{ "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": [ "WARNING: File NemoNorthSeaORCA025-N006_data/coordinates.nc could not be decoded properly by xarray (version 0.11.0).\n", " It will be opened with no decoding. Filling values might be wrongly parsed.\n", "WARNING: Casting lon data to np.float32\n", "WARNING: Casting lat data to np.float32\n", "INFO: Compiled JITParticleAdvectionRK4_3D ==> /var/folders/h0/01fvrmn11qb62yjw7v1kn62r0000gq/T/parcels-503/c7806282ccd5229d9f341baefbdfbb23.so\n" ] } ], "source": [ "from parcels import FieldSet, ParticleSet, JITParticle, AdvectionRK4_3D\n", "from glob import glob\n", "import numpy as np\n", "from datetime import timedelta as delta\n", "from os import path\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))\n" ] }, { "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)" ] } ], "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": "3.6.8" } }, "nbformat": 4, "nbformat_minor": 1 }