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Analysis of NCAR 1-degree grid.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import urllib.request\n",
"import os.path\n",
"import tarfile\n",
"import scipy.io\n",
"import netCDF4\n",
"import numpy\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def fetch_file_from_web(url, filename=None):\n",
" \"\"\"Download file from url and optionally names it as filename\"\"\"\n",
" if filename is None: local_file = os.path.split(url)[-1]\n",
" else: local_file = filename\n",
" if not os.path.isfile(local_file):\n",
" print('\\rDownload file %s ...'%local_file, end='')\n",
" urllib.request.urlretrieve(url, local_file)\n",
" print('\\rDone.', end='')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fetch_file_from_web('ftp://ftp.cgd.ucar.edu/pub/njn01/CESM_GFDL/grids/cesm.pop.gx1v6.grid.info.20170303.tar.gz')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<TarInfo 'cesm.pop.gx1v6.grid.info.20170303' at 0x191f17a3b38> 0\n",
"<TarInfo 'cesm.pop.gx1v6.grid.info.20170303/README' at 0x191f17a3c00> 899\n",
"<TarInfo 'cesm.pop.gx1v6.grid.info.20170303/gx1v6_domain_size.F90' at 0x191f17a3cc8> 1760\n",
"<TarInfo 'cesm.pop.gx1v6.grid.info.20170303/gx1v6.pop.gridinfo.20170303.nc' at 0x191f17a3d90> 19177868\n",
"<TarInfo 'cesm.pop.gx1v6.grid.info.20170303/gx1v6_vert_grid' at 0x191f17a3e58> 2280\n",
"<TarInfo 'cesm.pop.gx1v6.grid.info.20170303/gx1v6_POP_DomainSizeMod.F90' at 0x191f17a3f20> 1899\n",
"<TarInfo 'cesm.pop.gx1v6.grid.info.20170303/pop_in' at 0x191f1874048> 1644\n",
"<TarInfo 'cesm.pop.gx1v6.grid.info.20170303/gx1v6_region_ids' at 0x191f1874110> 790\n",
"<TarInfo 'cesm.pop.gx1v6.grid.info.20170303/grid.F90' at 0x191f18741d8> 104766\n"
]
}
],
"source": [
"# Open the tar file\n",
"TF = tarfile.open('cesm.pop.gx1v6.grid.info.20170303.tar.gz','r')\n",
"# Show list of files in tarfile\n",
"for m in TF.getmembers(): print(m, m.size)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ANGLE (384, 320)\n",
"ANGLET (384, 320)\n",
"DXT (384, 320)\n",
"DXU (384, 320)\n",
"DYT (384, 320)\n",
"DYU (384, 320)\n",
"HT (384, 320)\n",
"HTE (384, 320)\n",
"HTN (384, 320)\n",
"HU (384, 320)\n",
"HUS (384, 320)\n",
"HUW (384, 320)\n",
"KMT (384, 320)\n",
"KMU (384, 320)\n",
"REGION_MASK (384, 320)\n",
"TAREA (384, 320)\n",
"TLAT (384, 320)\n",
"TLONG (384, 320)\n",
"UAREA (384, 320)\n",
"ULAT (384, 320)\n",
"ULONG (384, 320)\n",
"dz (60,)\n",
"dzw (60,)\n",
"z_t (60,)\n",
"z_w (60,)\n",
"z_w_bot (60,)\n",
"z_w_top (60,)\n"
]
}
],
"source": [
"# Find the member we are looking for\n",
"member = [m for m in TF.getmembers() if 'gx1v6.pop.gridinfo.20170303.nc' in m.name][0]\n",
"# Open the netcdf file via a file-like object\n",
"nc = scipy.io.netcdf.netcdf_file(TF.extractfile(member), 'r', mmap=False)\n",
"# List variables and shapes in file\n",
"for v in nc.variables: print(v, nc.variables[v].shape)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"OrderedDict([('title', b'giaf.c2b4m.tidal.port.010'),\n",
" ('history',\n",
