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258 changes: 258 additions & 0 deletions docs/user_guide/examples/tutorial_fesom.ipynb
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As I can't easily comment on specific lines, here two comments:

  1. In other tutorials, we always use ds = parcels.convert.fesom_to_ugrid(ds) (instead of ds = fesom_to_ugrid(ds)) to make explicit that these convert functions are part of parcels. Do that here too?
  2. Is there an easy/quick way to also plot the velocity field before the pset.execute()? In that way, users get a feel for what kind of flow to expect. And that's a nice help for them to visualise their own unstructured flow field

Original file line number Diff line number Diff line change
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 🖥️ FESOM tutorial\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Parcels v4 supports unstructured-grid model output via [uxarray](https://uxarray.readthedocs.io/). This tutorial walks through the minimum steps to advect particles in real [FESOM2](https://fesom.de/) output. The recipe is:\n",
"\n",
"1. Open the FESOM grid and data files with `uxarray`.\n",
"2. Rename FESOM-specific dimensions to Parcels' UGRID conventions with `parcels.convert.fesom_to_ugrid`.\n",
"3. Build a `FieldSet` with `parcels.FieldSet.from_ugrid_conventions`.\n",
"4. Run the simulation as on any structured grid.\n",
"\n",
"If you have not done so already, work through the [quickstart tutorial](../../getting_started/tutorial_quickstart.md) first to get familiar with `ParticleSet`, `Kernel`, and `ParticleFile`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from pathlib import Path\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import uxarray as ux\n",
"\n",
"import parcels\n",
"import parcels.tutorial\n",
"from parcels.convert import fesom_to_ugrid"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Get the FESOM tutorial dataset\n",
"\n",
"We use a small periodic-channel snapshot from a FESOM2 simulation that ships with Parcels' tutorial data registry. As in the [quickstart](../../getting_started/tutorial_quickstart.md), `parcels.tutorial.open_dataset` downloads the files into a local cache on first use; subsequent calls just return the cached copy.\n",
"\n",
"`uxarray` expects file paths rather than an in-memory dataset, so we trigger the downloads and then point `ux.open_mfdataset` at the cached files:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for name in [\n",
" \"FESOM_periodic_channel/fesom_channel\", # grid description\n",
" \"FESOM_periodic_channel/u.fesom_channel\", # zonal velocity (face-registered)\n",
" \"FESOM_periodic_channel/v.fesom_channel\", # meridional velocity (face-registered)\n",
" \"FESOM_periodic_channel/w.fesom_channel\", # vertical velocity (node-registered)\n",
"]:\n",
" parcels.tutorial.open_dataset(name)\n",
"\n",
"from parcels._datasets.remote import _DATA_HOME\n",
"\n",
"data_dir = Path(_DATA_HOME) / \"data\" / \"FESOM_periodic_channel\"\n",
"\n",
"grid_path = str(data_dir / \"fesom_channel.nc\")\n",
"data_paths = [\n",
" str(data_dir / \"u.fesom_channel.nc\"),\n",
" str(data_dir / \"v.fesom_channel.nc\"),\n",
" str(data_dir / \"w.fesom_channel.nc\"),\n",
"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```{note}\n",
"The `FESOM_periodic_channel` dataset is a single-time-step snapshot of an idealised channel (~4.4° × ~18° wide, 40 vertical layers). Working with multi-time FESOM output is identical, except you pass a glob or list of data files to `ux.open_mfdataset`.\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Open the data with `uxarray`\n",
"\n",
"`ux.open_mfdataset(grid_path, data_paths)` reads the FESOM grid description and joins the velocity files on its grid. FESOM names its velocity variables `u`, `v`, `w` — we rename them to `U`, `V`, `W` so that `parcels.FieldSet.from_ugrid_conventions` recognises them as the velocity components:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ds = ux.open_mfdataset(grid_path, data_paths).rename_vars(\n",
" {\"u\": \"U\", \"v\": \"V\", \"w\": \"W\"}\n",
")\n",
"ds"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note the FESOM-specific dimension names: `elem` (number of triangular faces), `nod2` (number of nodes), `nz1` (vertical layer centres), and `nz` (layer interfaces). The horizontal velocities `U` and `V` live on face centres along `nz1`; the vertical velocity `W` lives on nodes along `nz`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Convert to UGRID conventions\n",
