ex03-Read SST, mask and calculate global mean.ipynb

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    "%load_ext load_style\n",
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   "source": [
    "# Read SST, Mask and Calculate global mean\n",
    "\n",
    "In this notebook, we will carry out the following basic operations\n",
    "* have a quick visualization of spatial data\n",
    "* use mask array to mask out land\n",
    "* calculate yearly climatology\n",
    "* calculate global mean, simply."
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Load basic libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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    "collapsed": true
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   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "         \n",
    "from netCDF4 import Dataset # http://unidata.github.io/netcdf4-python/\n",
    "\n",
    "import matplotlib.pyplot as plt         # to generate plots\n",
    "from matplotlib.pylab import rcParams\n",
    "rcParams['figure.figsize'] = 15, 9"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Read skt data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Read data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "ncfile = 'data\\skt.mon.mean.nc'\n",
    "\n",
    "fh  = Dataset(ncfile, mode='r') # file handle, open in read only mode\n",
    "skt = fh.variables['skt'][:]\n",
    "fh.close() # close the file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Have a dirty look of the first month\n",
    "\n",
    "You can fine the skt cover both of land and ocean."
   ]
  },
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FJdKiSF++mnZPNAFromT/D2OEyhFte+MLSl+fc78S/eWYqq+g0MI1aey6aGS5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\nOQ0A7h+WPriPjy8Vx88ebIc0O/1St8YaNT4GgJuz8vnoNumC14Uen3V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      "text/plain": [
       "<matplotlib.figure.Figure at 0x462e7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(skt[0])                        #data for the first time-step (January 1948)\n",
    "plt.title('Monthly SKT in Jan 1948 [$^oC$]')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Read mask data\n",
    "\n",
    "We hope only see skt over ocean. That is so-called (Sea Surface Temperature) SST. So have to mask the land part."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1  read data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lmfile = 'data\\lsmask.19294.nc'\n",