" b'Fri Mar 3 14:53:21 2017: ncks -v ULAT,ULONG,TLAT,TLONG,KMU,KMT,DXU,DXT,DYU,DYT,REGION_MASK,UAREA,TAREA,HU,HUS,HUW,HT,HTN,HTE,ANGLE,ANGLET,z_t,z_w,z_w_top,z_w_bot,dz,dzw giaf.c2b4m.tidal.port.010.pop.h.nstep1.0001-01-02-03600.nc gx1v6.pop.gridinfo.20170303.nc\\nnone'),\n",
" ('Conventions',\n",
" b'CF-1.0; http://www.cgd.ucar.edu/cms/eaton/netcdf/CF-current.htm'),\n",
" ('contents', b'Diagnostic and Prognostic Variables'),\n",
" ('source', b'CCSM POP2, the CCSM Ocean Component'),\n",
" ('revision', b'$Id: tavg.F90 80840 2016-09-29 13:32:09Z jet $'),\n",
" ('calendar', b'All years have exactly 365 days.'),\n",
" ('start_time',\n",
" b'This dataset was created on 2017-02-24 at 15:20:22.7'),\n",
" ('cell_methods',\n",
" b'cell_methods = time: mean ==> the variable values are averaged over the time interval between the previous time coordinate and the current one. cell_methods absent ==> the variable values are at the time given by the current time coordinate.'),\n",
" ('nsteps_total', 1),\n",
" ('tavg_sum', 3600.0),\n",
" ('NCO', b'\"4.5.5\"')])"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Global attributes\n",
"nc._attributes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# All data has the same shape which means staggering must be determined from values"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ULONG @ j=0, i=0:3 : [ 321.12500894 322.25000897 323.375009 324.50000903]\n",
"ULONG @ j=0, i=N-7:N : [ 315.50000878 316.62500881 317.75000884 318.87500887 320.0000089 ]\n",
"TLONG @ j=0, i=0:3 : [ 320.56250892 321.68750895 322.81250898 323.93750901]\n",
"TLONG @ j=0, i=N-7:N : [ 314.93750876 316.0625088 317.18750883 318.31250886 319.43750889]\n"
]
},
{
"data": {
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5/3ikeJoHDp/PfSdfwGMHz+Wrzz6HZ4s9ns1jBzKjNd3UcU3DLfuRlrMyA9Ghb4SJ39/P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\nGBs2tZnyAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x191f18847b8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.subplot(121)\n",
"v = nc.variables['ULONG']\n",
"plt.pcolormesh( v[:] ); plt.colorbar(); plt.xlabel('i'); plt.ylabel('j'); plt.title('ULONG');\n",
"print('ULONG @ j=0, i=0:3 :', v[0,:4])\n",
"print('ULONG @ j=0, i=N-7:N :', v[0,-5:])\n",
"plt.subplot(122)\n",
"v = nc.variables['TLONG']\n",
"plt.pcolormesh( v[:] ); plt.colorbar(); plt.xlabel('i'); plt.ylabel('j'); plt.title('TLONG');\n",
"print('TLONG @ j=0, i=0:3 :', v[0,:4])\n",
"print('TLONG @ j=0, i=N-7:N :', v[0,-5:])"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ULAT @ j=0, i=0:3 : [-78.95289509 -78.95289509 -78.95289509 -78.95289509]\n",
"ULAT @ j=0, i=N-7:N : [-78.95289509 -78.95289509 -78.95289509 -78.95289509 -78.95289509]\n",
"ULAT max 89.977342085\n",
"TLAT @ j=0, i=0:3 : [-79.22052261 -79.22052261 -79.22052261 -79.22052261]\n",
"TLAT @ j=0, i=N-7:N : [-79.22052261 -79.22052261 -79.22052261 -79.22052261 -79.22052261]\n",
"TLAT max 89.7064096419\n"
]
},
{
"data": {
"image/png": 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BUmP7gd2HN/ntiXQNcAdwh6q+v/z+Zgptf6Z6YqmIHAXuWbuwJVk7FT9F0BU1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\nQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x191f3ce60b8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.subplot(121)\n",
