{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Tutorial on how to use Parcels on 3D C grids" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One of the features of Parcels is that it can directly and natively work with `Field` data discretised on C-grids. These C grids are very popular in OGCMs, so velocity fields outputted by OGCMs are often provided on such grids, except if they have been firstly re-interpolated on a A grid.\n", "\n", "More information about C-grid interpolation can be found in [Delandmeter et al., 2019](https://www.geosci-model-dev-discuss.net/gmd-2018-339/).\n", "An example of such a discretisation is the NEMO model, which is one of the models supported in Parcels. A tutorial teaching how to [interpolate 2D data on a NEMO grid](https://nbviewer.jupyter.org/github/OceanParcels/parcels/blob/master/parcels/examples/tutorial_nemo_curvilinear.ipynb) is available within Parcels.\n", "\n", "Here, we focus on 3D fields. Basically, it is a straightforward extension of the 2D example, but it is very easy to do a mistake in the setup of the vertical discretisation that would affect the interpolation scheme.\n", "\n", "### Preliminary comments\n", "\n", "*How to know if your data is discretised on a C grid?* The best way is to read the documentation that comes with the data. Alternatively, an easy check is to assess the coordinates of the U, V and W fields: for an A grid, U, V and W are distributed on the same nodes, such that the coordinates are the same. For a C grid, there is a shift of half a cell between the different variables.\n", "\n", "*What about grid indexing?* Since the C-grid variables are not located on the same nodes, there is not one obvious way to define the indexing, i.e. where is `u[k,j,i]` compared to `v[k,j,i]` and `w[k,j,i]`. In Parcels, we use the same notation as in NEMO: see [horizontal indexing](https://www.nemo-ocean.eu/doc/img360.png) and [vertical indexing](https://www.nemo-ocean.eu/doc/img362.png).\n", "It is important that you check if your data is following the same notation. Otherwise, you should re-index your data properly (this can be done within Parcels, there is no need to regenerate new netcdf files).\n", "\n", "*What about the accuracy?* By default in Parcels, particle coordinates (i.e. longitude, latitude and depth) are stored using single-precision `np.float32` numbers. The advantage of this is that it saves some memory ressources for the computation. In some applications, especially where particles travel very close to the coast, the single precision accuracy can lead to uncontrolled particle beaching, due to numerical rounding errors. In such case, you may want to double the coordinate precision to `np.float64`. This can be done by adding the parameter `lonlatdepth_dtype=np.float64` to the ParticleSet constructor. Note that for C-grid fieldsets such as in NEMO, the coordinates precision is set by default to `np.float64`.\n", "\n", "### How to create a 3D NEMO `dimensions` dictionary?\n", "\n", "In the following, we will show how to create the `dimensions` dictionary for 3D NEMO simulations. What you require is a 'mesh_mask' file, which in our case is called `coordinates.nc` but in some other versions of NEMO has a different name. In any case, it will have to contain the variables `glamf`, `gphif` and `depthw`, which are the longitude, latitude and depth of the mesh nodes, respectively. Note that `depthw` is not part of the mesh_mask file, but is in the same file as the w data (`wfiles[0]`).\n", "\n", "For the C-grid interpolation in Parcels to work properly, it is important that `U`, `V` and `W` are on the same grid.\n", "\n", "The code below is an example of how to create a 3D simulation with particles, starting in the mouth of the river Rhine at 1m depth, and advecting them through the North Sea using the `AdvectionRK4_3D`" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO: Compiled ArrayJITParticleAdvectionRK4_3D ==> /var/folders/r2/8593q8z93kd7t4j9kbb_f7p00000gr/T/parcels-504/libe8f8d05e9f59db3883539a0a60266e35_0.so\n" ] } ], "source": [ "from parcels import FieldSet, ParticleSet, JITParticle, AdvectionRK4_3D\n", "from glob import glob\n", "from datetime import timedelta as delta\n", "\n", "from parcels import logger, XarrayDecodedFilter\n", "logger.addFilter(XarrayDecodedFilter()) # Add a filter for the xarray decoding warning\n", "\n", "data_path = 'NemoNorthSeaORCA025-N006_data/'\n", "ufiles = sorted(glob(data_path+'ORCA*U.nc'))\n", "vfiles = sorted(glob(data_path+'ORCA*V.nc'))\n", "wfiles = sorted(glob(data_path+'ORCA*W.nc'))\n", "mesh_mask = data_path + 'coordinates.nc'\n", "\n", "filenames = {'U': {'lon': mesh_mask, 'lat': mesh_mask, 'depth': wfiles[0], 'data': ufiles},\n", " 'V': {'lon': mesh_mask, 'lat': mesh_mask, 'depth': wfiles[0], 'data': vfiles},\n", " 'W': {'lon': mesh_mask, 'lat': mesh_mask, 'depth': wfiles[0], 'data': wfiles}}\n", "\n", "variables = {'U': 'uo',\n", " 'V': 'vo',\n", " 'W': 'wo'}\n", "dimensions = {'U': {'lon': 'glamf', 'lat': 'gphif', 'depth': 'depthw', 'time': 'time_counter'},\n", " 'V': {'lon': 'glamf', 'lat': 'gphif', 'depth': 'depthw', 'time': 'time_counter'},\n", " 'W': {'lon': 'glamf', 'lat': 'gphif', 'depth': 'depthw', 'time': 'time_counter'}}\n", "\n", "fieldset = FieldSet.from_nemo(filenames, variables, dimensions)\n", "\n", "pset = ParticleSet.from_line(fieldset=fieldset, pclass=JITParticle,\n", " size=10,\n", " start=(1.9, 52.5),\n", " finish=(3.4, 51.6),\n", " depth=1)\n", "\n", "kernels = pset.Kernel(AdvectionRK4_3D)\n", "pset.execute(kernels, runtime=delta(days=4), dt=delta(hours=6))" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING: Field.show() does not always correctly determine the domain for curvilinear grids. Use plotting with caution and perhaps use domain argument as in the NEMO 3D tutorial\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Level[8] depth is: [10.7679 12.846]\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "\n", "depth_level = 8\n", "print(\"Level[%d] depth is: [%g %g]\" % (depth_level, fieldset.W.grid.depth[depth_level], fieldset.W.grid.depth[depth_level+1]))\n", "\n", "pset.show(field=fieldset.W, domain={'N':60, 'S':49, 'E':15 ,'W':0}, depth_level=depth_level)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Adding other fields like cell edges\n", "It is quite straightforward to add other gridded data, on the same curvilinear or any other type of grid, to the fieldset. Because it is good practice to make no changes to a `FieldSet` once a `ParticleSet` has been defined in it, we redefine the fieldset and add the fields with the cell edges from the coordinates file using [`FieldSet.add_field()`](https://oceanparcels.org/gh-pages/html/#parcels.fieldset.FieldSet.add_field)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "from parcels import Field" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "fieldset = FieldSet.from_nemo(filenames, variables, dimensions)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "e1u = Field.from_netcdf(filenames=mesh_mask, variable='e1u', \n", " dimensions={'lon': 'glamu', 'lat': 'gphiu'},\n", " interp_method='nearest')\n", "e2u = Field.from_netcdf(filenames=mesh_mask, variable='e2u', \n", " dimensions={'lon': 'glamu', 'lat': 'gphiu'},\n", " interp_method='nearest')\n", "e1v = Field.from_netcdf(filenames=mesh_mask, variable='e1v', \n", " dimensions={'lon': 'glamv', 'lat': 'gphiv'},\n", " interp_method='nearest')\n", "e2v = Field.from_netcdf(filenames=mesh_mask, variable='e2v', \n", " dimensions={'lon': 'glamv', 'lat': 'gphiv'},\n", " interp_method='nearest')\n", "fieldset.add_field(e1u)\n", "fieldset.add_field(e2u)\n", "fieldset.add_field(e1v)\n", "fieldset.add_field(e2v)" ] } ], "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": "3.6.11" } }, "nbformat": 4, "nbformat_minor": 4 }