{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Vertical mixing with numeric inputs\n", "\n", "In the preceeding example, we looked at vertical mixing using an analytic diffusivity profile. The use of an analytic profile meant that we didn't have to interpolate between discrete data points when computing either the vertical eddy diffusivity or any of its derivatives. In more realisitic applications - for example, involving the outputs of a numerical model - this is not the case. Rather, we must interpolate between discrete data points to particle positions in order to obtain the data needed to compute changes in a particle's position. Mathematically, there are many different ways to interpolate between discrete data points, and the choice of interpolation scheme can have a marked effect on the model outputs. Furthermore, the result is also influenced by the number of particles used in the simulation, and the resolution of the vertical grid on which input data are defined. In this example, we investigate these dependencies using outputs from the [General Ocean Turbulence Model (*GOTM*)](https://github.com/gotm-model). The example draws some of its inspiration from the work of Grawe et al (2012), who assessed the merits of using numerical integration schemes of varying order.\n", "\n", "The *GOTM* outputs used have been pre-generated and made available for download. In total, four *GOTM* output files are needed to run the experiments described here. These correspond to model runs that use a varying number of vertical layers, but in other ways are identical. The outputs can be [downloaded here](https://drive.google.com/open?id=15UX7Y9JnuLpnPAz700mzmzd917nTClxR). If you would like to run the code in this notebook interactively, download the data into a directory of your chooising. By default, the notebook will look for these files in the directory `${HOME}/data/pylag_doc`. To change this, simply update the *data_dir* path below." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", "\n", "# Root directory for PyLag example input files\n", "data_dir='{}/data/pylag_doc'.format(os.environ['HOME'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Background\n", "\n", "In this example, we will compare outputs from the particle tracking model with those of a Eulerian model that simulates the vertical mixing of a single passive tracer or \"dye\" in 1D. For this, we use the DYE module from the [Framework for Aquatic Biogeochemical Models (*FABM*)](https://github.com/fabm-model), coupled to *GOTM*. We use a *GOTM* configuration for Station L4, which forms part of the [Western Channel Observatory (WCO)](https://www.westernchannelobservatory.org.uk/). The site has a depth of appriximately 50 m. We configure *FABM-GOTM* to release the dye at a point in space near to the ocean's surface and a time in Summer when the water column is stratified. Below, we plot the temporal evolution of the eddy diffusivity field and tracer concentration as simulated using two *GOTM* configurations: one using 20 vertical layers and a second using 200 vertical layers. The data are plotted for a single week at the end of June in 2010. When *GOTM* was run, data were saved every 30 minutes." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib\n", "from matplotlib import pyplot as plt\n", "from matplotlib import colors\n", "import matplotlib.dates as mdates\n", "from datetime import datetime\n", "\n", "from pylag.processing.plot import colourmap\n", "from pylag.processing.plot import GOTMPlotter\n", "\n", "# Ensure inline plotting\n", "%matplotlib inline\n", "\n", "\n", "def create_hovmoller_diagram(ax, gotm_file_name, var_name, vmin, vmax,\n", " cmap='jet', add_colorbar=False, cb_label=None):\n", " \"\"\" Helper plotting function \n", "\n", " \"\"\"\n", " norm = colors.LogNorm(vmin=vmin, vmax=vmax)\n", " plotter = GOTMPlotter(gotm_file_name, fs=font_size, time_rounding=gotm_time_rounding)\n", " plotter.hovmoller(ax, var_name, add_colorbar=add_colorbar, cb_label=cb_label,\n", " norm=norm, cmap=cmap)\n", " ax.set_xlim(date_start, date_end)\n", " ax.set_ylim(zmin, zmax)\n", " return\n", "\n", "# Start and end dates\n", "date_start = datetime(2010, 6, 25)\n", "date_end = datetime(2010, 7, 1)\n", "\n", "# Time rounding\n", "gotm_time_rounding = 1800.