"v = nc.variables['ULAT']\n",
"plt.pcolormesh( v[:] ); plt.colorbar(); plt.xlabel('i'); plt.ylabel('j'); plt.title('ULAT');\n",
"print('ULAT @ j=0, i=0:3 :', v[0,:4])\n",
"print('ULAT @ j=0, i=N-7:N :', v[0,-5:])\n",
"print('ULAT max', v[:].max())\n",
"plt.subplot(122)\n",
"v = nc.variables['TLAT']\n",
"plt.pcolormesh( v[:] ); plt.colorbar(); plt.xlabel('i'); plt.ylabel('j'); plt.title('TLAT');\n",
"print('TLAT @ j=0, i=0:3 :', v[0,:4])\n",
"print('TLAT @ j=0, i=N-7:N :', v[0,-5:])\n",
"print('TLAT max', v[:].max())"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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IIOEuFoSWVSSxKYn65581MxHZChzE6c76u6rOEpEDqtrJd1yA/f7HQa+dCkwF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\ndWe1cf995T1o8loG79mPS+GqT5WYGrq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ASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x191f4092be0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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lU9Qkb1hP+IQJZ69sAlu1JHzCBJI3rC+Qqx1vTW3QpEkTrrrqKurXr0+NGjVo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\n407HzMxK407HzMxK407HzMxK8y8PQkpmRmmZHQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x191f3bf2d30>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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X+KJPO6LeXEBoGmy47WJ6zV3OmZZkgoYd0WQjN0ceJ06X1bzzDg5Mm07Nu+76\nyzWbQCuKYQI2b97MxIkT+f3336lWrRqDBw8mJSUFcHqQjouLo2LFihw4cOAvnX4aE2jxa/7g1zG3\nErXkCBEhsKxHY/pO+IAKEVW8Ds1kYkc0+XQiyTSYNIla995Lg0mT/nLNJtCKapiAw4cPEx4eTpUq\nVdizZw9ffvnlyXmTJk0iKiqK999/n5tuuonjx48X6nsbk5XkpENMf/BSdl97He0XHWFdVEWqTP8f\ng16ea0kmSNkRTT6lrFxBg0mTTh7BhHftQoNJk0hZuSIgRzVeDRPQoUMHOnbsSKtWrWjUqBHnnHMO\nAGvXrmXq1Kn89ttvVKpUifPOO48xY8bw5JPWdNQEhvp8zH75Icp/9CUd9sDWeiEcf+xOrrryLq9D\nM6dgwwT4KU7d2RcmGyYgg/VplcGrukiYOpWlNZKZkD775FhPQzY3pt6sn6m9XzkYAbsu70rfx6cU\nWbcxtl9ksGECjDHF3tIayUSMnkz1fiHsry/0nbGd9su3kRYCy8+pw8UT3ufsmvVPvSITNCzRGGOC\nyoT02VTvG8LDH/lQgfBjsLoRTLuiJh/dEet1eCYfLNFkoqqIiNdhlBil9dSsyb9ym3Zy1c8+KqY6\nr79vCy/3KYNwyNvATL5ZqzM/YWFhJCQk2JdjIVFVEhISCAsL8zoUUwysXPgVH13VgQn/S6fJbjha\nFj7+h9BxI7TZ4qNueF2vQzT5ZEc0fho2bMj27dvZt2+f16EUqZSUlIAlg7CwMLu/xuTowP5dzH1i\nIC0X7KNVKqxpUZ5G244xsX8IqxqHsLKJj/tn+khq19vrUE0+WaLxU7ZsWZo2bep1GEUuNjaWjh07\neh2GKWV86el8OuFmanzxK9GJsKFJGeo/NILu8X+ytEYyiemzkeTdJLZuQFK73kQnhHsdssknzxON\niMQAtwAnDiMeU9U5IlID+Ag4E3hTVe/Opnx14AOgCRAPDFDVAwEO2xhTAN9/+AKH35hM683K3qqw\n8c7e9L77GUJCnLP5PYAeDPM2SFNoPE80rkmqOjHTtBTgCaCt+8jOcOBbVR0vIsPd148EJkxjTEFs\nWLWQxWPvJGrpUSqVgeU9m9J33HTCwou2n0BTtIIl0fyNqiYDP4lI81Ms2hfo7j5/C4jFEo0xQSXp\ncCJfjLyGpj9spc0RiGsfzpmjX+OMyE5eh2aKgOc9A7inzm4CDgGLgAf9T32JyGCgcw6nzg6qalX3\nuQAHTrzkEK/OAAAf6ElEQVTOYtlbgVsBatWq1WnGjBmFuCXFV1JSUrYDq5U2VhcZCqMu1Odj6/f/\no8H8RTTYC/H1Q0js25cGHXoWUpRFw/aLDOeff36eewYokkQjIvOArNomPg4sBPYDCowG6qnqEL+y\ng8llonFfH1DVaqeKKasuaEor614jg9VFhoLWxa9z32HXKxOIXJdOYiXY2+8c+j7635PXYYoT2y8y\nBG0XNKp6YW6WE5EpwBd5XP0eEamnqrtEpB6wN88BGmMKzY7Nq/jxySG0WnSYpsCy8+rRa8J0Kler\n7XVoxiOe/7Rwk8MJ/YGVeVzFZ8CN7vMbgbyNZ2yMKRTHUo4y49F+xA+4ig4LD7OpeXnKv/Uyg/77\nnSWZUi4YGgM8IyLROKfO4oHbTswQkXigMlBORPoBPVV1tYhMBSar6iJgPDBDRG4GtgADijh+Y0q9\nuVNHwvsf0m4n7Kgt7Lz3Bq64frjXYZkg4XmiUdXrc5jXJJvpQ/2eJ+A0uzfGFLEVC75g3bOP0WrV\ncZIqwqorO9B35JuULW/dDpkMeU40IhIOpKhqegDiMcYUA/v3bGPeyGto8UsCLdNg5VnVuWD8O3Sp\n38zr0EwQOmWiEZEQYBBwLc5d+seA8iKyH5gNvKaqGwIapTEmKBxPTWXWhCHUmvMHHQ7A+qZlaDT8\nSQb+8wqvQzNBLDdHNPOBecCjwEpV9cHJrl/OByaIyKeq+m7gwjTGeG3+B5NIfmMKbbYoe6rD5nsv\n5/I7J3gdlikGcpNohqrq+swTVTUR+Bj4WETKFnpkxpgi13nMN+xPSs2YMHc2zdI2MHTrG7RbmUrl\nsrD80ub0GzuN8hXtBkaTO7lJNLNEpDawBlgOrDjxV1UPA6jq8cCFaIwpKieSzBmyjn/IMqrtXk27\nJX8SfhRWd4ig69ipdGreweMoTXFzykSjqq1FpDzQGmgHtMfpX6y9iBxT1dLXr74xJdgZso7hR54l\naXEE9fYJmxsI/25zOR++MN7r0EwxlatWZ6p6DFgiIhuAo0BNoCXOkY0xpoQ4+/gCrln7CZU2VOJ4\nZdh7fjIrqvXkd183r0MzxVhuWp1FAr2By4BawDfAe8CtqpqaU1ljTPGwdf0KfhkzhOF/JKHAps6p\ndGuSSNnQMjyf2sbr8Ewxl5sjmjhgCTABmOUe3RhjSoCUo0eYFTOQRt9uoH0SLGtRjsktbqR6eWGV\nxrEwNYrF2tLrME0xl5tEcwfOwGN3AS+JSAJOg4AVOA0CZgYwPmNMAKjPx9ypjxMybSbtd8H2OsKx\nB4YwcVMH9ielEq+wOD0jwdSMKOdhtKa4y01jgNf8X4tIQzIaBVwJWKIxphhZ+sOnbHpuJFGr0zgU\nDqsGnEH/UW8RGlqGRX7LWdf4prDkuQsaVd0ObAe+LPxwjDGBsnfHZr6LuY6WvybSIg2Wn12DC8e/\nT9c6p3kdminhctMY4GwgTlUPFkE8xphCdjw1lZlP30CducvocBDWnV6Wpo+OYeC5l3sdmiklckw0\nIjIa+B24DxhYJBEZYwrNt+8+Q8rbb9J2q7K7OsTffyV9bxvjdVimlDnVEc3PwHnAuiKIxRhTSOIW\nx7Jywn1ELT9GSjlYcVkkfUe/S/kK1m2MKXo5JhpVnQvMLaJYjDEFdChxD1+OuobTf9xF62OwKroy\n5z79Bp2b2r0wxju5uUYjqqoFXcYYEzjq8zFz4u1U+exHOuyHTY1CqfnA/zHg0hu8Ds2Y3A0TICIf\n49ysufXERBEpB5wL3IgzlMCbAYnQGJOjnz6bQsJrz9Fqo4/9VWD90B5c9sALhISEeB2aMUDuEs0l\nwBBgmog0BQ4CYUAo8DXwnKouCVyIxpisbF27hF/G3ELU4mTCQ2D5BY24bMIHhFeq5nVoxvxFbm7Y\nTAFeAV5xx52pCRy15s7GeONI8mE+G3UNjedvon0yrG5TgeiYlxjY7h9eh2ZMlvJ0w6Y77syuAMVi\njMmB+nzMefURyn74BR12w9a6Quojt3LlgPu8Ds2YHOW5ZwBjTNFbPP8j4p+PIWpNOofCYfU1Z9Jv\nxBuEhtq/sAl+tpcaE8T2bNvI/CevI/LXgzT3wbJ/1KLn+PfoWruR16EZk2u5TjQiIsC1QDNVfUpE\nTgPqqupvAYvOmFIiYepUltZIZkL6bHYn76Zuhdpc900qrX/fR4cUWNuiHC0en8Cgrpd4HaoxeZaX\nI5pXAB9wAfAU8CfwMXBmAOIyplRZWiOZiNGTqd4vhDIVhLu+2EGzPbCvMiTeM5B+N8d4HaIx+ZaX\nRNNFVc8QkSUAqnrAvZfGGFNAE9Jn0+CSEB6d4aNsGigwu7Mw77L6zB0U43V4xhRIXhLNcREJxfkf\nQERq4RzhGGMK4MD+XXSI3cGAn3yUSQMBPusivHdBKHJsr9fhGVNgebl1+AXgU6C2iIwFfgKeLmgA\nIhIjIjtEZKn76OVOryEi80UkSUReymt5Y4KdLz2dT8YNYWnfCxgyz8eeKnAkDD46Rzh/udJmi4+6\n4XW9DtOYAsv1EY2qvicifwA9cH509VPVuEKKY5KqTsw0LQV4AmcY6bb5KG9M0Prhk1c4OPUlojYp\n+6rC4otPp/mPG3n2ihBWNQ5hVWMf98/0kdSut9ehGlNgeb1hcw2wJkCxZH6vZOAnEWleFO9nTFHY\nHLeI38fcRtSSI0SUgeUXNuHy8dNpM/1DlnZPJjF9NpK8m8TWDUhq15vohHCvQzamwORUnS6LyAM5\nzVfV/xQoAJEY4CbgELAIeFBVD/jNHwx0VtW781M+07K3ArcC1KpVq9OMGTMKEnqJkZSURESEjVMC\ngauL1JRkdn0+kchfdlPlCKxoHUa5q++kar0Whf5ehcX2iwxWFxnOP//8P1S1c17K5CbRjHKfRuI0\nZf7Mfd0H+E1Vrzvlm4jMA7I62fw4sBDYj9PIYDRQT1WH+JUdTM6Jpk5O5bMTGRmpa9euPdVipUJs\nbCzdu3f3OoygUNh1oT4fs19+gLCPvqLBHthSP4Twu+6i25V3Ftp7BIrtFxmsLjKISJ4TTW461XzS\nXfkPwBmq+qf7OgaYnZs3UdULc7OciEwBvsjNsn7r3lOQ8sYEyu/fTGP7S2NptTadgxEQd21X+j3+\nunXfb0qdvFyjqQOk+r1OdacViIjUU9UTHXX2B1YWZXljCtuuLWv5PuYGWi06TDMfLD+3DhePf5+z\na9b3OjRjPJGXRPM28JuIfIrT6qwv8FYhxPCMiETjnPqKB247MUNE4oHKQDkR6Qf0VNXVIjIVmKyq\ni3Iqb0xROpZylFmjr6X+N3F0OAxrWpaj1RMTGXjmRV6HZoyn8tK8eayIfAl0w/lSv6kwBjxT1etz\nmNckm+lDc1PemKLy1Rsx+N7/gHbbYWctYcfwa+k/+HGvwzImKOSlU82RmSb1EZE+qvpUIcdkTLGx\ncuFXrJn4f0StSiU5DFb2b0e/mLcpWz7M69CMCRp5OXWW7Pc8DLgMKKwbNo0pVhL2bufrkf+i5S/7\niDwOqzpX45/j3uKshsHbXNkYr+Tl1Nmz/q9FZCLwVaFHZEwQ86Wn8+mEIdT8/DeiD8CGJmVo8MhI\nBpx/tdehGRO0CjLwWUWgYWEFYkyw+/7DFzj8xmRab1b2VoNNd11G77smWHNlY04hL9doVuD23AyE\nArVwbpA0pkTbsGohi8feSdTSo063MRc3o9+4Dyhf0e4UNyY38nJEc5nf8zRgj6qmFXI8xgSNpMOJ\nfDFyEE1/2EabIxDXPoIzx7xGp5ZneB2aMcVKXhLNnar6iP8EEZmQeZoxxU3nMd+wP8nvXuS5XzAo\n+T0uXLaUDnshvkEIaaPu46q+t3gXpDHFWF4SzUVA5qRyaRbTjClWTiSZM2Qdl6TNo/aazUSuVxIr\nwZobu9H3kcl2HcaYAjhlohGRO4A7gWYistxvViXg50AFZkxRusC3kCt3TKPhsrIo8HOnSjxb/37i\nHr3G69CMKfZyc0TzPvAlMA4Y7jf9T1VNDEhUxhSRlKNHuDvxOc5esp3qf5ZlU4t02rY+SGKZbhxN\nr+x1eMaUCLnpvfkQzlgv9tPOlChfTh1ByPsf03snbK8NZc/5k4urJHOcMixMjfI6PGNKjNycOvtJ\nVc8VkT/JaN4MTseaqqr2s88UK8t++pwNkx6n1arjJFWED89pxps1byE6ZDNd0+JY6Itisbb0Okxj\nSozcHNGc6/6tFPhwjAmc/Xu2Me+Ja2i5MIGWabCyS3UuGP8en72+Hl9SKou1JYvTMxJMzYhyHkZr\nTMmRlxs2/9aU2Zo3m+LgeGoqs8bdRK25i+lwANY3K0ujR2IY+M8rAFg0osnJZW0kRWMKX17abGY1\nqMalhRWIMYHw3bSJfN0nmjbTFqMCm+/ty+VzltPRTTLGmMAraPPmBYEKzJiCWLv0B5aNv5fWy45x\nrBws79WCfmPfp3wF6zbGmKJmzZtNifLnoQS+eGIAp/+4kzZHYXV0Jc4eO5VOp7f3OjRjSq08NW8W\nkWpAC5zxaBARVPWHwIZozKmpz8esSXdR+dNYovfD5oYhpN/3IFdfNsTr0Iwp9fLSGGAoMAxnaICl\nQFfgF+CCwIRmTO78Mvt/7H11IpEbfCRUhrVDzufyh16ybmOMCRJ56etsGHAmsFBVzxeRVsDTgQnL\nmFPbun4Fv4wZQqtFSTQOgWXnN6D3uOlUqlrT69CMMX7ykmhSVDVFRBCR8qq6RkQiAxaZMdlIOXqE\nWaMG0ui7DbRPgtVRYbR/8gUGte/mdWjGmCzkJdFsF5GqwEzgGxE5AMQHJCpjsqA+H19OeZzQ6TNp\nvwu21RGOPXgzV17zoNehGWNykOtEo6r93acxIjIfqALYEY0pEku+/5TNz40kKi6NQ+GwakAn+o96\nk9DQgoxGbowpCvn6L1XV7wFEZCvw70KNyBg/e3ds5tuYa4n89QDN02H52TW5cPx7dK1zmtehGWNy\nqaA/B6VQojAmk+Opqcwcez11vlpO9EFY27wszR59moHnXHbqwsaYoFLQRKOnXsSYvPn23WdIeftN\n2m5VdtcQttx/Bf1uG+N1WMaYfMpNFzSZhwc4OQuoUOgRmVIjYepUltZIZkL6bHYn76ZRelVu/jiB\nyI0+jpaHFZdF0m/M+5QLq+h1qMaYAshNzwABHR5ARGKAW4B97qTHVHWOiFwEjAfKAanAw6r6XRbl\nqwMfAE1wWsENUNUDgYzZFI6lNZKJGD2ZGpeH0D4Rrpm/jwrHYU2LcnR94X06N23jdYjGmEIQLE12\nJqnqxEzT9gN9VHWniLQFvgIaZFF2OPCtqo4XkeHuaxu6oBiYkD6bTl2Fxz/wEaqQFgKvXhrC2nPq\ncIUlGWNKjGBJNH+jqkv8Xq4CKrg3ih7LtGhfoLv7/C0gFks0Qe+nz/7Lv97eTpd1SlJ5iDgGM7sK\nsdEhSPJur8MzxhSiYEk094jIDcAi4MEsTn1dCSzOIskA1FHVXe7z3UCd7N5ERG4FbgWoVasWsbGx\nBQ68JEhKSiqyuji0dxNHZr5Em2VH6RAC89oLZ61XPjpH6LlYWdXEx85mNTz7bIqyLoKd1UUGq4uC\nEdXANxwTkXlA3SxmPQ4sxDlNpsBooJ6qDvEr2wb4DOipqhuzWPdBVa3q9/qAqlY7VUyRkZG6du3a\nPG9LSVQUo0omJx3i85hraDx/M1WTYXWbCoRe2IN6U75gUr8QVjUOoc0WH/fP9JH0xO306D8soPFk\nx0bYzGB1kcHqIoOI/KGqnfNSpkiOaFT1wtwsJyJTgC/8XjcEPgVuyCrJuPaISD1V3SUi9YC9BQ7Y\nFBr1+Zj96sOU+3AOHXbD1nohpA6/jSuvvtdpdfZEQxLTZyPJu0ls3YCkdr2JTgj3OmxjTCHy/NTZ\niSThvuwPrHSnVwVmA8NV9eccVvEZcCNOC7UbgVkBDNfkwR/fzWDLC08RtSadg+Gw+pqz6Dfi9ZPd\nxtQYOpQeQA+8OXoxxhQNzxMN8IyIROOcOosHbnOn3w00B0aKyEh3Wk9V3SsiU4HJqroIJ8HMEJGb\ngS3AgCKN3vzNnm0bmR9zHZG/HeR0Hyz7R20ueWYaZ9es73VoxhgPeJ5oVPX6bKaPAbK8HVxVh/o9\nTwB6BCY6kxepx1KYOeZ66n+1kg6HYW2LcrR4fAKDul7idWjGGA95nmhMyfDNW2NIe/d92m1TdtUU\ntj08kH43j/I6LGNMELBEYwpk9e9fs/qZh4lamcqR8rCib2v6PfmOdRtjjDnJEo3JlwP7dzF35CBa\n/LyXVqmwqlNVuo39H2c2buV1aMaYIGOJxuSJLz2dmc8MpfrnC4lOhI2Ny1D34ccZcOEgr0MzxgQp\nSzQm13745GUOTX2ZqE3Kvqqw4fZL6H3vs4SEhHgdmjEmiFmiMae0afXvLBp7G1FLjhJRBpZd1IS+\n46ZTIaKK16EZY4oBSzQmW8lJh/j8iQE0+X4rbY5AXLtwOo+ezKBWeep9whhTylmiMX+jPh+fv3Q/\nFT/+mg57YEv9EI6PuJurrrjD69CMMcWQJRrzF79/M43tL46l1bp0DkRA3HX/oN9jU+w6jDEm3yzR\nlFKdx3zD/qTUk69rzXmdO3a+yhnLkmmmsKxbXS6dMI1/VM+q021jjMk9SzSl1Ikk05lV9Dv4AU3/\nOEL1P2FF87K0G/Usg868yOMIjTElhSWaUmxoyv84a8VKGu0QdtVS3u3SnvcibiTekowxphBZoimF\nVi6cy4R1w2m7Oo3kCsKOc49wbr3DLPJVhXSvozPGlDSWaEqRhL3b+Wbkv2jxyz5aH4c/2legZ4sd\n1CyTynHKsDAtyusQjTElkCWaUsCXns6n426i5uzf6XAANjQpw2utrmJlWGdm+tbRNS2Ohb4oFmtL\nr0M1xpRA1ma1hIud8Tyze7ej9bu/o8DGu/rQZ+4Kdtf8BwCLtSWvpPc9mWRqRpTzMFpjTElkRzQl\n1PrlC1gy/i6ilqZQqQwsv7gZ/cZ9QPmKEQAsGpFxwT82Npbu3bt7FKkxpqSzRFPC/Hkogdkjr6Hp\nj9tocwRWd4igy5gpdGoR7XVoxphSyhJNCaE+H589dw8Rn35Hh30Q3yCE9Jj7uPryW7wOzRhTylmi\nKQEWfvkWu195hsj1PhIrwZrB59H3/161bmOMMUHBEk0xtm3DSn4ePYSoP/6kCbDsn/XpNX46lavV\n8jo0Y4w5yRJNMZRy9AiznhxEo2/X0+FPiGsVRuuRkxh0RnevQzPGmL+xRFPMfDllBCHTPqb9Tthe\nRzg6bDBXXPd/XodljDHZskRTTCz9cRYbJz1Bq9XHSaoIq67uSL+Rb1KmrN33YowJbpZogtzenfF8\nF3MtLRcm0jINVnapQY8J79GlbmOvQzPGmFyxRBOkjqemMmvcYGp/uYQOB2F9s7I0Hv4UA8/r53Vo\nxhiTJ5ZogtB30yZy9M03aLNF2VMdNg/rz+V3PO11WMYYky+e32ghIjEiskNElrqPXu70i0TkDxFZ\n4f69IC/li6O1S39gxqBoaj/1OnV3K8t7teDsb3+nlyUZY0wxFixHNJNUdWKmafuBPqq6U0TaAl8B\nDfJQPiglTJ3K0hrJTEifze7k3dQNr8t9qd0pP20mlbb/SZsUWN2hEmePnUqn09t7Ha4xxhRYsCSa\nv1HVJX4vVwEVRKS8qh7zKqbCsLRGMhGjJ1O9Xwi7GodwxvztnPbdO4QqbG4Uiu++B7m6901eh2mM\nMYUmWBLNPSJyA7AIeFBVD2SafyWwOIckc6ryQWNC+myq9wvhgU98HAr30TABDoTDtEsq8vzo363b\nGGNMiSOqGvg3EZkH1M1i1uPAQpzTZAqMBuqp6hC/sm2Az4Ceqroxi3XXyal8pmVvBW4FqFWrVqcZ\nM2YUZLPy5aH1d9PvFx/9flFCFVaeBuMGhHK8rPBi4xeLPB6ApKQkIiIiPHnvYGN1kcHqIoPVRYbz\nzz//D1XtnJcyRZJocktEmgBfqGpb93VD4DvgJlX9Oa/lcxIZGalr164tSLh5ciT5MJ8/+S8afreR\n6kmQGgpfnyGct1KZ1D+ExNYN+Pqqr4ssHn82Hk0Gq4sMVhcZrC4yiEieE43np85EpJ6q7nJf9gdW\nutOrArOB4TklmezKBwv1+Zjz2qOUnfEZ7XfB7mqQXB4mXhnCqsYh/NHCx/0zfSS16+11qMYYExCe\nJxrgGRGJxjn1FQ/c5k6/G2gOjBSRke60nqq6V0SmApNVdVEO5T23JPYTNj8/iqi4NA6Fw6qBnfln\ng24sr3WUxPTZSPJuEls3IKldb6ITwr0O1xhjAsLzRKOq12czfQwwJpt5Q09V3kt7tm9mfsy1tPzt\nAM3TYfk/anHR+PfoWrsRAD2AHgzzNkhjjCkinieakiT1WAqznr6RunOX0+EQrG1eltMffZqB51zm\ndWjGGOMZSzSFZN6740h96x3ablN21RC2PHgV/W55yuuwjDHGc5ZoCijuj/msnHA/rVcc42h5WN6n\nFf1Hv0e5sIpeh2aMMUHBEk0+HUzYzZc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"text/plain": [
"<matplotlib.figure.Figure at 0x191f4128780>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def lplt():\n",
" j = numpy.arange(nc.variables['TLONG'].shape[0])\n",
" plt.plot(j+1, nc.variables['ULAT'][:].min(axis=-1),'s-',label='ULAT min');\n",
" plt.plot(j+1, nc.variables['ULAT'][:].max(axis=-1),'.-',label='ULAT max');\n",
" plt.plot(j+0.5, nc.variables['TLAT'][:].min(axis=-1),'o-',label='TLAT min');\n",
" plt.plot(j+0.5, nc.variables['TLAT'][:].max(axis=-1),'x-',label='TLAT max');\n",
" plt.legend(loc='upper left'); plt.xlabel('j'); plt.ylabel('Latitude ($^{\\circ}N$)')\n",
" plt.grid()\n",
"lplt(); plt.title('Meridional resolution range');\n",
"plt.figure();\n",
"lplt(); plt.xlim(184,190); plt.ylim(-1,1); plt.title('Equatorial meridional resolution');\n",
"plt.figure();\n",
"lplt(); plt.xlim(50,55); plt.ylim(-53,-50); plt.title('Southern Ocean meridional resolution');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Observations:\n",
