Simulating Argo floats trajectories
Virtual Fleet is a python tool that simulates Argo floats trajectories and measurement.
Argo is an international program that collects information from inside the ocean using a fleet of robotic instruments that drift with the ocean currents and move up and down between the surface and a mid-water level.
The Virtual Fleet software is born during the EA-Rise project, an H2020 EU project, which aims to enhance and coordinate the capabilities of Argo network at the European level.
The main idea was to develop a tool that would help to improve the sampling strategy of argo floats within boundary currents regions. Argo floats have some tuneable functioning parameters: cycle duration, parking depth or profile depth for example, and this tool would allow the user to observe the impact of these parameters, and help to adjust the configuration and the deployment strategy of the floats, prior to the real launch of the instruments, so that, in the end, the studied region is more efficiently sampled.
How it works
Virtual Fleet is a python tool that uses Ocean Parcels to perform and analyze numerical simulations of virtual Argo floats.
Within a jupyter notebook or a script, the user:
- Set up a 3d current velocity dataset from Netcdf files. This can be a model output or a physical reanalysis, for example.
- Create some virtual floats and adjust their mission parameters (see figure 1)
- Run the simulation for a desired period of time
- Save the results, analyze the trajectories and more.
The code with it's documentation and some examples are available on github
With the code freely available, users can customize the behavior of their virtual floats to adapt the simulation to their specific needs.
Scripts available on GitHub
The code and examples are available on github.
Increase sampling efficiency
Adjust parameters and observe their impact in the simulation, to optimize configuration and the deployment strategy of the floats.
Benefits of WEkEO for Argo floats
Virtual Fleet is a python tool, working with some physical dataset as inputs. WekEO is therefore a very interesting asset as it provides:
- Efficient access to datasets from Copernicus Marine Service
- On-demand Jupyter notebooks and customizable python environments
- Documentation and user assistance for those services.
Contact us to know more about Argo floats.