    "lmset  = Dataset(lmfile)\n",
    "lsmask = lmset['lsmask'][0,:,:]# read land mask"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Have a dirty look of the mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0xcb752e8>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9Ksmbk3zlbE0FAADgdA4Mat39Z0lqn7svnW9zAAAAOKNZHwEAAFi8WYY+sgb2GtO8rDHi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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x85f9828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(lsmask)                        \n",
    "plt.title('Land and Ocean Mask')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 Set \"1\" on Ocean and \"nan\" on the land"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0xcfe9a58>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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R1KQBm24R1yvnTrbZSN8Bq+p0d+8c9zg9agAAAIMR1AAAAAYjqAEAAAxGUAMAABiMBa/X\n1DwWmR7Jsn6fdV9gc93bP411/ywDzMPBc+GmneuB4+lRAwAAGIygBgAAMBhBDQAAYDBq1DbYOo1v\nX2Vd0kjv0yzvwyjtV1sGMLbDrg+znLuPu864HsB86FEDAAAYjKAGAAAwGEENAABgMGrUNsQ0485H\nqWVKjF9PlrdW3Mi1iQBMZ5prxizn+3lcI1zTYTH0qAEAAAxGUAMAABiMoAYAADAYQQ0AAGAwJhNh\n4datyHhRk3Gs2/twtua1kCoAsznunGtyKVgvetQAAAAGI6gBAAAMRlADAAAYTHX30ja2s7PTu7u7\nS9se87eNNUezjOlft/dpWXUL6/a+AHB3FslmHY1Uo1lVp7t757jH6VEDAAAYjKAGAAAwmKmDWlXd\ns6r+oKreMLl9SVVdV1XvrapXVdW5i2smAADA9jibddR+IMkNSc6f3H5Bkp/t7ldW1S8keUaSF825\nfSyZMeN3d9h7MtI450XYxt8ZgOlYrw2WY6oetaq6OMk/SfKLk9uV5JuSXD15yEuTPHERDQQAANg2\n0w59fGGSH0ryhcnt+yf5s+6+Y3L7liQXzbltAAAAW+nYoFZV35Hko919ev/dhzz00Hn+q+qKqtqt\nqt3bbrttxmYCAABsj2l61B6d5AlVdXOSV2ZvyOMLk9yvqu6scbs4yYcOe3J3X9XdO92984AHPGAO\nTQYAANhsZ7XgdVU9NskPdvd3VNVrkrx232Qif9jd/+1Mz7fg9XbYhglJjiuU9h7s2Yb3AYDjHbxm\nzOv6sIiJS6aZUMv1bf2tctKbZSx4fWWSZ1fVjdmrWXvxCV4LAACAibOZnj/d/ZYkb5n8+6Ykj5p/\nkwAAALbbSXrUAAAAWICzqlE7KTVqm8k47bs7bNzzyO/TNo6938bfGWAUs9Q5j7yQtmvI+tn0GjUA\nAAAWQFADAAAYjKAGAAAwGEENAABgMGc1PT+wmaYp2FYoDcC8bNo1ZZbJUVitafbHqiew0aMGAAAw\nGEENAABgMIIaAADAYJa64PX5dWFfWped6DVWPVZ02xlfvRmOW+x5pLH2I7UFgNXZxu+Arm/jOe47\n1DSu7asteA0AALCOBDUAAIDBCGoAAACDWbt11NZhzYNNYmz0ZjquJu2w/b6o42qW1/W5BABWYZnf\nQfSoAQAADEZQAwAAGIygBgAAMBhBDQAAYDBrN5kIi2WSBpKxJg6Z5nV8bgE2j8nhDn8PXPO2hx41\nAACAwQhqAAAAgxHUAAAABrMRNWqbNoZ5WYt6G+PMnY5bAHs0I3121csBsEzHXaNdhzaHHjUAAIDB\nCGoAAACDEdQAAAAGsxE1autWX3OcRbXfmGWmddhn5eDncpbarGledxbTvMaqPv+z/H6OVQBmNfI1\nkbOjRw0AAGAwghoAAMBgBDUAAIDBCGoAAACDqe5e2sbOrwv70rpsadvbb90nGJmFQlEWyULP0zE5\nEMB0tvG72jpx3Zmfa/vq0929c9zj9KgBAAAMRlADAAAYjKAGAAAwGDVqG8xYYpbpsGPMZ/Bwizgf\nea+BTbON393WjWvPbNSoAQAArClBDQAAYDCCGgAAwGBOrboBy3JwDO02jHu2zhXL5PM1vePeq1nq\n/dQIAsBm0aMGAAAwGEENAABgMIIaAADAYAQ1AACAwWzNZCIA62KWSUBMHAJsmm2cCG5krjPLp0cN\nAABgMIIaAADAYAQ1AACAwahRA5jSLPURs4zpt3g1wN0ddh5Ut7Y4rjurp0cNAABgMIIaAADAYAQ1\nAACAwQhqAAAAg9mayUQUm86HSQ4AAGDx9KgBAAAMRlADAAAYjKAGAAAwmK2pUWM2avvYVMv6bM+r\nrvPg66gNBbj7udD3FjaJHjUAAIDBCGoAAACDEdQAAAAGo0ZtQ8yrXsXYbjbVSJ/tkdoCsEmm+T7k\nHHx36p7HpEcNAABgMIIaAADAYAQ1AACAwQhqAAAAgzGZyJpaVdGnYlNGtI2F4dP8zo5XAFhfetQA\nAAAGI6gBAAAMRlADAAAYzNbUqB2s1djGmhZYR45VAJgvNczrQY8aAADAYAQ1AACAwQhqAAAAg9ma\nGjVgPahJm5+D76WaBIDN51y/OfSoAQAADEZQAwAAGMxUQa2q7ldVV1fVH1fVDVX1D6vqwqq6pqre\nO/n7gkU3FgAAYBtM26P2c0l+o7v/VpK/l+SGJM9J8ubufliSN09uAwAAcELHTiZSVecneUyS70mS\n7v5sks9W1eVJHjt52EuTvCXJlYto5LZbZlGoAlTYXCYXAdg8zuWba5oeta9McluSX6qqP6iqX6yq\n+yR5UHffmiSTvx+4wHYCAABsjWmC2qkkj0zyou5+RJK/ylkMc6yqK6pqt6p2P5fbZ2wmAADA9pgm\nqN2S5Jbuvm5y++rsBbePVNWDk2Ty90cPe3J3X9XdO929c07Om0ebAQAANtqxNWrd/eGq+kBVPby7\n353ksiTvmvx5WpLnT/7+9YW2dM4Ojue1yC4AADCKY4PaxPcneVlVnZvkpiRPz15v3Kur6hlJ3p9E\nJSMAAMAcTBXUuvsdSXYO+dFl820OAAAA066jBgAAwJJMO/Rx4x22BsWq6tash8E2Uz8KwLxs4jXE\n98TtoUcNAABgMIIaAADAYAQ1AACAwQhqAAAAgzGZyAAUhQIAcJDviNtNjxoAAMBgBDUAAIDBCGoA\nAACDUaO2ZMYawxgWdSyOvLiq8w/A2Jyn2U+PGgAAwGAENQAAgMEIagAAAIMR1AAAAAZjMpEzUNAJ\nq7dux+E07R15whEAFmPdrmesnh41AACAwQhqAAAAgxHUAAAABqNGDWDJDtYpLKpmTT0EsG3UALNJ\n9KgBAAAMRlADAAAYjKAGAAAwGDVqACumlmw209SieG+BZXCuYRH0qAEAAAxGUAMAABiMoAYAADAY\nQQ0AAGAwJhMBYC0p3gdWxfmHZdCjBgAAMBhBDQAAYDCCGgAAwGDUqAGwFg4ucK1GBFgW5xtWQY8a\nAADAYAQ1AACAwQhqAAAAg1GjBsDKHaw/O4waEWAZnGsYhR41AACAwQhqAAAAgxHUAAAABiOoAQAA\nDMZkIgCsnOJ9ALgrPWoAAACDEdQAAAAGI6gBAAAMRo0aAABr6ZovvGbVTYCF0aMGAAAwGEENAABg\nMIIaAADAYNSoAQCwlg6uwThLzZp1HBmVHjUAAIDBCGoAAACDEdQAAAAGI6gBAAAMRlADAAAYjKAG\nAAAwGEENAABgMIIaAADAYCx4DQDARphlAeyDj7EANqPQowYAADAYQQ0AAGAwghoAAMBg1KgBALCR\n1JuxzvSoAQAADEZQAwAAGIygBgAAMBhBDQAAYDCCGgAAwGAENQAAgMEIagAAAIMR1AAAAAYjqAEA\nAAxGUAMAABiMoAYAADAYQQ0AAGAwghoAAMBgBDUAAIDBCGoAAACDmSqoVdW/rarrq+qPquoVVXWv\nqrqkqq6rqvdW1auq6txFNxYAAGAbHBvUquqiJP8myU53f22SeyZ5cpIXJPnZ7n5Ykk8mecYiGwoA\nALAtph36eCrJl1TVqST3TnJrkm9KcvXk5y9N8sT5Nw8AAGD7HBvUuvuDSX4qyfuzF9D+PMnpJH/W\n3XdMHnZLkosW1UgAAIBtMs3QxwuSXJ7kkiQPSXKfJN92yEP7iOdfUVW7VbX7udx+krYCAABshWmG\nPn5zkj/t7tu6+3NJXpfkHyW532QoZJJcnORDhz25u6/q7p3u3jkn582l0QAAAJvs1PEPyfuTfH1V\n3TvJZ5JclmQ3yW8n+c4kr0zytCS/vqhGAgDAqlzzhdfc5fbj7vGkFbWEbTJNjdp12Zs05O1J3jl5\nzlVJrkzy7Kq6Mcn9k7x4ge0EAADYGtP0qKW7n5vkuQfuvinJo+beIgAAgC037fT8AAAALImgBgAA\nMJiphj6yvQ4Wz45u5OLedXsvWS8jf/YBRjbN9dk5llXQowYAADAYQQ0AAGAwghoAAMBg1KitgVnH\nTm9jTdRxv/O8xphv43vL2Fb5mVS7AYzqsHPjLOcsC16zCnrUAAAABiOoAQAADEZQAwAAGEx199I2\ntrOz07u7u0vbHgDrSw0IcFKz1PnPUvPrfLWZFlX/XVWnu3vnuMfpUQMAABiMoAYAADAYQQ0AAGAw\nghoAAMBgLHgNALCBFjURwiwWtcj0PBaiHul9Yn42Yb/qUQMAABiMoAYAADAYQQ0AAGAwFrwGYOEs\nBkuyGTUjkDinLdumnTsseA0AALCmBDUAAIDBCGoAAACDsY4aAGyQw2o5rC0FR1NvNj/OG/OlRw0A\nAGAwghoAAMBgBDUAAIDBCGoAAACDMZkIAHOlMH88CvxZB84diztWF/XeOrcslh41AACAwQhqAAAA\ngxHUAAAABlPdvbSN7ezs9O7u7tK2B8DiqStZf+pM2FTOT7NzXlicqjrd3TvHPU6PGgAAwGAENQAA\ngMEIagAAAIMR1AAAAAZjwWsATmSWgnMF/qtlkgC2hc8660yPGgAAwGAENQAAgMEIagAAAINRowbA\n0m1j3chIdXkH27KN+wNgdHrUAAAABiOoAQAADEZQAwAAGIwaNQBYAnVgAJwNPWoAAACDEdQAAAAG\nI6gBAAAMRlADAAAYjKAGAAAwGEENAABgMIIaAADAYAQ1AACAwVR3L29jVbcleV+SL0vysaVtmHmw\nz9aPfbZ+7LP1Y5+tH/ts/dhn68c+O7O/2d0POO5BSw1qX9xo1W537yx9w8zMPls/9tn6sc/Wj322\nfuyz9WOfrR/7bD4MfQQAABiMoAYAADCYVQW1q1a0XWZnn60f+2z92Gfrxz5bP/bZ+rHP1o99Ngcr\nqVEDAADgaIY+AgAADGbpQa2qHl9V766qG6vqOcvePmdWVQ+tqt+uqhuq6vqq+oHJ/c+rqg9W1Tsm\nf7591W3lr1XVzVX1zsm+2Z3cd2FVXVNV7538fcGq28meqnr4vmPpHVX1F1X1LMfZeKrqJVX10ar6\no333HXps1Z6fn1zf/rCqHrm6lm+nI/bXT1bVH0/2ya9W1f0m939FVX1m3/H2C6tr+XY7Yr8deT6s\nqh+eHGfvrqpvXU2rt9cR++tV+/bVzVX1jsn9jrMTWPY6avdM8p4kj0tyS5K3JXlKd79raY3gjKrq\nwUke3N1vr6r7Jjmd5IlJvivJX3b3T620gRyqqm5OstPdH9t3308k+UR3P3/ynyIXdPeVq2ojh5uc\nFz+Y5NIkT4/jbChV9Zgkf5nkl7v7ayf3HXpsTb5Ifn+Sb8/e/vy57r50VW3fRkfsr29J8lvdfUdV\nvSBJJvvrK5K84c7HsTpH7Lfn5ZDzYVV9TZJXJHlUkockuTbJV3f355fa6C122P468POfTvLn3f1j\njrOTWXaP2qOS3NjdN3X3Z5O8MsnlS24DZ9Ddt3b32yf//lSSG5JctNpWMaPLk7x08u+XZi9wM57L\nkvxJd79v1Q3h7rr7d5J84sDdRx1bl2fvi0t391uT3G/yn18syWH7q7vf1N13TG6+NcnFS28YZ3TE\ncXaUy5O8srtv7+4/TXJj9r5fsiRn2l9VVdn7z/1XLLVRG2rZQe2iJB/Yd/uWCAHDmvwvyCOSXDe5\n65mToSMvMYxuOJ3kTVV1uqqumNz3oO6+NdkL4EkeuLLWcSZPzl0vaI6z8R11bLnGje9fJfnf+25f\nUlV/UFX/p6q+cVWN4kiHnQ8dZ2P7xiQf6e737rvPcTajZQe1OuQ+004OqKq+NMlrkzyru/8iyYuS\nfFWSr0tya5KfXmHzuLtHd/cjk3xbku+bDEtgcFV1bpInJHnN5C7H2XpzjRtYVf1okjuSvGxy161J\nvry7H5Hk2UleXlXnr6p93M1R50PH2diekrv+56Pj7ASWHdRuSfLQfbcvTvKhJbeBY1TVOdkLaS/r\n7tclSXd/pLs/391fSPLfY5jBULr7Q5O/P5rkV7O3fz5y57Cryd8fXV0LOcK3JXl7d38kcZytkaOO\nLde4QVXV05J8R5J/0ZPi/MnQuY9P/n06yZ8k+erVtZL9znA+dJwNqqpOJflnSV51532Os5NZdlB7\nW5KHVdUlk/9JfnKS1y+5DZzBZGzxi5Pc0N0/s+/+/XUW/zTJHx18LqtRVfeZTPySqrpPkm/J3v55\nfZKnTR6ekX5BAAABdUlEQVT2tCS/vpoWcgZ3+Z9Hx9naOOrYen2S757M/vj12Sumv3UVDeSvVdXj\nk1yZ5And/el99z9gMplPquorkzwsyU2raSUHneF8+PokT66q86rqkuztt99fdvs41Dcn+ePuvuXO\nOxxnJ3NqmRubzLj0zCS/meSeSV7S3dcvsw0c69FJnprknXdOrZrkR5I8paq+LnvDC25O8q9X0zwO\n8aAkv7qXsXMqycu7+zeq6m1JXl1Vz0jy/iRPWmEbOaCq7p29GXD3H0s/4TgbS1W9Isljk3xZVd2S\n5LlJnp/Dj603Zm/GxxuTfDp7s3iyREfsrx9Ocl6Saybnybd29/cmeUySH6uqO5J8Psn3dve0E1ow\nR0fst8cedj7s7uur6tVJ3pW9oazfZ8bH5Tpsf3X3i3P3muvEcXYiS52eHwAAgOMtfcFrAAAAzkxQ\nAwAAGIygBgAAMBhBDQAAYDCCGgAAwGAENQAAgMEIagAAAIMR1AAAAAbz/wGgmtguJpy7MAAAAABJ\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcdad400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lsm = lsmask + 1\n",
    "lsm[lsm<1.0] = np.nan\n",
    "\n",
    "# now only ocean available\n",
    "plt.imshow(lsm)                        \n",
    "plt.title('Land Mask')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Calculate yearly climatology in the first year (1948)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Get skt for the first year (i.e., 12 months)\n",
    "* Calculate yearly mean\n",
    "* Mask the yearly mean over ocean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0xd26a588>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Tzaj1p0V27ReS6UzhXu6dh1qNKtLJmV8Y159qsUZ6xjhyADM3OJYp9Dkx70PX95BX5Krs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      "text/plain": [
       "<matplotlib.figure.Figure at 0xd0787f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "skt_y1 = np.mean(skt[0:12,:,:], axis=0)\n",
    "\n",
    "# masking the land, leave ocean alone\n",
    "sst_y1 = skt_y1*lsm\n",
    "\n",
    "# dirty look\n",
    "plt.imshow(sst_y1)                       \n",
    "plt.title('Yearly Climatology of SST in 1948 [$^oC$]')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Simply calculate global mean\n",
    "\n",
    "The following is the global averaged sst value. True only considering regular grid. **No weighting needed.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.126839"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sst_global = np.nanmean(sst_y1)\n",
    "sst_global"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## References\n",
    "\n",
    "http://unidata.github.io/netcdf4-python/\n",
    "\n",
    "John D. Hunter. Matplotlib: A 2D Graphics Environment, Computing in Science & Engineering, 9, 90-95 (2007), DOI:10.1109/MCSE.2007.55\n",
    "\n",
    "Stéfan van der Walt, S. Chris Colbert and Gaël Varoquaux. The NumPy Array: A Structure for Efficient Numerical Computation, Computing in Science & Engineering, 13, 22-30 (2011), DOI:10.1109/MCSE.2011.37\n",
    "\n",
    "Kalnay et al.,The NCEP/NCAR 40-year reanalysis project, Bull. Amer. Meteor. Soc., 77, 437-470, 1996."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}