"- Grid is 320 x 384 points (longitude x latitude)\n",
"- Nominally 1.125 degree zonal resolution\n",
"- NE indexing conventions since ULONG(0,0) is east of TLONG(0,0) and ULAT(0,0) is north of TLAT(0,0)\n",
" - this is the same as FMS and MOM6\n",
"- mesh has two singularities (poles) with northern singularity twisted away from north pole"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Construct a supergrid\n",
"\n",
"- This supergrid will be twice as fine as model grid so model cells can be constructed by combining four cells appropriately\n",
"- Data is symmetric so that supergrid mesh node locations are one row/column longer than cell areas"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Coarse mesh = 320 x 384, supergrid = 640 x 768\n"
]
}
],
"source": [
"nj,ni = nc.variables['TAREA'].shape\n",
"print('Coarse mesh = %i x %i, supergrid = %i x %i'%(ni,nj,2*ni,2*nj))\n",
"x = numpy.zeros((2*nj+1,2*ni+1))\n",
"y = numpy.zeros((2*nj+1,2*ni+1))\n",
"area = numpy.zeros((2*nj,2*ni))\n",
"dx = numpy.zeros((2*nj+1,2*ni))\n",
"dy = numpy.zeros((2*nj,2*ni+1))\n",
"# Fill in known values\n",
"x[1::2,1::2] = nc.variables['TLONG'][:,:]\n",
"y[1::2,1::2] = nc.variables['TLAT'][:,:]\n",
"x[2::2,2::2] = nc.variables['ULONG'][:,:]\n",
"y[2::2,2::2] = nc.variables['ULAT'][:,:]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Super grid node positions\n",
"\n",
"- The model T-cell centers and T corners correspond to data in the CESM grid file\n",
" - these are the [1::2,1::2] and [::2,::2] nodes in the supergrid\n",
"- The model u-point and v-point (C-grid nomenclature) are not porovided and need to be interpolated/extrapolated"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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KzslbdQNhbQ1i1XatMXl335f+PQg8hNYjiyEkj7SlA0aCI22LssSq7cqNvJmtMLOTu6+B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\nupMWVzYEAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x191f3f1d320>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot longitude for the four corners of mesh\n",
"plt.subplot(221); plt.pcolormesh(x[-19:,:20]); plt.colorbar(); # top-left\n",
"plt.subplot(222); plt.pcolormesh(x[-19:,-19:]); plt.colorbar(); # top-right\n",
"plt.subplot(223); plt.pcolormesh(x[:20,:20]); plt.colorbar(); # bottom-left\n",
"plt.subplot(224); plt.pcolormesh(x[:20,-19:]); plt.colorbar(); # bottom-right"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def av_longs(x0, x1):\n",
" \"\"\"Returns average of x0 and x1, taking into account periodicity\"\"\"\n",
" dx = numpy.abs( x1 - x0 )\n",
" xa = 0.5 * ( x0 + x1 )\n",
" xa[dx>180] = xa[dx>180] + 180.\n",
" return xa"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Apply zonal periodicity on q-point longitude\n",
"x[2::2,0] = x[2::2,-1]\n",
"# Meridionally average for v-point longitude\n",
"x[2:-1:2,1::2] = av_longs( x[1:-2:2,1::2] , x[3::2,1::2] )\n",
"# Longitude of southern edge by copying first q row\n",
"x[0,:] = x[2,:]\n",
"# Meridionally average for u-point longitude\n",
"x[1:-1:2,::2] = av_longs( x[:-2:2,::2] , x[2::2,::2] )\n",
"# Zonally average for top-row of v-point longitude\n",
