{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Notebook for model runs and data comparisons.\n", "\n", "Data is from PSF/ONC." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import sys\n", "sys.path.append('/data/nsoontie/MEOPAR/mixing-paper/analysis')\n", "\n", "import netCDF4 as nc\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import datetime\n", "import matplotlib.dates as md\n", "from itertools import cycle\n", "import collections\n", "import pandas as pd\n", "import seaborn as sns\n", "\n", "from salishsea_tools import tidetools, nc_tools, teos_tools\n", "\n", "import ONC_patrols as onc\n", "import mixing\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sns.set_context('paper')\n", "sns.set_palette(sns.hls_palette(6, l=.3, s=.8))\n", "sns.set_style('whitegrid')\n", "sns.set_color_codes()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Load mesh and grid" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "grid_B = nc.Dataset('/data/nsoontie/MEOPAR/NEMO-forcing/grid/bathy_meter_SalishSea2.nc')\n", "mesh_mask = nc.Dataset('/data/nsoontie/MEOPAR/NEMO-forcing/grid/mesh_mask_SalishSea2.nc')\n", "bathy, lons, lats = tidetools.get_bathy_data(grid_B)\n", "\n", "tmask = mesh_mask.variables['tmask'][:]\n", "\n", "gdept = mesh_mask.variables['gdept'][0,:,:,:]\n", "gdepw = mesh_mask.variables['gdepw'][0,:,:,:]\n", "\n", "e3t = mesh_mask.variables['e3t'][0,:,:,:]\n", "e3w = mesh_mask.variables['e3w'][0,:,:,:]\n", "\n", "thalweg_points, gdept_thal, xx = mixing.load_thalweg(gdept, lons, lats)\n", "_, gdepw_thal, _ = mixing.load_thalweg(gdepw, lons, lats)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Load Files" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "start=datetime.datetime(2015,7,9)\n", "end=datetime.datetime(2015,8,17)\n", "home_dir = '/ocean/nsoontie/MEOPAR/SalishSea/results/mixing_paper/'" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "runs = ['base_jul', 'holl_jul', 'base_spinup/full_run', 'holl_spinup/full_run',]\n", "labels = {'base_jul': 'Base',\n", " 'background_eddy': 'Back. eddy',\n", " 'base_jul_10s': '$k-\\epsilon 10s$',\n", " 'horizontal': 'Hori.',\n", " 'kw': '$k-\\omega$',\n", " 'holl_jul': 'Holl. corr.',\n", " 'new_bcs': 'BCs',\n", " 'enst': 'enst',\n", " 'mixed': 'mixed',\n", " 'biharm_1e6': 'biharm_1e6',\n", " 'biharm_5e6': 'biharm_5e6',\n", " 'base_spinup/full_run': 'base_spinup',\n", " 'holl_spinup/full_run': 'holl_spinup'}\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "grid_t=collections.OrderedDict({})\n", "grid_w=collections.OrderedDict({})\n", "for run in runs:\n", " grid_t[run] = mixing.results_dataset('1d',start,end,'grid_T',home_dir,run)\n", " grid_w[run] = mixing.results_dataset('1d',start,end,'grid_W',home_dir,run)\n", "times = mixing.load_time(grid_t[run])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Plotting" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def compare_averages_in_depth(region, start, end, dranges, variables, home_dir, lons, lats, tmask, gdept, axs,\n", " labels):\n", " count=0 \n", " data = mixing.load_obs_region(region, start, end, composite=False)\n", " for run in runs:\n", " model_obs_data = mixing.compile_regional_model_casts(region, data, run, home_dir, \n", " lons, lats, 1-tmask, gdept )\n", " avg_depth, depth_data, std_data = mixing.compile_depth_ranges(dranges, model_obs_data ) \n", " if count==0:\n", " diff_sal = pd.DataFrame(index=avg_depth)\n", " diff_temp = pd.DataFrame(index=avg_depth)\n", " for variable, ax in zip(variables, axs):\n", " if variable=='vosaline':\n", " avg_model = teos_tools.psu_teos(np.array(depth_data['Model Salinity (psu)']))\n", " avg_obs = teos_tools.psu_teos(np.array(depth_data['Observed Salinity (psu)']))\n", " ax.set_xlabel('Salinity [g/kg]')\n", " diff_sal[run] = avg_model-avg_obs\n", " obs_errorbar = np.array(std_data['Observed Salinity (psu)'][:])\n", " mod_errorbar = np.array(std_data['Model Salinity (psu)'][:])\n", " elif variable == 'votemper':\n", " avg_model = np.array(depth_data['Model Temperature (C)'])\n", " avg_obs = np.array(depth_data['Observed Temperature (C)'])\n", " ax.set_xlabel('Temperature [deg C]')\n", " diff_temp[run] = avg_model-avg_obs\n", " obs_errorbar = np.array(std_data['Observed Temperature (C)'][:])\n", " mod_errorbar = np.array(std_data['Model Temperature (C)'][:])\n", " if count==0:\n", " ax.errorbar(avg_obs, avg_depth, xerr=obs_errorbar,fmt='o-', label= 'Obs', color='k', capthick=1,ms=5)\n", " ax.errorbar(avg_model, avg_depth, xerr=mod_errorbar, fmt='o-', label= labels[run], capthick=1,ms=5) \n", " count=count+1\n", " for ax in axs:\n", " ax.set_ylim([150,0])\n", " ax.set_ylabel('Depth [m]')\n", " #ax.set_title(region)\n", " \n", " return diff_sal, diff_temp" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/data/nsoontie/MEOPAR/mixing-paper/analysis/ONC_patrols.py:130: FutureWarning: convert_objects is deprecated. Use the data-type specific converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.\n", " data = data.convert_objects(convert_numeric=True)\n" ] }, { "data": { "image/png": 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ynT/xeHOZ+n83o9VOW/xkoVTo3A1x8xq7qreg3L38tNHaxeXXr7BxnEvBOU40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\nmIvaqJelN5T6pOVmtrZoGhtJXLoY+57aQ3f5FWX65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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# only August data\n", "start = start=datetime.datetime(2015,8,1)\n", "\n", "# depth bins\n", "d1s = np.arange(0,160,20)\n", "d2s = np.arange(20,180,20)\n", "dranges = [(d1,d2) for d1,d2 in zip(d1s,d2s)]\n", "\n", "variables = ['vosaline', 'votemper']\n", "fig_comp, axs_comp = plt.subplots(2,2,figsize=(5,5.5), sharey=True)\n", "axs_vic = [axs_comp[0,0], axs_comp[0,1]]\n", "axs_stv = [axs_comp[1,0], axs_comp[1,1]]\n", "\n", "\n", "region = 'Victoria'\n", "Vic_sal, Vic_temp = compare_averages_in_depth(region, start, end, dranges, variables, home_dir, lons, \n", " lats, tmask, gdept, axs_vic, labels)\n", "region = 'Steveston'\n", "Stv_sal, Stv_tmep = compare_averages_in_depth(region, start, end, dranges, variables, home_dir, lons,\n", " lats, tmask, gdept, axs_stv, labels)\n", "\n", "\n", "axs_comp[1,0].legend(loc=0)\n", "ls = ['(a)', '(b)','(c)','(d)']\n", "xs = [30.6, 7.7, 25.4, 9.2]\n", "for ax, l ,x, in zip (axs_comp[:].flatten(), ls, xs):\n", " ax.text(x, 20, l)\n", "axs_comp[0,1].text(12.5,50,'Victoria',rotation=90)\n", "axs_comp[1,1].text(19.5,50,'Steveston',rotation=90)\n", "for ax in axs_comp[:].flatten():\n", " plt.setp( ax.xaxis.get_majorticklabels(), rotation=30 )\n", "plt.tight_layout(pad=0.4, h_pad=.8)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "base_jul -0.591968\n", "holl_jul -0.381121\n", "base_spinup/full_run -0.664913\n", "holl_spinup/full_run -0.361155\n", "dtype: float64" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Vic_sal.mean()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "base_jul 0.324808\n", "holl_jul -0.113352\n", "base_spinup/full_run 0.488724\n", "holl_spinup/full_run 0.049950\n", "dtype: float64" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Vic_temp.mean()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "base_jul -0.469837\n", "holl_jul -0.293500\n", "base_spinup/full_run -0.776217\n", "holl_spinup/full_run -0.261467\n", "dtype: float64" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Stv_sal.mean()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "base_jul 0.369259\n", "holl_jul 0.236692\n", "base_spinup/full_run 1.345052\n", "holl_spinup/full_run 0.864407\n", "dtype: float64" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Stv_tmep.mean()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Compared to Hollingsworth correction, mixed and enst are a little bit worse in SoG intermeditate, but a little bit better in Victoria. \n", "\n", "The improvement in Victoria comes mostly from the surface. " ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.savefig('spinup_data_compare.png',dpi=300,bbox_inches='tight')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }