Ocean mover's distance: using optimal transport for analysing oceanographic data.
Sangwon Hyun1, Aditya Mishra2, Christopher L Follett3
1Data Sciences and Operations, University of Southern California, California, CA, USA.
Proceedings. Mathematical, Physical, and Engineering Sciences
|June 27, 2022
Summary
The Wasserstein distance effectively compares satellite and model ocean data, improving marine ecosystem and climate predictions by analyzing spatial and temporal changes in chlorophyll. This method enhances our understanding of ocean dynamics.
Area of Science:
- Oceanography
- Biogeochemistry
- Remote Sensing
Background:
- Satellite remote sensing and biogeochemical models have advanced ocean studies.
- Comparing these complex, structured datasets across space and time is challenging.
- Existing methods like root-mean-squared error lack spatial comparison capabilities.
Purpose of the Study:
- To introduce and validate the Wasserstein distance as a metric for comparing structured oceanographic datasets.
- To enhance marine ecosystem and climate predictions by utilizing this novel metric.
- To assess temporal and spatial variability in ocean chlorophyll concentrations.
Main Methods:
- Application of the Wasserstein distance to compare satellite observations, model simulations, and in situ measurements of chlorophyll.
- Utilizing optimal transport vectors for data visualization and analysis.
- Focusing on chlorophyll as a proxy for phytoplankton biomass in the northeast Pacific.
Main Results:
- The Wasserstein distance quantifies differences in magnitude and spatial displacement, outperforming point-wise methods.
- It successfully isolates temporal and depth variability and identifies shifts in biogeochemical province boundaries.
- Temporal trends in satellite chlorophyll consistent with climate change predictions were revealed.
Conclusions:
- The Wasserstein distance is a powerful tool for comparing and integrating satellite and model data in oceanography.
- This metric improves marine ecosystem and climate predictions by accounting for spatial-temporal data structures.
- Optimal transport vectors offer a novel visualization approach for understanding ocean dynamics and validating models.
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