"x[-1,1::2] = av_longs( x[-1,:-1:2] , x[-1,2::2])"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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FozqR1IyorjGrnwSOA7YADwAfG1ZGqZ/GlcAfmdkvsutqvuaVSGP0DSO737Vp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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x191f40f2be0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot longitude for the four corners of mesh\n",
"plt.subplot(221); plt.pcolormesh(x[-19:,:20]); plt.colorbar(); # top-left\n",
"plt.subplot(222); plt.pcolormesh(x[-19:,-19:]); plt.colorbar(); # top-right\n",
"plt.subplot(223); plt.pcolormesh(x[:20,:20]); plt.colorbar(); # bottom-left\n",
"plt.subplot(224); plt.pcolormesh(x[:20,-19:]); plt.colorbar(); # bottom-right"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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beycuBlbj8jQz+4OSa5WdRTlOMIlr2429E5961yI7TjokrG039k5cDKzeW13H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\ndxzHaQFu7B3HcVqAG3vHcZwW8P8BiT88XPgnn/8AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x191f67176a0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot latitude for the four corners of mesh\n",
"plt.figure()\n",
"plt.subplot(221); plt.pcolormesh(y[-19:,:20]); plt.colorbar(); # top-left\n",
"plt.subplot(222); plt.pcolormesh(y[-19:,-19:]); plt.colorbar(); # top-right\n",
"plt.subplot(223); plt.pcolormesh(y[:20,:20]); plt.colorbar(); # bottom-left\n",
"plt.subplot(224); plt.pcolormesh(y[:20,-19:]); plt.colorbar(); # bottom-right"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Apply zonal periodicity on latitude q-points\n",
"y[2::2,0] = y[2::2,-1]\n",
"# Longitudinally average for u-point latitude\n",
"y[1::2,2:-1:2] = 0.5*( y[1::2,1:-2:2] + y[1::2,3::2])\n",
"y[1::2,0] = 0.5*( y[1::2,1] + y[1::2,-2])\n",
"y[1::2,-1] = y[1::2,0]\n",
"# Longitudinally average for u-point latitude\n",
"y[::2,1:-1:2] = 0.5*( y[::2,:-2:2] + y[::2,2::2])\n",
"#y[::2,-2] = 0.5*( y[::2,-3] + y[::2,-1] )\n",
"# Latitude of southern edge q-points\n",
"y[0,:] = y[1,:] + ( y[1,:] - y[2,:] )"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"y max: 89.977342085\n"
]
},
{
"data": {
"image/png": 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2BPbwJa0ys93p14uAbXnnO04bM3GoxF5Q1jZw6bF/BFwNvJok5sjbzezAgPJc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\nDb7jOM6M4A2+4zjOjOANvuM4zozw/wHznAOWIcxOCAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x191f3d8cac8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot latitude for the four corners of mesh\n",
"plt.figure()\n",
"plt.subplot(221); plt.pcolormesh(y[-19:,:20]); plt.colorbar(); # top-left\n",
"plt.subplot(222); plt.pcolormesh(y[-19:,-19:]); plt.colorbar(); # top-right\n",
"plt.subplot(223); plt.pcolormesh(y[:20,:20]); plt.colorbar(); # bottom-left\n",
"plt.subplot(224); plt.pcolormesh(y[:20,-19:]); plt.colorbar(); # bottom-right\n",
"print('y max:',y.max())"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(-180, 180)"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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