xarray.Dataset.argo.canyon_med

xarray.Dataset.argo.canyon_med#

Dataset.argo.canyon_med(**kwargs)#

Implementation of the CANYON-MED method.

CANYON-MED is a Regional Neural Network Approach to Estimate Water-Column Nutrient Concentrations and Carbonate System Variables in the Mediterranean Sea ([1], [2]).

When using this method, please cite the papers.

Examples

Load data, they must contain oxygen measurements:

>>> from argopy import DataFetcher
>>> ArgoSet = DataFetcher(ds='bgc', mode='standard', params='DOXY', measured='DOXY').float(1902605)
>>> ds = ArgoSet.to_xarray()

Once input data are loaded, make all or selected parameters predictions:

>>> ds.argo.canyon_med.predict()
>>> ds.argo.canyon_med.predict('PO4')
>>> ds.argo.canyon_med.predict(['PO4', 'NO3'])

Notes

This Python implementation is largely inspired by work from Marine Fourrier (MarineFou) and Florian Ricour (fricour) from LOV.

First Python implementation was published here: euroargodev/CANYON-MED

References

[1]

Fourrier, M., Coppola, L., Claustre, H., D’Ortenzio, F., Sauzède, R., and Gattuso, J.-P. (2020). A Regional Neural Network Approach to Estimate Water-Column Nutrient Concentrations and Carbonate System Variables in the Mediterranean Sea: CANYON-MED. Frontiers in Marine Science 7. doi:10.3389/fmars.2020.00620.

[2]

Fourrier, M., Coppola, L., Claustre, H., D’Ortenzio, F., Sauzède, R., and Gattuso, J.-P. (2021). Corrigendum: A Regional Neural Network Approach to Estimate Water-Column Nutrient Concentrations and Carbonate System Variables in the Mediterranean Sea: CANYON-MED. Frontiers in Marine Science 8. doi:10.3389/fmars.2021.650509.