add input field figure

Author: reint-fischer

docs/getting_started/tutorial_quickstart.md

@@ -13,6 +13,7 @@ TODO: Completely rewrite examples/parcels_tutorial.ipynb to be this quickstart t
 Welcome to the **Parcels** quickstart tutorial, in which we will go through all the necessary steps to run a simulation. The code in this notebook can be used as a starting point to run Parcels in your own environment. Along the way we will familiarize ourselves with some specific classes and methods. If you are ever confused about one of these and want to read more, we have a [concepts overview](concepts_overview.md) discussing them in more detail. Let's dive in!
 
 ## Imports
+
 Parcels depends on `xarray`, expecting inputs in the form of [`xarray.Dataset`](https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html) and writing output files that can be read with xarray.
 
 ```{code-cell}
@@ -22,11 +23,12 @@ import parcels
 ```
 
 ## Input 1: `FieldSet`
+
 A Parcels simulation of Lagrangian trajectories of virtual particles requires two inputs; the first is a set of hydrodynamics fields in which the particles are tracked. This set of vectorfields, with `U`, `V` (and `W`) flow velocities, can be read in to a `parcels.FieldSet` object from many types of models or observations. Here we provide an example using a subset of the [Global Ocean Physics Reanalysis](https://doi.org/10.48670/moi-00021) from the Copernicus Marine Service.
 
 ```{code-cell}
 example_dataset_folder = parcels.download_example_dataset(
-    "CopernicusMarine_data"
+    "CopernicusMarine_data_for_Argo_tutorial"
 )
 
 ds_in = xr.open_mfdataset(f"{example_dataset_folder}/*.nc", combine="by_coords")
@@ -35,20 +37,30 @@ ds_in.load()  # load the dataset into memory
 fieldset = parcels.FieldSet.from_copernicusmarine(ds_in)
 ```
 
-The reanalysis contains `U`, `V`, potential temperature (`thetao`) and salinity (`so`):
+The reanalysis data files contain `U`, `V`, potential temperature (`thetao`) and salinity (`so`) fields:
 
 ```{code-cell}
 ds_in
 ```
+
 The subset contains a region of the Agulhas current along the southeastern coast of Africa:
+
 ```{code-cell}
 import matplotlib.pyplot as plt
 import cartopy.crs as ccrs
-import cartopy.feature as cfeature
-ds_in
+
+temperature = ds_in.isel(time=0, depth=0).thetao.plot(cmap="magma",subplot_kws=dict(projection=ccrs.PlateCarree()))
+velocity = ds_in.isel(time=0, depth=0).plot.quiver(x="longitude", y="latitude", u="uo", v="vo")
+
+temperature.axes.set_extent([30, 34, -34, -29])
+gl = temperature.axes.gridlines(draw_labels=True)
+gl.top_labels = False
+gl.right_labels = False
+temperature.axes.coastlines()
 ```
 
 ## Input 2: `ParticleSet`
+
 Now that we have read in the hydrodynamic data, we need to provide our second input: the virtual particles for which we will calculate the trajectories. We need to define the initial time and position and read those into a `parcels.ParticleSet` object, which also needs to know about the `FieldSet` in which the particles "live". Finally, we need to specify the type of `parcels.Particle` we want to use. The default particles have `time`, `lon`, `lat`, and `z` to keep track of, but with Parcels you can easily build your own particles to mimic plastic or an [ARGO float](../user_guide/tutorial_Argofloats.ipynb), adding variables such as size, temperature, or age.
 
 ```{code-cell}
@@ -65,11 +77,13 @@ pset = parcels.ParticleSet(
 ```
 
 ## Compute: `Kernel`
+
 After setting up the input data, we need to specify how to calculate the advection of the particles. These calculations, or numerical integrations, will be performed by what we call a `Kernel`, operating on each particle in the `ParticleSet`. The most common calculation is the advection of particles through the velocity field. Parcels comes with a number of standard kernels, from which we will use the Euler-forward advection kernel:
 
 ```{note}
 TODO: link to list of included kernels
 ```
+
 ```{code-cell}
 kernels = parcels.kernels.AdvectionEE
 ```