[0], iLat[-1]+1))"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "74f72bc7-7612-4552-a5ff-61bead9b9088",
+   "id": "64184778-5bd9-490a-9927-6cb4907c6341",
    "metadata": {},
    "outputs": [],
    "source": [
-    "%%time\n",
-    "ds = xr.open_mfdataset(new_urls, engine='pydap', session=my_session, parallel=True, combine='nested', concat_dim='time')"
+    "nds['chlor_a']"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6d318916-8819-46fb-9cd8-443bb3ba2e14",
+   "id": "64430ebc-74e0-44ea-8379-bbc16e406ede",
    "metadata": {},
    "outputs": [],
    "source": [
-    "ds"
+    "%%time\n",
+    "nds.to_netcdf(\"data/pace_subset.nc4\", mode='w')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "19c4cec1-6979-44ac-8c67-b89f2d61ebe5",
+   "metadata": {},
+   "source": [
+    "## Finally inspect data"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "4dc79946-3c5e-421b-84f7-029188eeaa7b",
+   "id": "6210c848-483b-41c3-8a50-0c1a7a9779fe",
    "metadata": {},
    "outputs": [],
    "source": [
-    "chlor_a = ds['chlor_a'].isel(time=0)\n",
-    "chlor_a"
+    "mds = xr.open_dataset(\"data/pace_subset.nc4\", chunks={\"time\":1}) # use default engine for NetCDF4 \n",
+    "mds"
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "088e4df4-ae80-43e1-a8b4-6f26b62019d0",
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "49b6c507-da19-4ca8-b2f7-69b5d1b5d51e",
    "metadata": {},
+   "outputs": [],
    "source": [
-    "## Lets visualize some data\n"
+    "chlor_a_sub = mds['chlor_a']\n",
+    "chlor_a_sub"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "55743fc2-8b50-40cd-b0f7-f6c44db55f64",
+   "id": "50afd389-88a7-4264-8d39-05e6824f4f26",
    "metadata": {},
    "outputs": [],
    "source": [
     "%%time\n",
     "plt.figure(figsize=(25, 8))\n",
     "ax = plt.axes(projection=ccrs.PlateCarree())\n",
-    "ax.set_global()\n",
     "ax.coastlines()\n",
-    "plt.contourf(ds.lon, ds.lat, np.log(chlor_a), 400, cmap='nipy_spectral')\n",
-    "plt.colorbar().set_label(chlor_a.attrs['long_name'] + ' ['+chlor_a.attrs['units']+']')\n",
+    "plt.contourf(mds.lon, mds.lat, np.log(chlor_a_sub.isel(time=-1)), 400, cmap='nipy_spectral')\n",
+    "plt.colorbar().set_label(chlor_a_sub.attrs['long_name'] + ' ['+chlor_a_sub.attrs['units']+']')\n",
     "plt.show()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6375e132-770d-4318-800f-46c0fd933527",
+   "id": "447aef93-85b8-40be-bade-6e6eed7122c2",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -385,7 +433,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.12"
+   "version": "3.12.11"
   }
  },
  "nbformat": 4,