Scalable interpolation of satellite altimetry data with probabilistic machine learning

William Gregory (), Ronald MacEachern, So Takao, Isobel R. Lawrence, Carmen Nab, Marc Peter Deisenroth and Michel Tsamados
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William Gregory: Princeton University
Ronald MacEachern: University College London
So Takao: University College London
Isobel R. Lawrence: European Space Agency
Carmen Nab: University College London
Marc Peter Deisenroth: University College London
Michel Tsamados: University College London

Nature Communications, 2024, vol. 15, issue 1, 1-11

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-51900-x

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