\n", "\n", "# Depth lims\n", "zmin = -50.0\n", "zmax = 0.0\n", "\n", "# Create figure\n", "font_size=12\n", "figsize = (12, 8)\n", "fig, ax_arr = plt.subplots(2, 2, figsize=figsize)\n", "fig.set_facecolor('white')\n", "\n", "for ax in ax_arr.ravel():\n", " plt.setp(ax.get_xticklabels(), fontsize=font_size)\n", " plt.setp(ax.get_yticklabels(), fontsize=font_size)\n", "\n", "# Plot heat diffusivity for 20 z levels\n", "file_name = '{}/gotm_l4_20_level.nc'.format(data_dir)\n", "create_hovmoller_diagram(ax_arr[0, 0], file_name, 'nuh', vmin=1.e-5, vmax=0.1,\n", " cmap=colourmap('nuh'), add_colorbar=False)\n", "ax_arr[0, 0].set_title('N$_{\\mathrm{Z}}$ = 20', fontsize=font_size)\n", "ax_arr[0, 0].set_xlabel('', fontsize=font_size)\n", "ax_arr[0, 0].set_ylabel('Depth (m)', fontsize=font_size)\n", "ax_arr[0, 0].set_xticklabels([])\n", "\n", "# Plot heat diffusivity for 200 z levels\n", "file_name = '{}/gotm_l4_200_level.nc'.format(data_dir)\n", "create_hovmoller_diagram(ax_arr[0, 1], file_name, 'nuh', vmin=1.e-5, vmax=0.1,\n", " cmap=colourmap('nuh'), add_colorbar=True,\n", " cb_label='Heat diffusivity (m$^{2}$ s$^{-1}$)')\n", "ax_arr[0, 1].set_title('N$_{\\mathrm{Z}}$ = 200', fontsize=font_size)\n", "ax_arr[0, 1].set_xlabel('', fontsize=font_size)\n", "ax_arr[0, 1].set_ylabel('', fontsize=font_size)\n", "ax_arr[0, 1].set_xticklabels([])\n", "ax_arr[0, 1].set_yticklabels([])\n", "\n", "# Plot tracer_c for 20 z levels\n", "file_name = '{}/gotm_l4_20_level.nc'.format(data_dir)\n", "create_hovmoller_diagram(ax_arr[1, 0], file_name, 'tracer_c', vmin=0.001, vmax=0.1,\n", " cmap=colourmap('tracer1_c'), add_colorbar=False)\n", "ax_arr[1, 0].set_ylabel('Depth (m)', fontsize=font_size)\n", "ax_arr[1, 0].set_xlabel('Date', fontsize=font_size)\n", "ax_arr[1, 0].xaxis.set_major_locator(mdates.DayLocator())\n", "ax_arr[1, 0].xaxis.set_major_formatter(mdates.DateFormatter('%d %b'))\n", "for tick in ax_arr[1, 0].get_xticklabels():\n", " tick.set_rotation(45)\n", "\n", "# Plot tracer_c for 200 z levels\n", "file_name = '{}/gotm_l4_200_level.nc'.format(data_dir)\n", "create_hovmoller_diagram(ax_arr[1, 1], file_name, 'tracer_c', vmin=0.001, vmax=0.1,\n", " cmap=colourmap('tracer1_c'), add_colorbar=True,\n", " cb_label='Tracer (mg m$^{-3}$)')\n", "ax_arr[1, 1].set_ylabel('', fontsize=font_size)\n", "ax_arr[1, 1].set_xlabel('Date', fontsize=font_size)\n", "ax_arr[1, 1].xaxis.set_major_locator(mdates.DayLocator())\n", "ax_arr[1, 1].xaxis.set_major_formatter(mdates.DateFormatter('%d %b'))\n", "for tick in ax_arr[1, 1].get_xticklabels():\n", " tick.set_rotation(45)\n", "labels = ax_arr[1, 1].set_yticklabels([])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is clear from the top two panels that the water column is strongly stratified during this time, with a low diffusivity region separating top and bottom layers that are characterised by high levels of mixing. The periodicity evident in the bottom mixed layer reflects the action of the tides. In the bottom two figures, it can be seen the tracer exhibits a clear partitioning, with the majority persisting in the surface mixed layer while only small quantities have managed to mix down into deeper layers. The tracer was injected into the surface when the simulation started on 1st June 2010, at a time when the water column was also stratified. Over the month of the simulation, it has been effectively mixed within the surface layers, but under stratified conditions, it has not mixed throughout the water column.\n", "\n", "Here, we will investigate how well the particle tracking model reproduces the tracer profile above and its temporal evolution. In so doing, we will investigate how differences between the two fields depend on a) the resolution of the vertical grid on which the input data are defined, b) the number of particles used in the particle release experiments, and c) the vertical interpolation scheme. For this, we will work with the 1D Milstein numerical approximation. For a similar investigation that uses higher order numerical approximations, the reader is referred to Grawe et al (2012).\n", "\n", "For this example, we will run *PyLag* in a slightly different mode to the previous examples, which used analytic models encoded within mock `DataReaders` to drive *PyLag*. Here, we will launch a full *PyLag* simulation from the command line, with run confiuration options defined in a dedicated run configuration file; particle initial positions defined in a dedicated initial positions data file; and driving data read in from disk." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setting particle initial positions\n", "\n", "We will create initial position files in which to record [particle initial positions](../documentation/getting_started.ipynb). These will be read in by *PyLag*, and used to create the initial particle seed in each run. In total, four initial position files are created for four different GOTM configurations. The GOTM configurations are distinguished by the number of vertical levels used. These were 20, 50, 100, and 200 vertical levels. In each case, particles are released from the middle of the top-most cell at the start of the simulation. The release location corresponds to the same point in space where the Eulerian tracer is released in the GOTM-FABM runs. By default, in order to keep the runs relatively short, just 100 particles are used in each simulation." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "from pylag.processing.input import create_initial_positions_file_single_group\n", "\n", "# Keep a copy of the cwd\n", "cwd = os.getcwd()\n", "\n", "# Initial properties (other than z) of test particles\n", "group_id = 0\n", "x_0 = 0.0\n", "y_0 = 0.0\n", "\n", "# Create a dictionary of (key, value) pairs in which the `keys' are the number \n", "# of z levels used in the GOTM simulation and the `values' are the depths of \n", "# the top most z layer. Depths are defined at the middle of the depth layer\n", "# and are taken from the corresponding GOTM output file \n", "z_0s = {'20': -1.25, '50': -0.5, '100': -0.25, '200': -0.125}\n", "\n", "# The number of particles in the particle seed\n", "n_particles = 100\n", "\n", "# Create a separate run directory and input file for each GOTM simulation\n", "for n_z_levels, z_0 in z_0s.items():\n", " # Create run directory\n", " simulation_dir = '{}/simulations/gotm_{}_z_levels'.format(cwd, n_z_levels)\n", " try:\n", " os.makedirs(simulation_dir)\n", " except FileExistsError:\n", " pass\n", "\n", " # Create input sub-directory\n", " input_dir = '{}/input'.format(simulation_dir)\n", " try:\n", " os.makedirs(input_dir)\n", " except FileExistsError:\n", " pass\n", " \n", " # Crate the particle set\n", " x = []\n", " y = []\n", " z = []\n", " for i in range(n_particles):\n", " x.append(x_0)\n", " y.append(y_0)\n", " z.append(z_0)\n", "\n", " # Create the initial positions file\n", " file_name = '{}/initial_positions.dat'.format(input_dir)\n", " create_initial_positions_file_single_group(file_name, n_particles, group_id, x, y, z)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Creating the run configuration files\n", "\n", "When running applied examples, run configuration parameters are typically set in a [run configuration](../documentation/getting_started.ipynb) file. Examples of *PyLag* run configuration files can be found in PyLag's resources directory, which ships with the code. For this example, a template GOTM run configuration file has been provided. The file includes lots of comments, so we won't print it's full contents here. Instead, we will use configparser to look at a few settings specific to the example. First, we read in the file, then we print out some of the key options for the run." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Start time: 2010-06-01 00:00:00\n", "End time: 2010-07-01 00:00:00\n", "Number of particle releases: 1\n", "Model name: GOTM\n", "Data directory: \n", "Path to grid metrics file: \n", "File name stem of input files: \n", "Numerical method: standard\n", "Iterative method: Diff_Milstein_1D\n", "Time step: 5 s\n", "Vertical interpolation: linear\n" ] } ], "source": [ "import configparser\n", "\n", "config_file_name = './configs/gotm_template.cfg'\n", "\n", "cf = configparser.ConfigParser()\n", "cf.read(config_file_name)\n", "\n", "# Start time\n", "print('Start time: {}'.format(cf.get('SIMULATION', 'start_datetime')))\n", "\n", "# End time\n", "print('End time: {}'.format(cf.get('SIMULATION', 'end_datetime')))\n", "\n", "# We will do a single run, rather than an ensemble run\n", "print('Number of particle releases: {}'.format(cf.get('SIMULATION', 'number_of_particle_releases')))\n", "\n", "# Specify that we are