Unsupervised Learning of Sea Surface Height Interpolation from Multi-variate Simulated Satellite Observations

Core Concepts

It is possible to use sea surface temperature information to improve the reconstruction of sea surface height from satellite observations, even in an unsupervised setting where no ground truth is available.

Abstract

The authors designed a realistic simulation of satellite observations of sea surface height and temperature to evaluate interpolation methods. They introduced a deep learning network called Attention-Based Encoder-Decoder (ABED) that can leverage sea surface temperature information to improve sea surface height reconstruction. The authors compared supervised and unsupervised training strategies for ABED. In the unsupervised setting, the network is trained solely on satellite observations without access to ground truth data. The authors found that using sea surface temperature, even with noise, can enhance sea surface height reconstruction compared to using sea surface height observations alone. The unsupervised approach performed slightly worse than the supervised one, but still showed significant improvements over existing state-of-the-art methods. The authors also evaluated the ability of the different methods to detect and characterize mesoscale eddies, which are important ocean features. They found that incorporating sea surface temperature information led to better eddy detection and more accurate estimation of eddy properties like radius and velocity.

Stats

The root mean squared error (RMSE) of the sea surface height reconstruction decreased by 41% compared to existing state-of-the-art methods. The RMSE of the estimated eastward and northward surface currents decreased by 22% and 25% respectively compared to using sea surface height observations alone.

Quotes

"It is possible, even in an unsupervised setting to use SST to improve reconstruction performance compared to SST-agnostic interpolations." "We find that it is possible, even in an unsupervised setting to use SST to improve reconstruction performance compared to SST-agnostic interpolations."

Key Insights Distilled From

Learning of Sea Surface Height Interpolation from Multi-variate Simulated Satellite Observations

by Theo Archamb... at arxiv.org 05-06-2024

https://arxiv.org/pdf/2310.07626.pdf

Deeper Inquiries

How could the proposed unsupervised approach be extended to incorporate additional satellite observations beyond sea surface height and temperature

The proposed unsupervised approach could be extended to incorporate additional satellite observations beyond sea surface height and temperature by integrating data from other sensors or sources. For example, incorporating data from ocean color sensors could provide information on chlorophyll concentration, which is crucial for understanding phytoplankton distribution and primary productivity in the ocean. This additional data could enhance the interpolation process by providing more comprehensive information about the ocean ecosystem. Furthermore, integrating data from scatterometers could offer insights into wind speed and direction, which are essential for studying ocean-atmosphere interactions and surface currents. By combining multiple types of satellite observations, the unsupervised approach could be expanded to create a more holistic view of the ocean state and dynamics.

What are the limitations of the simulated dataset used in this study, and how could the methodology be applied to real-world satellite data with its inherent complexities

The limitations of the simulated dataset used in this study include the simplifications and assumptions made in the simulation process. While the simulated data provides a controlled environment for testing the interpolation methods, it may not fully capture the complexities and variability present in real-world satellite observations. To apply the methodology to real-world satellite data, researchers would need to address several challenges. These challenges include dealing with data gaps, noise, and errors inherent in satellite observations, as well as accounting for the variability and uncertainties associated with different sensors and measurement techniques. Additionally, the methodology would need to be adapted to handle the large volumes of real-time satellite data and the processing requirements for operational applications. By addressing these limitations and complexities, the methodology could be effectively applied to real-world satellite data to improve sea surface height interpolation and enhance our understanding of ocean dynamics.

Could the insights gained from this work on sea surface height interpolation be applied to other geophysical inverse problems where incomplete observations are available

The insights gained from this work on sea surface height interpolation could be applied to other geophysical inverse problems where incomplete observations are available. For example, similar methodologies could be used to interpolate other ocean variables such as sea surface temperature, salinity, or ocean currents. Additionally, the approach could be extended to address inverse problems in other geophysical fields such as atmospheric science, geology, or environmental monitoring. By leveraging deep learning techniques and unsupervised learning strategies, researchers could improve the reconstruction of complex geophysical variables from sparse or incomplete observations. This approach has the potential to enhance our understanding of Earth systems and improve the accuracy of models used for climate prediction, natural hazard assessment, and environmental monitoring.