🖥️ Mimicking an Argo float#
This tutorial shows how simple it is to construct a Kernel in Parcels that mimics the vertical movement of Argo floats.
from datetime import timedelta
import numpy as np
import parcels
import parcels.tutorial
We first define the Kernel for the vertical movement cycle of the Argo float.
# Define the new Kernel that mimics Argo vertical movement
mindepth = 1.0 # minimum depth in m
driftdepth = 1000 # maximum drift depth in m
maxdepth = 2000 # maximum depth in m
vertical_speed = 0.10 # sink and rise speed in m/s
cycletime = 10 * 86400 # total time of cycle in seconds
drifttime = 9 * 86400 # time of deep drift in seconds
@parcels.validate_kernel
def ArgoVerticalMovement(particles, fieldset):
# Split particles based on their current cycle_phase
ptcls0 = particles[particles.cycle_phase == 0]
ptcls1 = particles[particles.cycle_phase == 1]
ptcls2 = particles[particles.cycle_phase == 2]
ptcls3 = particles[particles.cycle_phase == 3]
ptcls4 = particles[particles.cycle_phase == 4]
# Phase 0: Sinking with vertical_speed until depth is driftdepth
ptcls0.dz += vertical_speed * ptcls0.dt
next_phase = ptcls0.z + ptcls0.dz >= driftdepth
ptcls0.cycle_phase[next_phase] = 1
ptcls0.dz[next_phase] = driftdepth - ptcls0.z[next_phase] # avoid overshoot
# Phase 1: Drifting at depth for drifttime seconds
ptcls1.drift_age += ptcls1.dt
next_phase = ptcls1.drift_age >= drifttime
ptcls1.cycle_phase[next_phase] = 2
ptcls1.drift_age[next_phase] = 0 # reset drift_age for next cycle
# Phase 2: Sinking further to maxdepth
ptcls2.dz += vertical_speed * ptcls2.dt
next_phase = ptcls2.z + ptcls2.dz >= maxdepth
ptcls2.cycle_phase[next_phase] = 3
ptcls2.dz[next_phase] = maxdepth - ptcls2.z[next_phase] # avoid overshoot
# Phase 3: Rising with vertical_speed until at surface
ptcls3.dz -= vertical_speed * ptcls3.dt
ptcls3.temp = fieldset.thetao[ptcls3.t, ptcls3.z, ptcls3.y, ptcls3.x]
next_phase = ptcls3.z + ptcls3.dz <= mindepth
ptcls3.cycle_phase[next_phase] = 4
ptcls3.dz[next_phase] = mindepth - ptcls3.z[next_phase] # avoid overshoot
# Phase 4: Transmitting at surface until cycletime is reached
next_phase = ptcls4.cycle_age >= cycletime
ptcls4.cycle_phase[next_phase] = 0
ptcls4.cycle_age[next_phase] = 0 # reset cycle_age for next cycle
ptcls4.temp = np.nan # no temperature measurement when at surface
particles.cycle_age += particles.dt # update cycle_age
And then we can run Parcels with this ArgoVerticalMovement Kernel.
Below we use the horizontal velocity fields of CopernicusMarine, which are provided as example_data with Parcels.
# Load the CopernicusMarine data in the Agulhas region from the example_datasets
ds_fields = parcels.tutorial.open_dataset(
"CopernicusMarine_data_for_Argo_tutorial/data"
)
# Select fields
fields = {
"U": ds_fields["uo"],
"V": ds_fields["vo"],
"thetao": ds_fields["thetao"],
}
# Convert to SGRID-compliant dataset and create FieldSet
ds_fset = parcels.convert.copernicusmarine_to_sgrid(fields=fields)
fieldset = parcels.FieldSet.from_sgrid_conventions(ds_fset)
fieldset.to_windowed_arrays()
# Define a new Particle type including extra Variables
ArgoParticle = parcels.Particle.add_variable(
[
parcels.Variable("cycle_phase", dtype=np.int32, initial=0.0),
parcels.Variable("cycle_age", dtype=np.float32, initial=0.0),
parcels.Variable("drift_age", dtype=np.float32, initial=0.0),
parcels.Variable("temp", dtype=np.float32, initial=np.nan),
]
)
# Initiate one Argo float in the Agulhas Current
pset = parcels.ParticleSet(
fieldset=fieldset,
pclass=ArgoParticle,
x=[32],
y=[-31],
z=[mindepth],
)
# combine Argo vertical movement Kernel with built-in Advection Kernel
kernels = [
ArgoVerticalMovement,
parcels.kernels.AdvectionRK2,
]
# Create a ParticleFile object to store the output
output_file = parcels.ParticleFile(
"argo_float.parquet",
outputdt=timedelta(minutes=15),
)
# Now execute the Kernels for 30 days, saving data every 30 minutes
pset.execute(
kernels,
runtime=timedelta(days=30),
dt=timedelta(minutes=15),
output_file=output_file,
)
Show code cell output
Hide code cell output
INFO: Output files are stored in argo_float.parquet
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Now we can plot the trajectory of the Argo float with some simple calls to parcels.read_particlefile() and matplotlib.
First plot the depth as a function of time, with the temperature as colour (only on the upcast).
import matplotlib.pyplot as plt
df = parcels.read_particlefile("argo_float.parquet")
fig = plt.figure(figsize=(13, 6))
ax = plt.axes()
ax.plot(df["t"], df["z"], color="gray")
cb = ax.scatter(df["t"], df["z"], c=df["temp"], s=20, marker="o", zorder=2)
ax.set_xlabel("Time [days]")
ax.set_ylabel("Depth (m)")
ax.invert_yaxis()
fig.colorbar(cb, label="Temperature (°C)")
plt.show()
We can also make a 3D plot of the trajectory coloured by temperature.
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure(figsize=(13, 8))
ax = plt.axes(projection="3d")
ax.view_init(azim=-145)
ax.plot3D(df["x"], df["y"], df["z"], color="gray")
cb = ax.scatter(df["x"], df["y"], df["z"], c=df["temp"], s=20, marker="o", zorder=2)
ax.set_xlabel("Longitude")
ax.set_ylabel("Latitude")
ax.set_zlabel("Depth (m)")
ax.set_zlim(df["z"].max(), 0)
fig.colorbar(cb, label="Temperature (°C)")
plt.show()