working with FVCOM \n", "print('Model name: {}'.format(cf.get('OCEAN_DATA', 'name')))\n", "\n", "# Set the location of the grid metrics and input files\n", "print('Data directory: {}'.format(cf.get('OCEAN_DATA', 'data_dir')))\n", "print('Path to grid metrics file: {}'.format(cf.get('OCEAN_DATA', 'grid_metrics_file')))\n", "print('File name stem of input files: {}'.format(cf.get('OCEAN_DATA', 'data_file_stem')))\n", " \n", "# Use the Milstein diffusive mixing scheme in 1D with a five second time step\n", "print('Numerical method: {}'.format(cf.get('NUMERICS', 'num_method')))\n", "print('Iterative method: {}'.format(cf.get('NUMERICS', 'iterative_method')))\n", "print('Time step: {} s'.format(cf.get('NUMERICS', 'time_step_diff')))\n", "\n", "# Use a linear interpolation scheme\n", "print('Vertical interpolation: {}'.format(cf.get('OCEAN_DATA', 'vertical_interpolation_scheme')))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From the above options, it can be seen we have not yet specified a directory within which the model can find *GOTM* output files. We will set this using `data_dir`, and use the file stem specific to each *GOTM* run to identify the required input file. We have not specified a [grid metrics](../documentation/overview.ipynb) file either. The grid metrics file is used to store information about the model grid, which saves computing the same information on the fly. However, it carries less importance with GOTM, which is a 1D model, and we can get away with simply pointing *PyLag* at a *GOTM* output file. All other paths are relative, and can be left as they are. It can be seen the model is run for a full month; however, we will only examine outputs from the final week of the simulation, as shown above. We will save the new config files in their respective simulation directories." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# Create a separate run configuration file for each GOTM simulation\n", "for n_z_levels in z_0s.keys():\n", " \n", " cf.set('OCEAN_DATA', 'data_dir', data_dir)\n", "\n", " data_file_stem = 'gotm_l4_{}'.format(n_z_levels)\n", " cf.set('OCEAN_DATA', 'data_file_stem', data_file_stem)\n", " \n", " grid_metrics_file_name = '{}/gotm_l4_{}_level.nc'.format(data_dir, n_z_levels)\n", " cf.set('OCEAN_DATA', 'grid_metrics_file', grid_metrics_file_name)\n", "\n", " simulation_dir = '{}/simulations/gotm_{}_z_levels'.format(cwd, n_z_levels)\n", " # Save a copy in the simulation directory\n", " with open(\"{}/pylag.cfg\".format(simulation_dir), 'w') as config:\n", " cf.write(config)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Running the model\n", "\n", "With the run configuration files saved, we can now run the example. In total four simulations will be run, one for each different number of vertical levels. We use the `subprocess` module to launch the four simulations simultaneously." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "import subprocess\n", "\n", "# Create a separate run configuration file for each GOTM simulation\n", "procs = []\n", "for n_z_levels in z_0s.keys():\n", " \n", " # Change to the run directory and launch\n", " simulation_dir = '{}/simulations/gotm_{}_z_levels'.format(cwd, n_z_levels)\n", " \n", " # Change to the run directory\n", " os.chdir('{}'.format(simulation_dir))\n", " \n", " proc = subprocess.Popen(\n", " ['python', '-m', 'pylag.main', '-c', 'pylag.cfg'],\n", " stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n", " \n", " procs.append(proc)\n", "\n", " # Return to the cwd\n", " os.chdir(cwd)\n", "\n", "# Wait for all jobs to finish\n", "for proc in procs:\n", " out, err = proc.communicate()\n", " if proc.returncode != 0:\n", " print(f\"Received error: {err}\")\n", " raise RuntimeError('One or more of the runs failed')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualising the result\n", "\n", "One of the goals here is to examine how closely the particle field corresponds to the Eulerian tracer field, and whether there is a dependency on the number of vertical layers used with *GOTM*. We will use a Gaussian Kernel Density Estimator (KDE) with reflecting boundary conditions to compute particle concentrations at the same points in space at which the Eulerian tracer is defined. Similar to the plot above, we do this for *GOTM* runs that used 20 and 200 vertical levels. In so doing, we implement another little helper function to assist with plotting the particle field. The particle and Eulerian tracer fields are plotted side by side." