xarray.Dataset.argo.canyon_b

xarray.Dataset.argo.canyon_b#

Dataset.argo.canyon_b(**kwargs)#

Nutrients and Carbonate System Variables predictor with CANYON-B

This is an implementation of the CANYON-B method: a bayesian neural network approach that estimates water-column nutrient concentrations and carbonate system variables ([1]). CANYON-B is based on the CANYON model ([2]) and provides more robust neural networks, that include a local uncertainty estimate for each predicted parameter.

When using this method, please cite the papers.

See also

canyon_b.predict(), canyon_b.input_list, canyon_b.output_list

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 with or without specifying input errors on pressure (epres, in dbar), temperature (etemp, in degC), salinity (epsal, in PSU) and oxygen (edoxy, in micromole/kg). For interested users, uncertainties on predicted parameters can also be included.

ds.argo.canyon_b.predict()
ds.argo.canyon_b.predict('PO4')
ds.argo.canyon_b.predict(['PO4', 'NO3'])
ds.argo.canyon_b.predict(['PO4', 'NO3'], include_uncertainties=True)
ds.argo.canyon_b.predict(['PO4', 'NO3'], epres=0.5, etemp=0.005, epsal=0.005, edoxy=0.01)
ds.argo.canyon_b.predict(['PO4', 'NO3'], epres=0.5, etemp=0.005, epsal=0.005, edoxy=0.01, include_uncertainties=True)
By default, if no input errors are specified, the following default values are used:

  • epres = 0.5 dbar

  • etemp = 0.005 degC

  • epsal = 0.005 PSU

  • edoxy = 1% of DOXY value

Notes

This Python implementation is largely inspired by work from Raphael Bajon (RaphaelBajon) which is available at RaphaelBajon/canyonbpy and from the EuroGO-SHIP organization (EuroGO-SHIP/AtlantOS_QC)

References

[1]

Bittig, H. C., Steinhoff, T., Claustre, H., Fiedler, B., Williams, N. L., Sauzede, R., Kortzinger, A., and Gattuso, J. P. (2018). An alternative to static climatologies: Robust estimation of open ocean CO2 variables and nutrient concentrations from T, S, and O2 data using Bayesian neural networks. Frontiers in Marine Science, 5, 328. https://doi.org/10.3389/fmars.2018.00328

[2]

Sauzede, R., Bittig, H. C., Claustre, H., Pasqueron de Fommervault, O., Gattuso, J. P., Legendre, L., and Johnson, K. S. (2017). Estimates of water-column nutrient concentrations and carbonate system parameters in the global ocean: A novel approach based on neural networks. Frontiers in Marine Science, 4, 128. https://doi.org/10.3389/fmars.2017.00128