{ "cells": [ { "cell_type": "markdown", "id": "202f4da1", "metadata": {}, "source": [ "# Particle-Field interaction example\n", "\n", "This notebook illustrates a simple way to make particles interact with a ``` Field``` object and modify it. The ``` Field``` will thus change at each step of the simulation, and will be written using the same time resolution as the particle outputs, in as many ```netCDF``` files.\n", "\n", "The concept is similar to that of [Field sampling](https://nbviewer.jupyter.org/github/OceanParcels/parcels/blob/particle_interaction_prototype/parcels/examples/tutorial_sampling.ipynb): here instead, on top of reading the field value at their location, particles are able to alter it as defined in the ```Kernel```. To do this, it is important to keep in mind that:\n", "\n", "- Particles have to be defined as ```ScipyParticles```\n", "- ```Field``` writing at each ```outputdt``` is not default and has to be enabled\n", "- The time of the ```Field``` to be saved has to be updated within a ```Kernel```\n", "\n", "In this example, particles will carry a tracer and release it into a clean ```Field``` during their advection by surface currents. To show how can particles interact with a ```Field``` and alter it, the exchange of such tracer is modelled here with a discretized version of the mass transfer equation, defined as follows:\n", "\n", "\\begin{equation}\n", "\\Delta c_{particle}(t) = aC_{field}(t-1) - bc_{particle}(t-1)\n", "\\tag{1}\n", "\\label{1}\n", "\\end{equation}\n", "\n", "In Eq.1, $c_{particle}$ is the tracer concentration associated with the particle, $C_{field}$ is the tracer concentration in seawater at particle location, and $a$ and $b$ are weights that modulate the sorption of tracer from seawater and its desorption, respectively. \n", "\n", "Additionally to a relevant ```Kernel```, we will define a suitable particle class to store $c_{particle}$, as it is needed to solve Eq.1. \n" ] }, { "cell_type": "markdown", "id": "c6d39d45", "metadata": {}, "source": [ "### Particle altering a Field during advection" ] }, { "cell_type": "code", "execution_count": 1, "id": "b5f35c63", "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "from parcels import Variable, Field, FieldSet, ParticleSet, ScipyParticle, AdvectionRK4, plotTrajectoriesFile\n", "\n", "import numpy as np\n", "from datetime import timedelta as delta\n", "import netCDF4\n", "import xarray as xr\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "id": "068921eb", "metadata": {}, "source": [ "In this specific example, particles will be advected by surface ocean velocities stored in netCDF files in the folder ```GlobCurrent_example_data```. We will store these in a ```FieldSet``` object, and then add a ```Field``` to it to represent the tracer field. This latter field will be initialized with zeroes, as we assume that this tracer is absent on the ocean surface and released by particles only. Note that, in order to conserve mass, it is important to set `interp_method='nearest'` for the tracer Field.\n", "\n", "As we are interested in storing this new field during the simulation, we will set its ```.to_write``` method to ```True```. Note that this only works for Fields that consist of only _one_ snapshot in time. " ] }, { "cell_type": "code", "execution_count": 2, "id": "a64ed4f0", "metadata": {}, "outputs": [], "source": [ "# Velocity fields\n", "fname = r'GlobCurrent_example_data/*.nc'\n", "filenames = {'U': fname, 'V': fname}\n", "variables = {'U': 'eastward_eulerian_current_velocity', 'V': 'northward_eulerian_current_velocity'}\n", "dimensions = {'U': {'lat': 'lat', 'lon': 'lon', 'time': 'time'},\n", " 'V': {'lat': 'lat', 'lon': 'lon', 'time': 'time'},\n", " } \n", "fieldset = FieldSet.from_netcdf(filenames, variables, dimensions)\n", "\n", "# In order to assign the same grid to the tracer field, it is convenient to load a single velocity file\n", "fname1 = r'GlobCurrent_example_data/20030101000000-GLOBCURRENT-L4-CUReul_hs-ALT_SUM-v02.0-fv01.0.nc'\n", "filenames1 = {'U': fname1, 'V': fname1}\n", "\n", "field_for_size = FieldSet.from_netcdf(filenames1, variables, dimensions) # this field has the same variables and dimensions as the other velocity fields\n", "\n", "# Adding the tracer field to the FieldSet\n", "dimsC = [len(field_for_size.U.lat),len(field_for_size.U.lon)] # it has to have the same dimentions as the velocity fields\n", "dataC = np.zeros([dimsC[0],dimsC[1]])\n", "\n", "fieldC = Field('C', dataC, grid=field_for_size.U.grid, interp_method='nearest') # the new Field will be called C, for tracer Concentration. For mass conservation, interp_method='nearest'\n", "\n", "fieldset.add_field(fieldC) # C field added to the velocity FieldSet\n", "\n", "fieldset.C.to_write = True # enabling the writing of Field C during execution " ] }, { "cell_type": "code", "execution_count": 3, "id": "22fa6745", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fieldset.C.show() # our new C field has been added to the FieldSet" ] }, { "cell_type": "markdown", "id": "6caf2eb7", "metadata": {}, "source": [ "Some global parameters have to be defined, such as $a$ and $b$ of Eq.1, and a weight that works as a conversion factor from $\\Delta c_{particle}$ to $C_{field}$. \n", "We will add these parameters to the ```FieldSet```." ] }, { "cell_type": "code", "execution_count": 4, "id": "54ec0e6f", "metadata": {}, "outputs": [], "source": [ "fieldset.add_constant('a', 10) \n", "fieldset.add_constant('b', .2) \n", "fieldset.add_constant('weight', .01) " ] }, { "cell_type": "markdown", "id": "470c2e80", "metadata": {}, "source": [ "We will now define a new particle class. A ```VectorParticle``` is a ```ScipyParticle``` having a ```Variable``` to store the current tracer concentration ```c``` associated with it. As in this case we want our particles to release a tracer into a clean field, we will initialize ```c``` with an arbitrary value of `100`.\n", "\n", "We also need to define the ```Kernel``` that performs the particle-field interaction. In this Kernel, we will implement Eq.1, so that $\\Delta c_{particle}$ can be used to update $c_{particle}$ and $C_{field}$ at the particle location, and thus get their values at the current time $t$.\n", "\n", "Additionally, the time of ```fieldset.C``` is updated within the ```Interaction``` Kernel, which is an important step to properly write its value." ] }, { "cell_type": "code", "execution_count": 5, "id": "203a82eb", "metadata": {}, "outputs": [], "source": [ "class VectorParticle(ScipyParticle):\n", " c = Variable('c', dtype=np.float32, initial=100.) # particle concentration c is initialized with a non-zero value\n", "\n", "def Interaction(particle, fieldset, time):\n", " deltaC = (fieldset.a*fieldset.C[particle]-fieldset.b*particle.c) # the exchange is obtained as a discretized mass transfer equation\n", " \n", " xi, yi = particle.xi[fieldset.C.igrid], particle.yi[fieldset.C.igrid], \n", " if abs(particle.lon - fieldset.C.grid.lon[xi+1]) < abs(particle.lon - fieldset.C.grid.lon[xi]):\n", " xi += 1\n", " if abs(particle.lat - fieldset.C.grid.lat[yi+1]) < abs(particle.lat - fieldset.C.grid.lat[yi]):\n", " yi += 1\n", " \n", " particle.c += deltaC\n", " fieldset.C.data[0, yi, xi] += -deltaC*fieldset.weight # weight, defined as a constant for the FieldSet, acts here as a conversion factor between c_particle and C_field\n", " fieldset.C.grid.time[0] = time # updating Field C time\n", "\n", "def WriteInitial(particle, fieldset, time): # will be used to store the initial conditions of fieldset.C\n", " fieldset.C.grid.time[0] = time\n", "\n", "pset = ParticleSet(fieldset=fieldset, pclass=VectorParticle, lon=[24.5], lat=[-34.8]) # for simplicity, we'll track a single particle here" ] }, { "cell_type": "markdown", "id": "2a6783bf", "metadata": {}, "source": [ "Three things are worth noticing in the code above:\n", "- The use of ```fieldset.C[particle]``` \n", "- The computation of the relevant grid cell (`xi`, `yi`)\n", "- Writing $C_{field}$ through ```fieldset.C.data[0, yi, xi]``` \n", "\n", "Because `fieldset.C[particle]` interpolates the $C_{field}$ value from the nearest grid cell to the particle, it is important to also write to that same grid cell. That is not completely trivial to do in Parcels, which is why lines 7-11 in the cell above calculate which `xi` and `yi` are closest to the particle longitude and latitude (this extends trivially to depth too). Our first guess is `particle.xi[fieldset.C.igrid]`, the location in the particular grid, but it could be that `xi+1` is closer, which is what the `if`-statements are for.\n", "\n", "The new indices are then used in ```fieldset.C.data[0, yi, xi]``` to access the field value in the cell where the particle is found, that can be different from the result of the interpolation at the particle's coordinates. Note that here we need to use ```fieldset.C.data[0, yi, xi]``` both for calculating ```deltaC``` and for the consequent update of the cell value for consistency between the forcing (the seawater-particle gradient) and its effect (the exchange, and consequent alteration of the field).