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def create_particle_hovmoller_diagram(ax, gotm_file_name, pylag_file_names,\n", " vmin, vmax, cmap='jet', add_colorbar=False,\n", " cb_label=None):\n", " \"\"\" Helper plotting function \n", "\n", " \"\"\"\n", " norm = colors.LogNorm(vmin=vmin, vmax=vmax)\n", " plotter = GOTMPlotter(gotm_file_name, fs=font_size, time_rounding=gotm_time_rounding)\n", " plotter.hovmoller_particles(ax, pylag_file_names, date_start, date_end, pylag_time_rounding,\n", " add_colorbar=add_colorbar, cb_label=cb_label,\n", " norm=norm, cmap=cmap)\n", " ax.set_xlim(date_start, date_end)\n", " ax.set_ylim(zmin, zmax)\n", " return\n", "\n", "# Time rounding\n", "pylag_time_rounding = 1800.\n", "\n", "# Create figure\n", "font_size=12\n", "figsize = (12, 8)\n", "fig, ax_arr = plt.subplots(2, 2, figsize=figsize)\n", "fig.set_facecolor('white')\n", "\n", "for ax in ax_arr.ravel():\n", " plt.setp(ax.get_xticklabels(), fontsize=font_size)\n", " plt.setp(ax.get_yticklabels(), fontsize=font_size)\n", "\n", "# Particle field for 20 z levels\n", "gotm_file_name = '{}/gotm_l4_20_level.nc'.format(data_dir)\n", "pylag_file_names = ['{}/simulations/gotm_20_z_levels/output/pylag_1.nc'.format(cwd)]\n", "create_particle_hovmoller_diagram(ax_arr[0, 0], gotm_file_name, pylag_file_names,\n", " add_colorbar=False, vmin=0.001, vmax=0.1,\n", " cmap=colourmap('tracer1_c'))\n", "ax_arr[0, 0].set_title('N$_{\\mathrm{Z}}$ = 20', fontsize=font_size)\n", "ax_arr[0, 0].set_xlabel('', fontsize=font_size)\n", "ax_arr[0, 0].set_ylabel('Depth (m)', fontsize=font_size)\n", "ax_arr[0, 0].set_xticklabels([])\n", "\n", "# Particle field for 200 z levels\n", "gotm_file_name = '{}/gotm_l4_200_level.nc'.format(data_dir)\n", "pylag_file_names = ['{}/simulations/gotm_200_z_levels/output/pylag_1.nc'.format(cwd)]\n", "create_particle_hovmoller_diagram(ax_arr[0, 1], gotm_file_name, pylag_file_names,\n", " vmin=0.001, vmax=0.1, cmap=colourmap('tracer1_c'),\n", " add_colorbar=True,\n", " cb_label='Particle field (mg m$^{-3}$)')\n", "ax_arr[0, 1].set_title('N$_{\\mathrm{Z}}$ = 200', fontsize=font_size)\n", "ax_arr[0, 1].set_xlabel('', fontsize=font_size)\n", "ax_arr[0, 1].set_ylabel('', fontsize=font_size)\n", "ax_arr[0, 1].set_xticklabels([])\n", "ax_arr[0, 1].set_yticklabels([])\n", "\n", "# Plot tracer_c for 20 z levels\n", "gotm_file_name = '{}/gotm_l4_20_level.nc'.format(data_dir)\n", "create_hovmoller_diagram(ax_arr[1, 0], gotm_file_name, 'tracer_c', vmin=0.001, vmax=0.1,\n", " cmap=colourmap('tracer1_c'), add_colorbar=False)\n", "ax_arr[1, 0].set_ylabel('Depth (m)', fontsize=font_size)\n", "ax_arr[1, 0].set_xlabel('Date', fontsize=font_size)\n", "ax_arr[1, 0].xaxis.set_major_locator(mdates.DayLocator())\n", "ax_arr[1, 0].xaxis.set_major_formatter(mdates.DateFormatter('%d %b'))\n", "for tick in ax_arr[1, 0].get_xticklabels():\n", " tick.set_rotation(45)\n", "\n", "# Plot tracer_c for 200 z levels\n", "gotm_file_name = '{}/gotm_l4_200_level.nc'.format(data_dir)\n", "create_hovmoller_diagram(ax_arr[1, 1], gotm_file_name, 'tracer_c', vmin=0.001, vmax=0.1,\n", " cmap=colourmap('tracer1_c'), add_colorbar=True,\n", " cb_label='Tracer (mg m$^{-3}$)')\n", "ax_arr[1, 1].set_ylabel('', fontsize=font_size)\n", "ax_arr[1, 1].set_xlabel('Date', fontsize=font_size)\n", "ax_arr[1, 1].xaxis.set_major_locator(mdates.DayLocator())\n", "ax_arr[1, 1].xaxis.set_major_formatter(mdates.DateFormatter('%d %b'))\n", "for tick in ax_arr[1, 1].get_xticklabels():\n", " tick.set_rotation(45)\n", "labels = ax_arr[1, 1].set_yticklabels([])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It should be pointed out that the above plot shows results for a single run with relatively few particles. As the model is stochastic, a second simulation would produce slightly different results. With this in mind, there are features within the figures that are worthy of comment. Firstly, the Eulerian tracer fields are not identical, which is to be expected - the two *GOTM* runs were performed using two different vertical grids. Second, in qualitative terms at least, there appears to be a better match between the particle and Eulerian fields in the run that used 200 vertical layers. In particular, the sharp gradient in the tracer field at a depth of around 15 m is not reproduced well in the run that used just 20 vertical layers. We can also compare the Eulerian and particle fields in more quantitative terms. Below, we compare the Eulerian and particle fields by computing the RMSE for all depth levels at six hour intervals over the last week of the simulation." