{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "A notebook to compare the Froude number between 2d and 3D runs. " ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import netCDF4 as nc\n", "import datetime\n", "import os\n", "import numpy as np\n", "\n", "import froude\n", "from salishsea_tools.nowcast import analyze\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Load data" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def load_simulation(path, period, d1, d2, n2=True):\n", " \n", " for grid in ['grid_T', 'grid_U', 'grid_W', 'grid_V']:\n", " filename = 'SalishSea_{}_{}_{}_{}.nc'.format(period,d1.strftime('%Y%m%d'), d2.strftime('%Y%m%d'),grid)\n", " print os.path.join(path,filename)\n", " d = nc.Dataset(os.path.join(path,filename))\n", " if grid=='grid_T':\n", " sal = d.variables['vosaline'][:]\n", " sal = np.ma.masked_values(sal,0)\n", " depsT = d.variables['deptht'][:]\n", " temp = d.variables['votemper'][:]\n", " temp = np.ma.masked_values(temp,0)\n", " ssh = d.variables['sossheig'][:]\n", " if n2:\n", " n2 = d.variables['buoy_n2'][:]\n", " n2 = np.ma.masked_values(n2,0)\n", " else: \n", " n2=np.zeros(temp.shape)\n", " times = d.variables['time_counter'][:]\n", " try :\n", " time_origin = datetime.datetime.strptime(d.variables['time_counter'].time_origin, \n", " '%Y-%m-%d %H:%M:%S')\n", " except :\n", " time_origin = datetime.datetime.strptime(d.variables['time_counter'].time_origin, \n", " ' %Y-%b-%d %H:%M:%S')\n", "\n", " if grid =='grid_U':\n", " U = d.variables['vozocrtx'][:]\n", " U = np.ma.masked_values(U,0)\n", " depsU=d.variables['depthu'][:]\n", "\n", " if grid=='grid_W':\n", " try:\n", " avt = d.variables['vert_eddy_diff'][:]\n", " avm = d.variables['vert_eddy_visc'][:]\n", " except KeyError:\n", " avt = d.variables['ve_eddy_diff'][:]\n", " avm = d.variables['ve_eddy_visc'][:]\n", " avm = np.ma.masked_values(avm,0)\n", " avt = np.ma.masked_values(avt,0)\n", " depsW=d.variables['depthw'][:]\n", "\n", " if grid=='grid_V':\n", " V = d.variables['vomecrty'][:]\n", " V= np.ma.masked_values(V,0)\n", " depsV=d.variables['depthv'][:]\n", "\n", " \n", " return sal, temp, ssh, n2, depsT, U, depsU, avt,avm, depsW, V, depsV, times, time_origin" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "paths = {'2D': '/data/nsoontie/MEOPAR/SalishSea/results/2Ddomain/3.6/base_aug/',\n", " '3D': '/data/nsoontie/MEOPAR/SalishSea/results/stratification/dwr_diff1e-6_visc1e-5/'}\n", "period = '1d'\n", "d1 = datetime.datetime(2003,8,19)\n", "d2 = datetime.datetime(2003,9,27)\n", "\n", "n2_flag = {'2D': True,\n", " '3D': False}" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/data/nsoontie/MEOPAR/SalishSea/results/2Ddomain/3.6/base_aug/SalishSea_1d_20030819_20030927_grid_T.nc\n", "/data/nsoontie/MEOPAR/SalishSea/results/2Ddomain/3.6/base_aug/SalishSea_1d_20030819_20030927_grid_U.nc\n", "/data/nsoontie/MEOPAR/SalishSea/results/2Ddomain/3.6/base_aug/SalishSea_1d_20030819_20030927_grid_W.nc\n", "/data/nsoontie/MEOPAR/SalishSea/results/2Ddomain/3.6/base_aug/SalishSea_1d_20030819_20030927_grid_V.nc\n", "/data/nsoontie/MEOPAR/SalishSea/results/stratification/dwr_diff1e-6_visc1e-5/SalishSea_1d_20030819_20030927_grid_T.nc\n", "/data/nsoontie/MEOPAR/SalishSea/results/stratification/dwr_diff1e-6_visc1e-5/SalishSea_1d_20030819_20030927_grid_U.nc\n", "/data/nsoontie/MEOPAR/SalishSea/results/stratification/dwr_diff1e-6_visc1e-5/SalishSea_1d_20030819_20030927_grid_W.nc\n", "/data/nsoontie/MEOPAR/SalishSea/results/stratification/dwr_diff1e-6_visc1e-5/SalishSea_1d_20030819_20030927_grid_V.nc\n" ] } ], "source": [ "sals={}; temps={}; sshs={};n2s={}; depsTs={}; Us={}; depsUs={} \n", "avts={}; avms={}; depsWs={}; Vs={}; depsVs={}; times={}; time_origins={};\n", "\n", "for key in paths:\n", " (sals[key], temps[key], sshs[key], n2s[key], depsTs[key], \n", " Us[key], depsUs[key], avts[key],avms[key], depsWs[key], Vs[key], depsVs[key], times[key], \n", " time_origins[key])= load_simulation(paths[key],period,d1,d2,n2_flag[key])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Slice 2D data" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": true }, "outputs": [], "source": [ "yslice=5\n", "key='2D'\n", "n2s[key] = n2s[key][:,:,yslice,:]\n", "temps[key] = temps[key][:,:,yslice,:]\n", "sals[key] = sals[key][:,:,yslice,:]\n", "Us[key] = Us[key][:,:,yslice,:]\n", "Vs[key] = Vs[key][:,:,yslice,:]\n", "avts[key] = avts[key][:,:,yslice,:]\n", "avms[key] = avms[key][:,:,yslice,:]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Isolate Thalweg in 3D\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "lines = np.loadtxt('/data/nsoontie/MEOPAR/tools/bathymetry/thalweg_working.txt', delimiter=\" \")\n", "lines = lines.astype(int)\n", "key='3D'\n", "sals[key] = sals[key][:,:,lines[:,0], lines[:,1]]\n", "temps[key] = temps[key][:,:,lines[:,0], lines[:,1]]\n", "avts[key] = avts[key][:,:,lines[:,0], lines[:,1]]\n", "avms[key] = avms[key][:,:,lines[:,0], lines[:,1]]" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "Us[key] = 0.5*(Us[key][:,:,lines[:,0], lines[:,1]] + Us[key][:,:,lines[:,0], lines[:,1]-1])\n", "Vs[key] = 0.5*(Vs[key][:,:,lines[:,0], lines[:,1]] + Vs[key][:,:,lines[:,0]-1, lines[:,1]])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3D buoyancy frequency" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mesh = nc.Dataset('/ocean/nsoontie/MEOPAR/Ariane/mesh_mask.nc')\n", "e3w = mesh.variables['e3w'][:]\n", "e3w = e3w[0, :, lines[:,0], lines[:,1]]\n", "\n", "\n", "n2s[key] = froude.calculate_buoyancy_frequency(temps[key], sals[key], e3w.T, depth_axis=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Quick plot of 3D to see if I can average over same region." ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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3dzKpp4mpIO3Rpr29OA07ljPBdDNGlCfmiYqmrNeeyrKEmQou9gShmMFRJW+0ce9iZs8x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\nAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "key='3D'\n", "nmin=0; nmax=0.01;\n", "t=39\n", "plt.pcolormesh(np.arange(n2s[key].shape[-1]), depsTs[key], n2s[key][t,:,:],vmin=nmin,vmax=nmax)\n", "plt.colorbar()\n", "plt.axis([0,1100,420,0])\n", "plt.title('N2 - {}'.format(key))" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "key='2D'\n", "t=39\n", "plt.pcolormesh(np.arange(n2s[key].shape[-1]), depsTs[key], n2s[key][t,:,:], vmin=nmin,vmax=nmax)\n", "plt.colorbar()\n", "plt.axis([0,1100,420,0])\n", "plt.title('N2 - {}'.format(key))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2D case more strongly stratified, hence less mixing." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Froude numbers" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [], "source": [ "\n", "Frs={}; cs={}; uavgs = {}; dates={};rhos = {}\n", "for key in paths:\n", " rhos[key] = froude.calculate_density(temps[key], sals[key])\n", " Frs[key], cs[key], uavgs[key], dates[key] = froude.froude_time_series(\n", " n2s[key], rhos[key], Us[key], depsTs[key], depsUs[key],\n", " times[key], time_origins[key])" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": true }, "outputs": [], "source": [ "xmin=300\n", "xmax=700" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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39VUA10qaKOnAmkZWJ+7/Lxbno1icj2JxPorHOSmWRudjaB2fe7B9oZtFxDNZ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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axs = plt.subplots(3,1,sharex=True, figsize=(10,10))\n", "for key in paths:\n", " ax=axs[0]\n", " ax.plot(dates[key],Frs[key], label=key)\n", " ax=axs[1]\n", " avts_dep_avg = np.ma.mean(analyze.depth_average(avts[key][:,1:,xmin:xmax+1],depsWs[key][1:],1),axis=1)\n", " ax.plot(dates[key],avts_dep_avg,label=key)\n", " ax=axs[2]\n", " avms_dep_avg = np.ma.mean(analyze.depth_average(avms[key][:,1:,xmin:xmax+1],depsWs[key][1:],1),axis=1)\n", " ax.plot(dates[key],avms_dep_avg,label=key)\n", " \n", "ax=axs[0]\n", "ax.legend(loc=0)\n", "ax.grid()\n", "ax.set_ylabel('Fr')\n", "\n", "ax=axs[1]\n", "ax.legend(loc=0)\n", "ax.grid()\n", "ax.set_ylabel('Vertical diff')\n", "\n", "ax=axs[2]\n", "ax.legend(loc=0)\n", "ax.grid()\n", "ax.set_ylabel('Vertical visc')\n", "fig.autofmt_xdate()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Froude number suggests more mixing in 3D but the vertical eddy coefficients are much smaller (an order of magnitude). Is this an artifact of 3.6 vs 3.4? \n", "\n", "* 3.6 used backgound visc/diff 1e-4 and 1e-6\n", "* 3.4 used background visc/diff 1e-5 and 1e-6\n", "* Both use the k-eps. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Diffusivities" ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "key='3D'\n", "dmin=0; dmax=10;\n", "t=39\n", "plt.pcolormesh(np.arange(avts[key].shape[-1]), depsTs[key], avts[key][t,:,:],vmin=dmin,vmax=dmax,cmap='hot')\n", "plt.colorbar()\n", "plt.axis([0,1100,420,0])\n", "plt.title('Vertical diff - {}'.format(key))" ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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AnQFDmae/cTerPi9BZWg8YaF8M8MOLjDuQ/lWwR0FHIOvrDAdGGlmBb9msWZY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\n1tAM79NwD2PdJA4FNh7v4T8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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "key='2D'\n", "t=39\n", "plt.pcolormesh(np.arange(avts[key].shape[-1]), depsTs[key], avts[key][t,:,:],vmin=dmin,vmax=dmax,cmap='hot')\n", "plt.colorbar()\n", "plt.axis([0,1100,420,0])\n", "plt.title('Vertical diff - {}'.format(key))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "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": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }