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

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., Boyer, T. P., Garcia, H. E., Baranova, O. K., Zweng, M. M., Paver, C. R., Reagan, J. R., Johnson, D. R., Hamilton, M. and Seidov, D.: World Ocean Atlas 2013. Vol. 1: Temperature., S. Levitus, Ed.; A. Mishonov, Tech. Ed.; NOAA Atlas NESDIS, 73(September), 40, doi:10.1182/blood-2011-06-357442, 2013.

Szekely, T., Gourrion, J., Pouliquen, S. and Reverdin, G.: The CORA 5.2 dataset for global in situ temperature and salinity measurements: Data description and validation, Ocean Sci., 15(6), 1601–1614, doi:10.5194/os-15-1601-2019, 2019.

Zweng, M. M., Reagan, J. R., Antonov, J. I., Mishonov, A. V., Boyer, T. P., Garcia, H. E., Baranova, O. K., Johnson, D. R., Seidov, D. and Bidlle, M. M.: World Ocean Atlas 2013, Volume 2: Salinity, NOAA Atlas NESDIS, 119(1), 227–237, doi:10.1182/blood-2011-06-357442, 2013.

本数据集用于测试与评估一款深度学习算法，该算法目前作为候选方案，用于开发2010-2018年时段、分辨率为1/10°、覆盖北大西洋的逐日三维海洋产品，属于欧洲空间局世界海洋环流计划（European Space Agency World Ocean Circulation, ESA-WOC）的研究内容。

该方法基于堆叠长短期记忆（Long Short-Term Memory, LSTM）神经网络，并结合蒙特卡洛丢弃法（Monte-Carlo dropout）策略，通过稀疏匹配的原位垂直海洋水文剖面进行训练后，可将卫星反演的海表温度、海表盐度与绝对动力地形数据投影至深度维度（Buongiorno Nardelli, 2020, doi:10.3390/rs12193151）。

本次提供的测试数据集包含多组匹配的温盐垂直剖面：

1. 原位观测数据：取自哥白尼海洋环境监测服务（Copernicus Marine Environment Monitoring Service）发布的经过质量控制的Argo与CTD剖面数据集CORA 5.2（访问地址：http://marine.copernicus.eu/services-portfolio/access-to-products/，产品编号：INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b，doi: 10.17882/46219TS1，Szekely等, 2019），并通过样条插值法将数据插值至间隔为10米的规则垂直网格；
2. 气候态剖面数据：取自《世界海洋图集2013》（World Ocean Atlas 2013）的最优插值月平均场（Locarnini等, 2013; Zweng等, 2013），先通过样条插值法插值至间隔为10米的规则垂直网格，再通过三次样条插值法插值至1/10°水平网格，并在每月中间日之间进行时间线性插值；
3. 合成剖面数据：通过三种不同方法生成：多变量经验正交函数（Empirical Orthogonal Function, EOF）重构法、2层前馈神经网络（每隐藏层含1000个神经元）以及堆叠长短期记忆神经网络（含2个LSTM层与35个隐藏单元）

参考文献：
Buongiorno Nardelli, B.: 用于从卫星与原位观测联合数据反演海洋水文剖面的深度学习网络，2020，已投稿。
Locarnini, R. A., Mishonov, A. V., Antonov, J. I., Boyer, T. P., Garcia, H. E., Baranova, O. K., Zweng, M. M., Paver, C. R., Reagan, J. R., Johnson, D. R., Hamilton, M. 及 Seidov, D.: 《世界海洋图集2013 第1卷：温度》，S. Levitus 编辑；A. Mishonov 技术编辑；NOAA Atlas NESDIS, 73(9月), 40, doi:10.1182/blood-2011-06-357442, 2013.
Szekely, T., Gourrion, J., Pouliquen, S. 及 Reverdin, G.: 用于全球原位温盐观测的CORA 5.2数据集：数据描述与验证，《海洋科学》（Ocean Sci.）, 15(6), 1601–1614, doi:10.5194/os-15-1601-2019, 2019.
Zweng, M. M., Reagan, J. R., Antonov, J. I., Mishonov, A. V., Boyer, T. P., Garcia, H. E., Baranova, O. K., Johnson, D. R., Seidov, D. 及 Bidlle, M. M.: 《世界海洋图集2013 第2卷：盐度》，NOAA Atlas NESDIS, 119(1), 227–237, doi:10.1182/blood-2011-06-357442, 2013.