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Scalable interpolation of satellite altimetry data with probabilistic machine learning

Author

Listed:
  • William Gregory

    (Princeton University)

  • Ronald MacEachern

    (University College London
    University College London)

  • So Takao

    (University College London)

  • Isobel R. Lawrence

    (European Space Agency)

  • Carmen Nab

    (University College London
    Met Office)

  • Marc Peter Deisenroth

    (University College London
    The Alan Turing Institute)

  • Michel Tsamados

    (University College London)

Abstract

We present GPSat; an open-source Python programming library for performing efficient interpolation of non-stationary satellite altimetry data, using scalable Gaussian process techniques. We use GPSat to generate complete maps of daily 50 km-gridded Arctic sea ice radar freeboard, and find that, relative to a previous interpolation scheme, GPSat offers a 504 × computational speedup, with less than 4 mm difference on the derived freeboards on average. We then demonstrate the scalability of GPSat through freeboard interpolation at 5 km resolution, and Sea-Level Anomalies (SLA) at the resolution of the altimeter footprint. Interpolated 5 km radar freeboards show strong agreement with airborne data (linear correlation of 0.66). Footprint-level SLA interpolation also shows improvements in predictive skill over linear regression. In this work, we suggest that GPSat could overcome the computational bottlenecks faced in many altimetry-based interpolation routines, and hence advance critical understanding of ocean and sea ice variability over short spatio-temporal scales.

Suggested Citation

  • William Gregory & Ronald MacEachern & So Takao & Isobel R. Lawrence & Carmen Nab & Marc Peter Deisenroth & Michel Tsamados, 2024. "Scalable interpolation of satellite altimetry data with probabilistic machine learning," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-51900-x
    DOI: 10.1038/s41467-024-51900-x
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    References listed on IDEAS

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    4. Noel Cressie & Gardar Johannesson, 2008. "Fixed rank kriging for very large spatial data sets," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(1), pages 209-226, February.
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