"\n",
"Parcels works with a small UGRID-compliant dialect: nodes are `n_node`, faces are `n_face`, vertical centres are `zc`, and vertical interfaces are `zf`. The helper `parcels.convert.fesom_to_ugrid` does this rename in one call:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ds = fesom_to_ugrid(ds)\n",
"print(\"dims:\", dict(ds.sizes))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Build the `FieldSet`\n",
"\n",
"With UGRID-compliant dimensions in place, `parcels.FieldSet.from_ugrid_conventions` builds the `FieldSet`. It detects `U`, `V`, `W`, assigns a `UxGrid` to each field, and picks an appropriate interpolator based on each variable's coordinate location (face- vs. node-registered, centre vs. interface). Use `mesh=\"spherical\"` so that velocities in m/s are correctly converted to deg/s along the lon/lat coordinates:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fieldset = parcels.FieldSet.from_ugrid_conventions(ds, mesh=\"spherical\")\n",
"\n",
"for name, field in fieldset.fields.items():\n",
" interp = getattr(field, \"interp_method\", None)\n",
" interp_name = interp.__name__ if interp is not None else \"-\"\n",
" print(f\"{name:>4s} -> {type(field).__name__:<11s} interp={interp_name}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`U` and `V` get face-registered interpolation, `W` gets node-registered linear interpolation. The combined vector fields `UV` and `UVW` are assembled automatically."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": "## Release particles and advect\n\nWe seed particles on a grid of four latitudes spanning the channel and ten longitudes, and integrate for two days with RK4. Because this snapshot has only a single time level, `fieldset.time_interval` is `None` and we omit the `time=` argument so that Parcels treats the flow as constant in time:"
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"lon_grid, lat_grid = np.meshgrid(\n",
" np.linspace(0.5, 4.0, 10),\n",
" np.linspace(3.0, 15.0, 4),\n",
")\n",
"lon = lon_grid.ravel()\n",
"lat = lat_grid.ravel()\n",
"z = np.full(lon.size, 50.0) # release at 50 m depth\n",
"\n",
"pset = parcels.ParticleSet(\n",
" fieldset=fieldset,\n",
" pclass=parcels.Particle,\n",
" lon=lon,\n",
" lat=lat,\n",
" z=z,\n",
")\n",
"\n",
"output_file = parcels.ParticleFile(\n",
" \"output-fesom.parquet\", outputdt=np.timedelta64(1, \"h\")\n",
")\n",
"\n",
"pset.execute(\n",
" [parcels.kernels.AdvectionRK4],\n",
" runtime=np.timedelta64(2, \"D\"),\n",
" dt=np.timedelta64(5, \"m\"),\n",
" output_file=output_file,\n",
" verbose_progress=False,\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": "## Plot the result\n\nEach particle trajectory is coloured by observation time so we can follow how the particles drift through the FESOM2 velocity field:"
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df = parcels.read_particlefile(\"output-fesom.parquet\")\n",
"\n",
"fig, ax = plt.subplots(figsize=(10, 5))\n",
"ax.scatter(lon, lat, facecolors=\"none\", edgecolors=\"k\", s=60, label=\"release\")\n",
"sc = ax.scatter(\n",
" df[\"lon\"], df[\"lat\"], c=df[\"time\"].dt.total_seconds(), s=8, cmap=\"viridis\"\n",
")\n",
"fig.colorbar(sc, ax=ax, label=\"time since release [s]\")\n",
"ax.set_xlabel(\"Longitude [deg E]\")\n",
"ax.set_ylabel(\"Latitude [deg N]\")\n",
"ax.legend(loc=\"upper right\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The particles drift through the channel following the FESOM2 velocity field. From here, the rest of Parcels — custom kernels, sampling fields onto particles, writing your own interpolators — works identically to structured grids. See the [interpolation tutorial](./tutorial_interpolation.ipynb) for the available `Ux*` interpolators and how to write a custom one."
]
}
],
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"display_name": "docs",
"language": "python",
"name": "python3"
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"codemirror_mode": {
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1 change: 1 addition & 0 deletions docs/user_guide/index.md
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Expand Up @@ -28,6 +28,7 @@ examples/explanation_grids.md
examples/tutorial_nemo.ipynb
examples/tutorial_croco_3D.ipynb
examples/tutorial_mitgcm.ipynb
examples/tutorial_fesom.ipynb
examples/tutorial_velocityconversion.ipynb
examples/tutorial_nestedgrids.ipynb
examples/tutorial_manipulating_field_data.ipynb
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