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 0, 'N$_{\\\\mathrm{Z}}$')" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "from datetime import timedelta\n", "\n", "from pylag.processing.plot import create_figure\n", "from pylag.processing.gotm import get_rmse\n", "\n", "\n", "# Create an array of dates on which to compare the eulerian and lagrangian models\n", "# With a step of 6, the two fields are compared every six hours for the last week\n", "# of the simulation, given the default start and end dates.\n", "n_days = (date_end - date_start).days\n", "hours_per_day = 24\n", "step = 6\n", "hours = range(0, (hours_per_day * n_days) + step, step)\n", "dates = np.array([date_start + timedelta(hours=h) for h in hours])\n", "\n", "# Compute RMSE\n", "z_levels = []\n", "epsilons = []\n", "for n_z_levels in z_0s.keys():\n", " simulation_dir = '{}/simulations/gotm_{}_z_levels'.format(cwd, n_z_levels)\n", " gotm_file_name = '{}/gotm_l4_{}_level.nc'.format(data_dir, n_z_levels)\n", " pylag_file_names = ['{}/output/pylag_1.nc'.format(simulation_dir)]\n", "\n", " epsilon = get_rmse(gotm_file_name, 'tracer_c', pylag_file_names, dates,\n", " gotm_time_rounding, pylag_time_rounding)\n", " \n", " z_levels.append(float(n_z_levels))\n", " epsilons.append(epsilon)\n", "\n", "# Create figure\n", "font_size = 15\n", "fig, ax = create_figure(figure_size=(12., 12.), font_size=font_size)\n", " \n", "# Plot\n", "ax.plot(z_levels, epsilons, 'o-')\n", "ax.set_xlim(0.0, 220)\n", "ax.set_ylim(0.0, 0.0125)\n", "\n", "ax.set_title('RMSE ({} particles)'.format(n_particles), fontsize=font_size)\n", "ylabel = ax.set_ylabel('$\\mathrm{\\epsilon}$', fontsize=font_size)\n", "xlabel = ax.set_xlabel('N$_{\\mathrm{Z}}$', fontsize=font_size)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the plot, we can see the RMSE halves as the number of vertical levels is increased from 20 to 200. This result has relevance to work with 3D hydrodynamic models, where typical setups use between 20 and 50 vertical layers, depending on the application.\n", "\n", "It is interesting to see how the RMSE plot changes with the number of particles used in each simulation. Try rerunning the model and plotting the outputs with 12 and 1000 particles in each simulation. Next, try rerunning the model using a `cubic_spline` vertical interpolation scheme.\n", "\n", "
\n", "\n", "**Note:**\n", " \n", "Depending on your machine, simulations that use 1000 particles per run may take some time to complete as the model is computing 4 * 1000 particle trajectories over a month using a 5 second time step.\n", "\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Extending the analysis\n", "\n", "The results of an experiment in which both the interpolation scheme and the numerical method were varied when investigating the relationship between the number of vertical levels and the RMSE are shown below.\n", "\n", "![GOTM RMSE](figures/gotm_rmse.png)\n", "\n", "The results are based on runs done using 1000 particles in ensemble simulations with 10 trials. Thus, each data point uses the pathlines of 10000 particles to compute the RMSE. It can be seen the Euler, Visser and Milstein methods all perform simularly. This is to be expected, as they all converge with the same order in the weak sense, and it is this property that we are testing here. What is also striking is the effect of putting a spline through the data points and using it for the interpolation. It can be seen that this markedly reduces the RMSE, especially for GOTM configurations that used just 10 or 50 vertical levels." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## References\n", "\n", "Grawe, U., 2010. Implementation of high-order particle-tracking schemes in a water column model. Ocean Modelling. 36(1-2) 80-89. DOI: 10.1016/j.ocemod.2010.10.002." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.9.7" } }, "nbformat": 4, "nbformat_minor": 4 }