Developing a deep Learning network to retrieve ocean hydrographic profiles in the North Atlantic from combined satellite and in situ measurements: training datasets

We provide here the datasets used for the development of a deep learning algorithm which is presently candidate for the development of a daily 3D ocean product covering the North Atlantic at 1/10° resolution, over the 2010-2018 period, as part of the European Space Agency World Ocean Circulation project (ESA-WOC). The method is based on a stacked Long Short-Term Memory neural network, coupled to a Monte-Carlo dropout approach, and allows to project satellite-derived sea surface temperature, sea surface salinity and absolute dynamic topography data at depth after training with sparse co-located in situ vertical hydrographic profiles (Buongiorno Nardelli, 2020, doi:10.3390/rs12193151). 

The training/test dataset presented here includes different sets of co-located temperature and salinity vertical profiles and corresponding satellite surface data: 

• in situ observations extracted from the quality controlled Argo and CTD profiles produced by Copernicus Marine Environment Monitoring Service CORA 5.2 (http://marine.copernicus.eu/services-portfolio/access-to-products/, product_id: INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b, doi: 10.17882/46219TS1, Szekely et al., 2019) and interpolated through a spline on a regularly spaced vertical grid (with 10 m intervals);
• climatological profiles extracted from World Ocean Atlas 2013 optimally interpolated monthly fields (Locarnini et al., 2013; Zweng et al., 2013), interpolated through a spline on a regularly spaced vertical grid (with 10 m intervals), upsized to a 1/10° horizontal grid through a cubic spline and linearly interpolated in time between the central day of each month;
• co-located Sea Surface Temperature taken from the level 4 (L4, i.e. interpolated) multi-year reprocessed Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) developed by U.K. Met Office and distributed (upon free registration) through the Copernicus Marine Environment Monitoring Service (CMEMS, http://marine.copernicus.eu/services-portfolio/access-to-products/, product_id=SST_GLO_SST_L4_REP_OBSERVATIONS_010_011);
• co-located Sea Surface Salinity taken from dataset developed within ESA-WOC project (https://doi.org/10.5281/zenodo.3943813);
• co-located Absolute Dynamic Topography (ADT) data distributed by CMEMS as reprocessed data (http://marine.copernicus.eu/services-portfolio/access-to-products/, product_id: SEALEVEL_GLO_PHY_L4_REP_OBSERVATIONS_008_047), upsized here to the ESA-WOC 1/10°x1/10° grid through a cubic spline and adjusted to insitu steric heights by regressing steric heights and co-located ADT data in the neighbourhood of each grid point, considering matchups within a temporal window of 10 days (as in Buongiorno Nardelli et al., 2017).