Rick de Kreij: Statistical inversion of surface tracers to infer fine-scale near-surface ocean currents

Atmosphere/ML Workshop 2026 Atmosphere/ML Workshop 2026 Abstracts
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Rick de Kreij: Statistical inversion of surface tracers to infer fine-scale near-surface ocean currents

Author Information:
Rick de Kreij1, Andrew Zammit Mangion2, Matt Rayson1, Nicole Jones1, Andrew Zulberti1
1 The University of Western Australia, School of Earth and Oceans, and the Oceans Institute, Perth, WA, Australia, and 2 School of Mathematics and Statistics, University of New South Wales, Sydney, NSW, Australia, 3 School of Mathematics and Applied Statistics, University of Wollongong, Wollongong, NSW, Australia

ABSTRACT BODY

  1. Introduction and scientific context
    Measuring sea surface currents (SSC) directly is challenging. Instead, SSC are often inferred from indirect measurements like altimetry, which only provide large-scale (>100 km) geostrophically-balanced velocity estimates. Here, we present a statistical inversion model to predict fine-scale SSC using remotely sensed sea surface temperature (SST) data.

  2. Methodology/project summary
    Our approach employs Gaussian Process (GP) regression informed by a two-dimensional tracer transport equation. This method yields a predictive distribution of SSC, from which we can generate an ensemble of surface currents to derive both predictions and prediction uncertainties. Our approach incorporates prior knowledge of the SSC length scales and variances that appear in the covariance function of the GP, which are then estimated from the SST data. The framework naturally handles noisy and incomplete SST data (e.g., due to cloud cover), without the need for pre-filtering.

  3. Results
    We validate the model through an observing system simulation experiment (OSSE), demonstrating that GP-based inversion outperforms existing methods, especially at low signal-to-noise ratios. When applied to Himawari-9 satellite SST data over the Australian North-West Shelf, our method resolves SSC down to the sub-mesoscale. We anticipate our framework being used to improve understanding of fine-scale ocean dynamics, and to facilitate the coherent propagation of uncertainty into downstream applications such as ocean particle tracking.

  4. Audience
    This talk is for oceanographers, remote sensing scientists, and spatial statisticians interested in machine learning methods to predict surface currents from tracer fields.

  5. Keywords
    sea surface currents, sea surface temperature, spatio-temporal, heat transport, remotely sensed temperature, Gaussian processes, statistical inversion.