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erikvansebillereint-fischer
erikvansebille
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Adding a tutorial to show gsw use in Kernels
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‎docs/examples/tutorial_gsw_density.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "0",
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"metadata": {},
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"source": [
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"# Using the `gsw` toolbox to compute density"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1",
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"metadata": {},
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"source": [
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"This tutorial shows how to use the [`gsw` toolbox](https://teos-10.github.io/GSW-Python/) (the Gibbs SeaWater Oceanographic Toolbox of TEOS-10) within Parcels to compute density from temperature and salinity fields. The `gsw` toolbox can be installed via `conda install gsw`."
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]
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},
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{
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"cell_type": "markdown",
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"id": "2",
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"metadata": {},
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"source": [
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"First, load the necessary libraries and the data:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import xarray as xr\n",
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"\n",
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"import parcels\n",
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"\n",
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"# Load the CopernicusMarine data in the Agulhas region from the example_datasets\n",
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"example_dataset_folder = parcels.download_example_dataset(\n",
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" \"CopernicusMarine_data_for_Argo_tutorial\"\n",
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")\n",
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"\n",
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"ds = xr.open_mfdataset(f\"{example_dataset_folder}/*.nc\", combine=\"by_coords\")\n",
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"\n",
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"# TODO check how we can get good performance without loading full dataset in memory\n",
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"ds.load() # load the dataset into memory\n",
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"\n",
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"fieldset = parcels.FieldSet.from_copernicusmarine(ds)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4",
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"metadata": {},
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"source": [
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"Now, define a custom Particle class that includes temperature, salinity, and density as variables, and create a ParticleSet with one particle at a known location:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "5",
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"metadata": {},
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"outputs": [],
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"source": [
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"GSWParticle = parcels.Particle.add_variable(\n",
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" [\n",
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" parcels.Variable(\"temp\", dtype=np.float32, initial=np.nan),\n",
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" parcels.Variable(\"salt\", dtype=np.float32, initial=np.nan),\n",
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" parcels.Variable(\"density\", dtype=np.float32, initial=np.nan),\n",
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" ]\n",
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")\n",
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"\n",
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"# Initiate one Argo float in the Agulhas Current\n",
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"pset = parcels.ParticleSet(\n",
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" fieldset=fieldset,\n",
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" pclass=GSWParticle,\n",
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" lon=[32],\n",
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" lat=[-31],\n",
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" depth=[200],\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "6",
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"metadata": {},
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"source": [
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"Now (as the core part of this tutorial) define a custom kernel that uses the `gsw` toolbox to compute density from temperature and salinity:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7",
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"metadata": {},
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"outputs": [],
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"source": [
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"def ParcelsGSW(particles, fieldset):\n",
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" import gsw\n",
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"\n",
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" particles.temp = fieldset.thetao[particles]\n",
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" particles.salt = fieldset.so[particles]\n",
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" pressure = gsw.p_from_z(-particles.depth, particles.lat)\n",
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" particles.density = gsw.density.rho(particles.salt, particles.temp, pressure)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8",
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"metadata": {},
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"source": [
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"Finally, run the `ParcelsGSW` Kernel for one timestep and check (for Continuous Integration purposes) that the computed density is as expected:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9",
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"metadata": {},
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"outputs": [],
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"source": [
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"pset.execute(ParcelsGSW, runtime=np.timedelta64(1, \"s\"), dt=np.timedelta64(1, \"s\"))\n",
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"\n",
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"np.testing.assert_allclose(pset.density, [1026.8281], rtol=1e-5)\n",
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"\n",
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"print(\n",
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" f\"Temperature: {pset.temp[0]:.2f}, Salinity: {pset.salt[0]:.2f}, Density: {pset.density[0]:.2f}\"\n",
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")"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "parcels",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.13.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎parcels/_tutorial.py‎

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],
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"CopernicusMarine_data_for_Argo_tutorial": [
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"cmems_mod_glo_phy-cur_anfc_0.083deg_P1D-m_uo-vo_31.00E-33.00E_33.00S-30.00S_0.49-2225.08m_2024-01-01-2024-02-01.nc",
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"cmems_mod_glo_phy-so_anfc_0.083deg_P1D-m_so_31.00E-33.00E_33.00S-30.00S_0.49-2225.08m_2024-01-01-2024-02-01.nc",
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"cmems_mod_glo_phy-thetao_anfc_0.083deg_P1D-m_thetao_31.00E-33.00E_33.00S-30.00S_0.49-2225.08m_2024-01-01-2024-02-01.nc",
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],
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"DecayingMovingEddy_data": [

‎pixi.toml‎

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jupyter = "*"
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trajan = "*"
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matplotlib-base = ">=2.0.2"
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gsw = "*"
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[feature.docs.dependencies]
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numpydoc = "!=1.9.0"

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