EGU22-6350
https://doi.org/10.5194/egusphere-egu22-6350
EGU General Assembly 2022
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

OSDYN: a new python tool for the analysis of high-volume ocean outputs.

Valerie Garnier1, Jean-Francois Le Roux1, Justus Magin1, Tina Odaka1, Pierre Garreau1, Martial Boutet1,2, Stephane Raynaud3, Claude Estournel4, and Jonathan Beuvier5
Valerie Garnier et al.
  • 1Ifremer, ODE/LOPS/OC, PLOUZANE, France (valerie.garnier@ifremer.fr)
  • 2M2C UMR66143, CNRS, Université de Caen
  • 3SHOM-HOM
  • 4LEGOS/OMP - CNRS, CNES, IRD, Université Toulouse III - Paul Sabatier
  • 5Mercator Ocean International, Toulouse

OSDYN (Observations and Simulations of the DYNamics) is a Python library that proposes diagnostics to explore the dynamics of the ocean and its interactions with the atmosphere and waves. Its main strengths are its genericity concerning the different types of netCDF files and its ability to handle large volumes of data.

Dedicated to large data sets such as in-situ, satellite, and numerical model observations, OSDYN is particularly powerful to manage different types of Arakawa-C grids and vertical coordinates (Nemo, Croco, Mars, Symphonie, WW3, MesoNH). Based on common Pangeo stack (xarray, dask, xgcm), OSDYN provides data readers that standardize the dimensions, coordinates, and variables names and properties of the datasets. Thus, all python diagnostics can be shared regardless of the model outputs.

Thanks to progress made using kerchunk and efforts on transforming metadata of Ifremer’s HPC center (auto-kerchunk), the reading of a large amount of netCDF files is fast and the selection of sub-domains or specific variables is almost immediate.

Jupyter notebooks will detail the implementation of three kinds of analyses. The first one focuses on climatologic issues. In order to compare modeled and satellite sea surface temperatures, the second one addresses spatial interpolation and comparison of data when some may be missing. Lastly, the third analysis provides an overview of how diagnostics describing the formation of deep water masses can be used from different data sets.

How to cite: Garnier, V., Le Roux, J.-F., Magin, J., Odaka, T., Garreau, P., Boutet, M., Raynaud, S., Estournel, C., and Beuvier, J.: OSDYN: a new python tool for the analysis of high-volume ocean outputs., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6350, https://doi.org/10.5194/egusphere-egu22-6350, 2022.