Scalable interpolation of satellite altimetry data with probabilistic machine learning.

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, rela...

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Bibliographic Details
Published in:Nature Communications
Main Authors: Gregory, William, MacEachern, Ronald, Takao, So, Lawrence, Isobel R, Nab, Carmen, Deisenroth, Marc Peter, Tsamados, Michel
Format: Article in Journal/Newspaper
Language:English
Published: Nature Publishing Group 2024
Subjects:
Arctic
Sea ice
Online Access:https://doi.org/10.1038/s41467-024-51900-x
https://pubmed.ncbi.nlm.nih.gov/39198427
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11358133/
Description
Summary: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.

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