cartopy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ec5b7679",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cartopy.crs as ccrs\n",
    "import matplotlib.pyplot as plt\n",
    "import xarray as xr\n",
    "import cartopy.feature as cf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "adc8ec20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<cartopy.mpl.contour.GeoContourSet at 0x297acca5e50>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8,5))\n",
    "ax = plt.subplot(1,1,1,projection= ccrs.PlateCarree())\n",
    "file=xr.open_dataset('sst.mnmean.nc')\n",
    "\n",
    "#ax.stock_img()\n",
    "ax.add_feature(cf.COASTLINE)\n",
    "ax.add_feature(cf.BORDERS)\n",
    "ax.add_feature(cf.STATES)\n",
    "ax.add_feature(cf.LAND)\n",
    "ax.add_feature(cf.LAKES)\n",
    "ax.add_feature(cf.OCEAN)\n",
    "ax.set_extent([-3.5,2,4,12],crs=ccrs.PlateCarree())\n",
    "axis=ax.gridlines(draw_labels=True,lw=1.5,color='grey',alpha=1,ls='-')\n",
    "ax.set_title('GHANA')\n",
    "ax.contourf(file.lon,file.lat,file.sst[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "22309a65",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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",
       "  margin-bottom: 0;\n",
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       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
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       "\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",
       "}\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-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",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-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",
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       "\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",
       "  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",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (lat: 89, lon: 180, time: 128)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
       "  * time     (time) datetime64[ns] 2010-01-01 2010-02-01 ... 2020-08-01\n",
       "Data variables:\n",
       "    sst      (time, lat, lon) float32 ...\n",
       "Attributes: (12/37)\n",
       "    climatology:               Climatology is based on 1971-2000 SST, Xue, Y....\n",
       "    description:               In situ data: ICOADS2.5 before 2007 and NCEP i...\n",
       "    keywords_vocabulary:       NASA Global Change Master Directory (GCMD) Sci...\n",
       "    keywords:                  Earth Science &gt; Oceans &gt; Ocean Temperature &gt; S...\n",
       "    instrument:                Conventional thermometers\n",
       "    source_comment:            SSTs were observed by conventional thermometer...\n",
       "    ...                        ...\n",
       "    creator_url_original:      https://www.ncei.noaa.gov\n",
       "    license:                   No constraints on data access or use\n",
       "    comment:                   SSTs were observed by conventional thermometer...\n",
       "    summary:                   ERSST.v5 is developed based on v4 after revisi...\n",
       "    dataset_title:             NOAA Extended Reconstructed SST V5\n",
       "    data_modified:             2020-09-07</pre><div class='xr-wrap' hidden><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-a462443d-5b80-437c-bce5-e9c3ab3aa44c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a462443d-5b80-437c-bce5-e9c3ab3aa44c' 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'>lat</span>: 89</li><li><span class='xr-has-index'>lon</span>: 180</li><li><span class='xr-has-index'>time</span>: 128</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-49571c8d-486f-4b86-a8e7-788f469386e5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-49571c8d-486f-4b86-a8e7-788f469386e5' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>88.0 86.0 84.0 ... -86.0 -88.0</div><input id='attrs-cceb909b-9109-469a-945a-5ac5ead4d6e8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cceb909b-9109-469a-945a-5ac5ead4d6e8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b4894955-d6d4-4cd5-b3ce-cdb8e6a20bea' class='xr-var-data-in' type='checkbox'><label for='data-b4894955-d6d4-4cd5-b3ce-cdb8e6a20bea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>actual_range :</span></dt><dd>[ 88. -88.]</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([ 88.,  86.,  84.,  82.,  80.,  78.,  76.,  74.,  72.,  70.,  68.,  66.,\n",