\n", "\n", "Remember that reading and writing ```Fields``` at particle location through particle indices is only possible for ```ScipyParticles``` (and returns an error if ```JITParticles``` are used). " ] }, { "cell_type": "code", "execution_count": 6, "id": "b96bafdb", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "pset.show(field=fieldset.C) # Initial particle location and the tracer field C" ] }, { "cell_type": "markdown", "id": "93994a91", "metadata": {}, "source": [ "Now we are going to execute the advection of the particle and the simultaneous release of the tracer it carries. We will thus add the ```interactionKernel``` defined above to the built-in Kernel ```AdvectionRK4```.\n", "\n", "Before running the advection, we will execute the ```pset``` with the ```WriteInitial``` for ```dt=0```: this will write the initial condition of fieldset.C to a ```netCDF``` file.\n", "\n", "While particle outputs will be written in a file named ```interaction.zarr``` at every ```outputdt```, the field will be automatically written in ```netCDF``` files named ```interaction_wxyzC.nc```, with ```wxyz``` being the number of the output and ```C``` the ```FieldSet``` variable of our interest. Note that you can use tools like [ncrcat](https://linux.die.net/man/1/ncrcat) (on linux/macOS) to combine these separate files into one large ```netCDF``` file after the simualtion." ] }, { "cell_type": "code", "execution_count": 7, "id": "d20fcba8", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING: dt or runtime are zero, or endtime is equal to Particle.time. The kernels will be executed once, without incrementing time\n" ] } ], "source": [ "output_file = pset.ParticleFile(name=r'interaction.zarr', outputdt=delta(days=1))\n", "\n", "pset.execute(WriteInitial, dt=0., output_file=output_file)\n", "\n", "pset.execute(AdvectionRK4 + pset.Kernel(Interaction), # the particle will FIRST be transported by currents and THEN interact with the field\n", " dt=delta(days=1),\n", " runtime=delta(days=24), # we are going to track the particle and save its trajectory and tracer concentration for 24 days\n", " output_file=output_file)" ] }, { "cell_type": "markdown", "id": "42182927", "metadata": {}, "source": [ "We can see that $c_{particle}$ has been saved along with particle trajectory, as expected." ] }, { "cell_type": "code", "execution_count": 8, "id": "a6d869e8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[100. 80. 64. 51.2 40.96\n", " 32.767998 26.214397 20.971518 16.777214 13.421771\n", " 11.072961 9.428794 7.9624653 6.369972 5.095978\n", " 4.076782 3.3633454 2.6906762 2.152541 1.7758462\n", " 1.420677 1.1720585 0.93764675 0.7501174 0.6235351 ]]\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "pset_traj = xr.open_zarr(r'interaction.zarr')\n", "\n", "print(pset_traj['c'].values)\n", "\n", "plotTrajectoriesFile('interaction.zarr');" ] }, { "cell_type": "markdown", "id": "3fcc6dca", "metadata": {}, "source": [ "But what about ```fieldset.C```? We can see that it has been accordingly modified during particle motion. Using ```fieldset.C``` we can access the field as resulting at the end of the run, with no information about the previous time steps." ] }, { "cell_type": "code", "execution_count": 9, "id": "184d6860", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "c_results = fieldset.C.data[0,:,:].copy() # Copying the final field data in a new array\n", "c_results[[field_for_size.U.data==0][0][0]]= np.nan # using a mask for fieldset.C.data on land\n", "c_results[c_results==0] = np.nan # masking the field where its value is zero -- areas that have not been modified by the particle, for clearer plotting\n", "\n", "try: # Works if Cartopy is installed\n", " import cartopy\n", " import cartopy.crs as ccrs\n", " extent = [10, 33, -37, -29]\n", " \n", " X = fieldset.U.lon\n", " Y = fieldset.U.lat\n", "\n", " plt.figure(figsize=(12, 6))\n", " ax = plt.axes(projection=ccrs.Mercator())\n", " ax.set_extent(extent)\n", "\n", " ax.add_feature(cartopy.feature.OCEAN, facecolor='lightgrey')\n", " ax.add_feature(cartopy.feature.LAND, edgecolor='black', facecolor='floralwhite')\n", " gl=ax.gridlines(xlocs = np.linspace(10,34,13) , ylocs=np.linspace(-29,-37,9),draw_labels=True)\n", " gl.right_labels = False\n", " gl.bottom_labels = False\n", "\n", " xx, yy = np.meshgrid(X,Y)\n", "\n", " results = ax.pcolormesh(xx,yy,(c_results),transform=ccrs.PlateCarree(),vmin=0,)\n", " cbar=plt.colorbar(mappable = results, ax=ax)\n", " cbar.ax.text(.8,.070,'$C_{field}$ concentration', rotation=270, fontsize=12)\n", " \n", "except:\n", " print('Please install the Cartopy package.')" ] }, { "cell_type": "markdown", "id": "52a42fbe", "metadata": {}, "source": [ "When looking at tracer concentrations, we see that $c_{particle}$ decreases along its trajectory (right to left), as it is releasing the tracer it carries. Accordingly, values of $C_{field}$ provided by particle interaction progressively reduce along the particle's route.