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.