Published April 15, 2024 | Version v1
Dataset Open

Mediterranean Sea Super Resolved Geostrophic Currents

Creators

Contributors

  • 1. ROR icon Collecte Localisation Satellites (France)

Description

Super Resolved Geostrophic Surface Currents for the Mediterranean Sea (2008-01-02 to 2019-12-31) computed by means of Convolutional Neural Networks (CNNs) and Generated using E.U. Copernicus Marine Service Information. The generation algorithm is described in Ciani et al. 2024 (https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1164/) and Buongiorno Nardelli et al. 2022. 

The main directory contains 5 subfolders:

  1. ADT_MFSeas4_Copernicus. Daily data for the year 2017: please refer to Clementi et al. 2019;
  2. ADT_MFSeas4_OI_synth_MAP_MED_DT2018_4SAT (Satellite Equivalent Absolute Dynamic Topography, SE-ADT). Daily data for the year 2017: the generation is detailed in Ciani et al. 2021 (section 2.1, item #3);
  3. SST_MFSeas4_Copernicus. Daily data for the year 2017: please refer to Clementi et al. 2019;
  4. SST_MFSeas4_OI_synth_MAP_MED_HR (Satellite Equivalent Sea Surface Temperature, SE-SST), Daily data for the year 2017: the SE-SSTs merge infromation from SST_MFSeas4_Copernicus  with the multi-sensor L3S satellite SSTs for the Mediterranean Area (product ID: SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012). Such L3S SSTs have gaps where infrared SST retrieval is impossible (e.g., due to cloud cover) or where single-sensor satellite SSTs are deemed of poor quality.  Choosing the data contained in subfolder #3, a synthetic model-derived L3S SST time series was generated introducing synthetic gaps in the original modelled SSTs. Subsequently, gap-free SSTs, along with an estimate of uncertainty, are generated using standard Optimal Interpolation (OI) via a dedicated algorithm, following methods outlined in Buongiorno Nardelli et al. (2013);
  5. dADR-SR_ADT_SST_dtSST_MED: super-resolved geostrophic currents. daily data (2008-01-02 to 2019-12-31). This dataset is generated using E.U. Copernicus Marine Service data of gridded gap-free (Level 4) ADTs and SSTs over the Mediterranean Area and employing a CNN trained by means of the dataset contained in the subfolders 1-4. The CNN architecture/algorithm is described in Ciani et al. (submitted) and Buongiorno Nardelli et al. 2022.

All data are provided in NetCDF format

Files

Super_Resolved_Geostrophic_Currents.zip

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Additional details

Funding

WOC (World Ocean Circulation) ESA Contract No. 4000130730/20/I-NB
European Space Agency
CIRCOL (ocean CIRculation from ocean COLour observations) ESA Contract No. 4000128147/19/I-DT
European Space Agency

References

  • Estimating Ocean Currents from Joint Reconstruction of Absolute Dynamic Topography and Sea Surface Temperature through Physics Informed Deep Learning Algorithms", by D. Ciani, C. Fanelli and B. Buongiorno Nardelli, submitted.);
  • Buongiorno Nardelli, B., Cavaliere, D., Charles, E., & Ciani, D. (2022). Super-resolving ocean dynamics from space with computer vision algorithms. Remote Sensing, 14(5), 1159
  • Ciani, D., Charles, E., Buongiorno Nardelli, B., Rio, M. H., & Santoleri, R. (2021). Ocean currents reconstruction from a combination of altimeter and ocean colour data: A feasibility study. Remote Sensing, 13(12), 2389.
  • Clementi E., Pistoia J., Escudier R., Delrosos D., Drudi M., Grandi A., Lecci R., Creti S., Ciliberti S.A., Coppini G., Masina S., Pinardi N. (2019). Mediterranean Sea Analysis and Forecast (CMEMS MED-Currents 2016-2019)[Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). DOI: https://doi.org/10.25423/CMCC/MEDSEA_ANALYSIS_FORECAST_PHY_006_013_EAS4
  • Buongiorno Nardelli, B., Tronconi, C., Pisano, A., & Santoleri, R. (2013). High and Ultra-High resolution processing of satellite Sea Surface Temperature data over Southern European Seas in the framework of MyOcean project. Remote Sensing of Environment, 129, 1-16