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Scalable interpolation of satellite altimetry data with probabilistic machine learning.

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.

SUBMITTER: Gregory W

PROVIDER: S-EPMC11358133 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature

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Scalable interpolation of satellite altimetry data with probabilistic machine learning.

Gregory William W MacEachern Ronald R Takao So S Lawrence Isobel R IR Nab Carmen C Deisenroth Marc Peter MP Tsamados Michel M

Nature communications 20240828 1

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 t ...[more]

PMID: 39198427

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Project description:Coastal zones with dense populations, low elevations and/or inadequate adaptive capacity are on the frontline of unprecedented impacts from climate change. The Gulf of Guinea (GoG), stretching from Liberia to Gabon, is in particular vulnerable to coastal flooding caused by local and/or climate-induced sea level rise. In this region, interannual to decadal coastal sea level changes remain poorly understood, mainly due to a lack of tide gauge stations. Here we use nearly three decades (1993-2021) of satellite altimetry data to study the link between the Equatorial Atlantic and coastal GoG sea level variability. The rate of mean sea level rise increased from 3.47 to 3.89 ± 0.10 mm/yr from the Equatorial oceanic domain to the GoG coastal area, with an acceleration of 0.094 ± 0.050 mm/yr2. This corresponds to a mean sea level rise of about 8.9 cm over the entire altimetry period, 1993-2021. We focus on the (extreme) warm/cold events that occur in both the GoG during Atlantic Niños, and along the Angola-Namibia coast during Benguela Niños. Both events are driven by remote forcing via equatorial Kelvin waves and local forcing by local winds, freshwater fluxes and currents intensifications. Analysis of altimetry-based sea level, sea surface temperature anomalies, 20 °C isotherm based PIRATA moorings, and the Argo-based steric and thermometric sea level allows us to follow the coastal trapped waves (CTWs) along the GoG, and its link with major events observed along the strong Equatorial Atlantic warmings in 2010, 2012, 2019 and 2021. Both 2019 and 2021 warming have been identified as the warmest event ever reported in this region during the last 40 years. A lag of 1 month is observed between equatorial and West African coastal trapped wave propagation. This observation may help to better anticipate and manage the effects of extreme events on local ecosystems, fisheries, and socio-economic activities along the affected coastlines. In order to enable informed decision-making and guarantee the resilience of coastal communities in the face of climate change, it emphasises the significance of ongoing study in this field.

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Scalable interpolation of satellite altimetry data with probabilistic machine learning.

Publications

Scalable interpolation of satellite altimetry data with probabilistic machine learning.

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