Scalable interpolation of satellite altimetry data with probabilistic machine learning

• Published in: Nature Communications
• Main Authors: William Gregory, Ronald MacEachern, So Takao, Isobel R. Lawrence, Carmen Nab, Marc Peter Deisenroth, Michel Tsamados
• Format: Article in Journal/Newspaper
• Language: English
• Published: Nature Portfolio 2024
• Subjects: Science; Q; Arctic; Sea ice
• Online Access: https://doi.org/10.1038/s41467-024-51900-x; https://doaj.org/article/8d0f5c37cee44e48ada5993d34540361

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.