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

• Main Author: Buongiorno Nardelli, Bruno
• Format: Dataset
• Language: unknown
• Published: 2020
• Subjects: ocean; observations; Cora; North Atlantic
• Online Access: https://zenodo.org/record/3943700; https://doi.org/10.5281/zenodo.3943700

We provide here the datasets used for the test and assessment 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 test dataset presented here includes different sets of co-located temperature and salinity vertical profiles: 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; synthetic profiles obtained through three different techniques: multivariate EOF reconstruction, a 2 layer feed-forward network (with 1000 units in each hidden layer) and a stacked LSTM network (with 2 LSTM layers and 35 hidden units) References: Buongiorno Nardelli, B.: A Deep Learning network to retrieve ocean hydrographic profiles from combined satellite and in situ measurements, 2020, submitted. Locarnini, R. A., Mishonov, A. V., Antonov, J. I., ...