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

• Main Author: Buongiorno Nardelli, Bruno
• Format: Other/Unknown Material
• Language: unknown
• Published: Zenodo 2020
• Subjects: North Atlantic; Sea ice
• Online Access: https://doi.org/10.5281/zenodo.4040843

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 byCopernicus Marine Environment Monitoring ServiceCORA 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 Temperaturetaken 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/ , ...