Tutorial showing how to use the Parcels FieldSet.advancetime method

In many real-world applications, particles are run for long times, using many snapshots of the hydrographic data. If these files are large, having to read them all into memory can take a significant amount of resources

The FieldSet.advancetime method allows a simulation where only three snapshots of the hydrodynamic fields are in memory at any time, and they can be cycled through. This brief tutorial shows how to use the FieldSet.advancetime method to read in only a sebset of all the time slices available at once

We start with importing the relevant modules

In [1]:
from parcels import FieldSet, ParticleSet, JITParticle, AdvectionRK4
from datetime import timedelta as delta
import numpy as np
from glob import glob
from os import path

Now define a function that loads the Globcurrent fields from the GlobCurrent_example_data directory

In [2]:
def loadglobcurrentfile(filenames):
    filenames = {'U': filenames,
                 'V': filenames}
    variables = {'U': 'eastward_eulerian_current_velocity',
                 'V': 'northward_eulerian_current_velocity'}
    dimensions = {'lat': 'lat',
                  'lon': 'lon',
                  'time': 'time'}
    return FieldSet.from_netcdf(filenames, variables, dimensions)

We can create a list of all the files available in the GlobCurrent_example_data directory using

In [3]:
files = glob(str(path.join('GlobCurrent_example_data','20*.nc')))

Now we read in the first three files into the fieldset (by using files[0:3])

In [4]:
fieldset = loadglobcurrentfile(files[0:3])
WARNING: Casting lon data to np.float32
WARNING: Casting lat data to np.float32
WARNING: Casting depth data to np.float32

Now create a ParticleSet object

In [5]:
pset = ParticleSet(fieldset=fieldset, pclass=JITParticle, lon=[20], lat=[-35])

Now we can advect the particles, for ten days. Normally, since we only have three days in memory, we can not advect that long. But in this case, we can use a custom for-loop to constantly update the fieldset with the latest snapshot.

In [6]:
for i in range(10):
    pset.execute(AdvectionRK4,            # First advect the particles
                 starttime=pset[0].time,  # Note that starttime is constantly updated to be the time of the pset
                 runtime=delta(days=1),   # runtime needs to be equal to the time between snapshots

    # Then update the fieldset using the advancetime method
INFO: Compiled JITParticleAdvectionRK4 ==> /var/folders/r2/8593q8z93kd7t4j9kbb_f7p00000gn/T/parcels-501/27805ff3aa34ba12ddb373f3f2cb1d1b.so

With this relatively simple setup, Parcels can be run on hydrodynamic datasets that are potentially hundreds of gigabytes in size; just as long as any single snapshot isn't too big.