nathanieltarshish/ssh_eddy_identify.ipynb

## gistfile1.txt
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "This document outlines an example of how I identified and tested the coherency of a sample Eulerian SSH eddy in CM2.6 by seeding the interior with virtual Lagrangian particles and generating a float file for MITgcm advection. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "from matplotlib import pyplot as plt\n",
    "import matplotlib\n",
    "%matplotlib inline\n",
    "matplotlib.style.use('ggplot')\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import netCDF4 as nc\n",
    "import xarray as xr\n",
    "import pandas as pd\n",
    "import sys\n",
    "sys.path.append('/Users/ntarshish/Documents/floater/')\n",
    "from floater import generators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (lat: 2700, lon: 3600)\n",
       "Coordinates:\n",
       "    time       object  202-01-01 12:00:00\n",
       "  * lon        (lon) float64 -279.9 -279.8 -279.7 -279.6 -279.5 -279.4 ...\n",
       "  * lat        (lat) float64 -81.11 -81.07 -81.02 -80.98 -80.94 -80.9 -80.86 ...\n",
       "Data variables:\n",
       "    sea_level  (lat, lon) float64 nan nan nan nan nan nan nan nan nan nan ...\n",
       "Attributes:\n",
       "    filename: 02020101.ocean_minibling_surf_field.nc\n",
       "    title: CM2.6_miniBling\n",
       "    grid_type: mosaic\n",
       "    grid_tile: 1\n",
       "    history: Thu Jan 12 18:13:11 2017: ncks -d time,,2 sea_level_2020.nc sea_level_sample.nc\n",
       "Thu Jan 12 18:05:41 2017: ncks -v sea_level /archive/Richard.Slater/CM2.6/CM2.6_A_Control-1860_V03/history/02020101.ocean_minibling_surf_field.nc /archive/net/sea_level_2020.nc\n",
       "    NCO: 4.0.3"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = xr.open_dataset('sea_level_sample.nc') #open SSH file  \n",
    "ds = ds.rename({'xt_ocean':'lon', 'yt_ocean':'lat'}) #rename variables\n",
    "ds['sea_level'] = ds.sea_level.isel(time=0) #select single time\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example, we will select a Gulf Stream ring. Here is the SSH field in that region."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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8l2FKDXautgqTrrxCmJdK7vFee3L7vG0WnKj3TB2sT0YVJRYJRT8GzIrjeRrg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\naTp3tQ3l5CpLYgqS0shbNAqZa6rntS8XFvEeZVPifeiph9Dstf8cMWW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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10db65f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_region = {'lat': slice(15,45), 'lon': slice(-85,-40)}\n",
    "plt.figure(figsize=(20,15))\n",
    "ds.sea_level.sel(**sample_region).plot(cmap='seismic')\n",
    "plt.title(\"SSH Field\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Several rings are apparent, for example at (-69, 31) there is a strong SSH field anomaly. Zooming in on this SSH structure,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c965a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eddy_zoom = {'lat': slice(30, 33, None), 'lon': slice(-71, -68, None)}\n",
    "plt.figure(figsize=(10*3,10*3))\n",
    "ds.sea_level.sel(**eddy_zoom).plot(cmap='seismic', vmax=1, vmin=-1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we will identify the boundary and create an \"eddy mask\". The mask will be equal to 1 in the interior and equal to 0 outside the eddy. Chelton assumes that there is 1 cm background noise in the SSH field. Therefore, we will equate the interior of the eddy with the region where `sea_level < .01`. As described in an eariler email, we are selecting a \"generic\" SSH eddy that we expect any reasonable SSH algorithm would identify (although the exact boundary may vary from id. method to method)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sl_array = ds.sea_level.sel(**eddy_zoom).values #2d numpy array of sea_level in eddy region\n",
    "sl_flat = sl_array.flatten() #flatten\n",
    "sl_flat[sl_flat > .01] = 0 #exterior to eddy \n",
    "sl_flat[sl_flat != 0] = 1  #interior of eddy \n",
    "eddy_mask = sl_flat.reshape(sl_array.shape[0], sl_array.shape[1]) #unflatten "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is a visualization of the generated `eddy_mask`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x10db65910>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iCACIIgAgigCA5vgmqHv37pXL5ZJlWVqwYIGOHz+eqFwAUmhORWBZloLBoJYsWZKoPAAc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\nXD6sPAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b96c210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(eddy_mask, cmap='bwr_r', origin=\"Left\") #the eddy mask"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we wish to seed the interior with virtual particles. To do this, we use the `floater` package. We can exploit the `model_grid` feature to only place Lagrangian particles in the eddy interior by having the `eddy_mask` masquerade as a `land_mask`.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#building the eddy_grid from lon, lat, and eddy_mask\n",
    "eddy_zoom_lon = {'lon': slice(-71, -68)}\n",
    "eddy_zoom_lat = {'lat': slice(30, 33)}\n",
    "eddy_grid_lon = ds.lon.sel(**eddy_zoom_lon).values\n",
    "eddy_grid_lat = ds.lat.sel(**eddy_zoom_lat).values \n",
    "eddy_grid = {'lon': eddy_grid_lon, 'lat': eddy_grid_lat, 'land_mask': eddy_mask}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we create a `FloatSet` with 1/32 particle resolution using the `eddy_grid`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fs = generators.FloatSet(xlim=(-71.0, -68.0), ylim=(30,33), dx=.03125, dy=.03125,\n",
    "                         model_grid=eddy_grid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is a plot to verify the particle placement: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1044cc0d0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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X2MVegRNEqwEAAKBWGPgAAAAMx8AHAABgOAY+AAAAw5G0AXyPpA16JmnDvHXc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\nC4E0hhNNQV0uAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1044ea190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "floats = pd.DataFrame(np.array(fs.get_rectmesh()).T, columns=['lon','lat'])\n",
    "floats.plot(kind='scatter', x='lon', y='lat', figsize=(10,10), \n",
    "            title='Lagrangian Virtual Particle Deployment')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we write the particle locations out to a float file (MITgcm input file for offline advection)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fs.to_mitgcm_format(\"flt_eddy_32bit.bin\", mesh=\"rect\",read_binary_prec=32)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [scienv]",
   "language": "python",
   "name": "Python [scienv]"
  },
  "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": 0
}

## ssh_eddy_identify.ipynb

      
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