Reconstruction of Three-Dimensional Temperature and Salinity Fields From Satellite Observations
Date
2021-11-07
Journal Title
Journal ISSN
Volume Title
Publisher
Journal of Geophysical Research: Oceans
Abstract
Observation of the ocean is crucial to the studies of ocean dynamics, climate change, and biogeochemical cycle. However, current oceanic observations are patently insufficient, because the in situ observations are of difficulty and high cost while the satellite remote-sensed measurements are mainly the sea surface data. To make up for the shortage of ocean interior data and make full use of the abundant satellite data, here we develop a data-driven deep learning model to estimate ocean subsurface and interior variables from satellite-observed sea surface data. Exclusively and simply using satellite data, three-dimensional ocean temperature and salinity fields are successfully reconstructed, which are at 26 level depths from 0 to 2,000 m. We further design a scheme to increase the horizontal resolution from 1° to 1/4°, which is higher than the Argo gridded data. Estimations from our model are accurate, reliable, and stable for a wide range of research areas and periods. Dynamic height fields that are derived from the estimated temperature and salinity, as well as the associated ocean geostrophic flows, are also calculated and analyzed, which indicates the potentials of our model for reconstructing the ocean circulation fields as well. This study enriches oceanic observations with respect to vertical dimension and horizontal resolution, which can largely make up for the paucity of the subsurface and deep ocean observation, both before and during Argo era. This work also provides some new foundations for and insights into geoscience and climate change fields.
Plain Language Summary:
Ocean data are important for ocean science and climate change studies; however, oceanic observations are difficult and costly and thus are still very sparse in space and time. Satellites have been providing plentiful oceanic observations, but these data are only at the sea surface. To fully utilize the copious satellite data and to make up for the shortage of ocean interior data. Here, we establish a deep learning model to connect the surface ocean with the subsurface and deep oceans, through which, subsurface and deep ocean temperature and salinity data are estimated from the surface data observed by satellites. We also design a new scheme to improve the horizontal resolution of the obtained data. The results show that our model successfully reconstructed the three-dimensional field data of temperature and salinity. Our model could facilitate ocean science studies by largely enriching the ocean data availability.
Description
An edited version of this paper was published by AGU. Copyright 2021 American Geophysical Union. This article was originally published in Journal of Geophysical Research: Oceans. The version of record is available at: https://doi.org/10.1029/2021JC017605
Keywords
Citation
Meng, L., Yan, C., Zhuang, W., Zhang, W., & Yan, X.-H. (2021). Reconstruction of three-dimensional temperature and salinity fields from satellite observations. Journal of Geophysical Research: Oceans, 126, e2021JC017605. https://doi.org/10.1029/2021JC017605