\n", "\n", "Notice that the first particle-field interaction occurs at time $t = 1$ day, and namely after the execution of the first step of ```AdvectionRK4```, as shown by the unaltered field value at the particle's starting location. \n", "In order to let the particle interact before being advected, we would have to change the order in which the two Kernels are added together in ```pset.execute```, i.e. ```pset.execute(interactionKernel + AdvectionRK4, ...)```. In this latter case, the interaction would not occur at the particle's final position instead." ] }, { "cell_type": "code", "execution_count": 10, "id": "e2ce3270", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "x_centers, y_centers = np.meshgrid(fieldset.U.lon-np.diff(fieldset.U.lon[:2])/2, fieldset.U.lat-np.diff(fieldset.U.lat[:2])/2)\n", "\n", "fig,ax = plt.subplots(1,1,figsize=(10,7),constrained_layout=True)\n", "ax.set_facecolor('lightgrey') # For visual coherence with the plot above\n", "\n", "fieldplot=ax.pcolormesh(x_centers[-28:-17,22:41],y_centers[-28:-17,22:41],c_results[-28:-18,22:40], vmin=0, vmax=0.2,cmap='viridis') \n", "# Zoom on the area of interest\n", "field_cbar = plt.colorbar(fieldplot,ax=ax)\n", "field_cbar.ax.text(.6,.070,'$C_{field}$ concentration', rotation=270, fontsize=12)\n", "\n", "particle = plt.scatter(pset_traj['lon'][:].values[0,:],pset_traj['lat'][:].values[0,:], c=pset_traj['c'][:].values[0,:],vmin=0, s=100, edgecolor='white') \n", "particle_cbar = plt.colorbar(particle,ax=ax, location = 'top')\n", "particle_cbar.ax.text(40,300,'$c_{particle}$ concentration', fontsize=12);" ] }, { "cell_type": "markdown", "id": "ad657e8f", "metadata": {}, "source": [ "Finally, to see the ```C``` field in time we have to load the ```.nc``` files produced during the run. In the following plots, particle location and field values are shown at each time step. " ] }, { "cell_type": "code", "execution_count": 11, "id": "53b8979a", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(5,5, figsize=(30,20))\n", "\n", "daycounter = 1\n", "\n", "for i in range(len(ax)):\n", " for j in range(len(ax)):\n", " data = netCDF4.Dataset(r'interaction_00'+ '%02d' % daycounter+'C.nc')\n", " \n", " c_results = data['C'][0,0,:,:].data.copy() # copying the final field data in a new array\n", " c_results[[field_for_size.U.data==0][0][0]]= np.nan # using a mask for fieldset.C.data on land\n", " c_results[c_results==0] = np.nan # masking the field where its value is zero -- areas that have not been modified by the particle, for clearer plotting\n", "\n", " \n", " ax[i,j].set_facecolor('lightgrey') # For visual coherence with the plots above\n", "\n", " fieldplot=ax[i,j].pcolormesh(x_centers[-28:-17,22:41],y_centers[-28:-17,22:41],c_results[-28:-18,22:40], vmin=0, vmax=0.2,cmap='viridis') \n", " \n", " particle = ax[i,j].scatter(pset_traj['lon'][:].values[0,daycounter-1],pset_traj['lat'][:].values[0,daycounter-1], c=pset_traj['c'][:].values[0,daycounter-1],vmin=0, vmax=100, s=100, edgecolor='white') \n", " # plotting particle location at current time step -- daycounter-1 due to different indexing\n", " \n", " ax[i,j].set_title('Day '+ str(daycounter-1))\n", " \n", " daycounter +=1 # next day\n", "\n", "fig.subplots_adjust(right=0.8)\n", "fig.subplots_adjust(top=0.8)\n", " \n", "cbar_ax = fig.add_axes([0.82, 0.12, 0.03, 0.7])\n", "fig.colorbar(fieldplot, cax=cbar_ax)\n", "cbar_ax.tick_params(labelsize=18)\n", "cbar_ax.text(.4,.08,'$C_{field}$ concentration', fontsize=25, rotation=270)\n", "\n", "cbar_ax1 = fig.add_axes([0.1, .85, .7, 0.04])\n", "fig.colorbar(particle, cax=cbar_ax1, orientation = 'horizontal')\n", "cbar_ax1.tick_params(labelsize=18)\n", "cbar_ax1.text(42,170,'$c_{particle}$ concentration', fontsize=25);" ] } ], "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": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 5 }