       "        64.,  62.,  60.,  58.,  56.,  54.,  52.,  50.,  48.,  46.,  44.,  42.,\n",
       "        40.,  38.,  36.,  34.,  32.,  30.,  28.,  26.,  24.,  22.,  20.,  18.,\n",
       "        16.,  14.,  12.,  10.,   8.,   6.,   4.,   2.,   0.,  -2.,  -4.,  -6.,\n",
       "        -8., -10., -12., -14., -16., -18., -20., -22., -24., -26., -28., -30.,\n",
       "       -32., -34., -36., -38., -40., -42., -44., -46., -48., -50., -52., -54.,\n",
       "       -56., -58., -60., -62., -64., -66., -68., -70., -72., -74., -76., -78.,\n",
       "       -80., -82., -84., -86., -88.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 2.0 4.0 ... 354.0 356.0 358.0</div><input id='attrs-d3b7ce05-f923-4634-91bc-872a89eaf428' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d3b7ce05-f923-4634-91bc-872a89eaf428' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6288f242-a0dc-41e0-beb8-ecff65dcd5b6' class='xr-var-data-in' type='checkbox'><label for='data-6288f242-a0dc-41e0-beb8-ecff65dcd5b6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>actual_range :</span></dt><dd>[  0. 358.]</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.,  20.,  22.,\n",
       "        24.,  26.,  28.,  30.,  32.,  34.,  36.,  38.,  40.,  42.,  44.,  46.,\n",
       "        48.,  50.,  52.,  54.,  56.,  58.,  60.,  62.,  64.,  66.,  68.,  70.,\n",
       "        72.,  74.,  76.,  78.,  80.,  82.,  84.,  86.,  88.,  90.,  92.,  94.,\n",
       "        96.,  98., 100., 102., 104., 106., 108., 110., 112., 114., 116., 118.,\n",
       "       120., 122., 124., 126., 128., 130., 132., 134., 136., 138., 140., 142.,\n",
       "       144., 146., 148., 150., 152., 154., 156., 158., 160., 162., 164., 166.,\n",
       "       168., 170., 172., 174., 176., 178., 180., 182., 184., 186., 188., 190.,\n",
       "       192., 194., 196., 198., 200., 202., 204., 206., 208., 210., 212., 214.,\n",
       "       216., 218., 220., 222., 224., 226., 228., 230., 232., 234., 236., 238.,\n",
       "       240., 242., 244., 246., 248., 250., 252., 254., 256., 258., 260., 262.,\n",
       "       264., 266., 268., 270., 272., 274., 276., 278., 280., 282., 284., 286.,\n",
       "       288., 290., 292., 294., 296., 298., 300., 302., 304., 306., 308., 310.,\n",
       "       312., 314., 316., 318., 320., 322., 324., 326., 328., 330., 332., 334.,\n",
       "       336., 338., 340., 342., 344., 346., 348., 350., 352., 354., 356., 358.],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2010-01-01 ... 2020-08-01</div><input id='attrs-1e07f63c-b6c0-4ae0-9c7a-710aea274e6b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1e07f63c-b6c0-4ae0-9c7a-710aea274e6b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e7f24f9f-e6ac-413c-82de-bfbb16c37b15' class='xr-var-data-in' type='checkbox'><label for='data-e7f24f9f-e6ac-413c-82de-bfbb16c37b15' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>delta_t :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>avg_period :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>prev_avg_period :</span></dt><dd>0000-00-07 00:00:00</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>actual_range :</span></dt><dd>[19723. 80566.]</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2010-01-01T00:00:00.000000000&#x27;, &#x27;2010-02-01T00:00:00.000000000&#x27;,\n",
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       "       &#x27;2014-03-01T00:00:00.000000000&#x27;, &#x27;2014-04-01T00:00:00.000000000&#x27;,\n",
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       "       &#x27;2014-09-01T00:00:00.000000000&#x27;, &#x27;2014-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2014-11-01T00:00:00.000000000&#x27;, &#x27;2014-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2015-01-01T00:00:00.000000000&#x27;, &#x27;2015-02-01T00:00:00.000000000&#x27;,\n",
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       "       &#x27;2017-11-01T00:00:00.000000000&#x27;, &#x27;2017-12-01T00:00:00.000000000&#x27;,\n",
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       "       &#x27;2019-03-01T00:00:00.000000000&#x27;, &#x27;2019-04-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-05-01T00:00:00.000000000&#x27;, &#x27;2019-06-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-07-01T00:00:00.000000000&#x27;, &#x27;2019-08-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-09-01T00:00:00.000000000&#x27;, &#x27;2019-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-11-01T00:00:00.000000000&#x27;, &#x27;2019-12-01T00:00:00.000000000&#x27;,\n",
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       "       &#x27;2020-03-01T00:00:00.000000000&#x27;, &#x27;2020-04-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-05-01T00:00:00.000000000&#x27;, &#x27;2020-06-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-07-01T00:00:00.000000000&#x27;, &#x27;2020-08-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-11f1904e-5c88-495a-8a51-0dca047e40e4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-11f1904e-5c88-495a-8a51-0dca047e40e4' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>sst</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-25ada9e1-323b-4c57-84ee-69682411f0f7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-25ada9e1-323b-4c57-84ee-69682411f0f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-90b77331-1d32-4f9a-bdb4-2e129653b79b' class='xr-var-data-in' type='checkbox'><label for='data-90b77331-1d32-4f9a-bdb4-2e129653b79b' 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>Monthly Means of Sea Surface Temperature</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>var_desc :</span></dt><dd>Sea Surface Temperature</dd><dt><span>level_desc :</span></dt><dd>Surface</dd><dt><span>statistic :</span></dt><dd>Mean</dd><dt><span>dataset :</span></dt><dd>NOAA Extended Reconstructed SST V5</dd><dt><span>parent_stat :</span></dt><dd>Individual Values</dd><dt><span>actual_range :</span></dt><dd>[-1.8     42.32636]</dd><dt><span>valid_range :</span></dt><dd>[-1.8 45. ]</dd></dl></div><div class='xr-var-data'><pre>[2050560 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-98b4c311-3a14-4996-914e-3a879ad9de65' class='xr-section-summary-in' type='checkbox'  ><label for='section-98b4c311-3a14-4996-914e-3a879ad9de65' class='xr-section-summary' >Attributes: <span>(37)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>climatology :</span></dt><dd>Climatology is based on 1971-2000 SST, Xue, Y., T. M. Smith, and R. W. Reynolds, 2003: Interdecadal changes of 30-yr SST normals during 1871.2000. Journal of Climate, 16, 1601-1612.</dd><dt><span>description :</span></dt><dd>In situ data: ICOADS2.5 before 2007 and NCEP in situ data from 2008 to present. Ice data: HadISST ice before 2010 and NCEP ice after 2010.</dd><dt><span>keywords_vocabulary :</span></dt><dd>NASA Global Change Master Directory (GCMD) Science Keywords</dd><dt><span>keywords :</span></dt><dd>Earth Science &gt; Oceans &gt; Ocean Temperature &gt; Sea Surface Temperature &gt;</dd><dt><span>instrument :</span></dt><dd>Conventional thermometers</dd><dt><span>source_comment :</span></dt><dd>SSTs were observed by conventional thermometers in Buckets (insulated or un-insulated canvas and wooded buckets) or Engine Room Intaker</dd><dt><span>geospatial_lon_min :</span></dt><dd>-1.0</dd><dt><span>geospatial_lon_max :</span></dt><dd>359.0</dd><dt><span>geospatial_laty_max :</span></dt><dd>89.0</dd><dt><span>geospatial_laty_min :</span></dt><dd>-89.0</dd><dt><span>geospatial_lat_max :</span></dt><dd>89.0</dd><dt><span>geospatial_lat_min :</span></dt><dd>-89.0</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>cdm_data_type :</span></dt><dd>Grid</dd><dt><span>project :</span></dt><dd>NOAA Extended Reconstructed Sea Surface Temperature (ERSST)</dd><dt><span>original_publisher_url :</span></dt><dd>http://www.ncdc.noaa.gov</dd><dt><span>References :</span></dt><dd>https://www.ncdc.noaa.gov/data-access/marineocean-data/extended-reconstructed-sea-surface-temperature-ersst-v5 at NCEI and http://www.esrl.noaa.gov/psd/data/gridded/data.noaa.ersst.v5.html</dd><dt><span>source :</span></dt><dd>In situ data: ICOADS R3.0 before 2015, NCEP in situ GTS from 2016 to present, and Argo SST from 1999 to present. Ice data: HadISST2 ice before 2015, and NCEP ice after 2015</dd><dt><span>title :</span></dt><dd>NOAA ERSSTv5 (in situ only)</dd><dt><span>history :</span></dt><dd>created 07/2017 by PSD data using NCEI&#x27;s ERSST V5 NetCDF values</dd><dt><span>institution :</span></dt><dd>This version written at NOAA/ESRL PSD: obtained from NOAA/NESDIS/National Centers for Environmental Information and time aggregated. Original Full Source: NOAA/NESDIS/NCEI/CCOG</dd><dt><span>citation :</span></dt><dd>Huang et al, 2017: Extended Reconstructed Sea Surface Temperatures Version 5 (ERSSTv5): Upgrades, Validations, and Intercomparisons. Journal of Climate, https://doi.org/10.1175/JCLI-D-16-0836.1</dd><dt><span>platform :</span></dt><dd>Ship and Buoy SSTs from ICOADS R3.0 and NCEP GTS</dd><dt><span>standard_name_vocabulary :</span></dt><dd>CF Standard Name Table (v40, 25 January 2017)</dd><dt><span>processing_level :</span></dt><dd>NOAA Level 4</dd><dt><span>Conventions :</span></dt><dd>CF-1.6, ACDD-1.3</dd><dt><span>metadata_link :</span></dt><dd>:metadata_link = https://doi.org/10.7289/V5T72FNM (original format)</dd><dt><span>creator_name :</span></dt><dd>Boyin Huang (original)</dd><dt><span>date_created :</span></dt><dd>2017-06-30T12:18:00Z (original)</dd><dt><span>product_version :</span></dt><dd>Version 5</dd><dt><span>creator_url_original :</span></dt><dd>https://www.ncei.noaa.gov</dd><dt><span>license :</span></dt><dd>No constraints on data access or use</dd><dt><span>comment :</span></dt><dd>SSTs were observed by conventional thermometers in Buckets (insulated or un-insulated canvas and wooded buckets), Engine Room Intakers, or floats and drifters</dd><dt><span>summary :</span></dt><dd>ERSST.v5 is developed based on v4 after revisions of 8 parameters using updated data sets and advanced knowledge of ERSST analysis</dd><dt><span>dataset_title :</span></dt><dd>NOAA Extended Reconstructed SST V5</dd><dt><span>data_modified :</span></dt><dd>2020-09-07</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (lat: 89, lon: 180, time: 128)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
       "  * time     (time) datetime64[ns] 2010-01-01 2010-02-01 ... 2020-08-01\n",
       "Data variables:\n",
       "    sst      (time, lat, lon) float32 ...\n",
       "Attributes: (12/37)\n",
       "    climatology:               Climatology is based on 1971-2000 SST, Xue, Y....\n",
       "    description:               In situ data: ICOADS2.5 before 2007 and NCEP i...\n",
       "    keywords_vocabulary:       NASA Global Change Master Directory (GCMD) Sci...\n",
       "    keywords:                  Earth Science > Oceans > Ocean Temperature > S...\n",
       "    instrument:                Conventional thermometers\n",
       "    source_comment:            SSTs were observed by conventional thermometer...\n",
       "    ...                        ...\n",
       "    creator_url_original:      https://www.ncei.noaa.gov\n",
       "    license:                   No constraints on data access or use\n",
       "    comment:                   SSTs were observed by conventional thermometer...\n",
       "    summary:                   ERSST.v5 is developed based on v4 after revisi...\n",
       "    dataset_title:             NOAA Extended Reconstructed SST V5\n",
       "    data_modified:             2020-09-07"
      ]
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