Scalable interpolation of satellite altimetry data with probabilistic machine learning DOI Creative Commons
William K. Gregory,

Ronald MacEachern,

So Takao

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: April 8, 2024

Abstract In this work, we present a new open-source Python programming library for performing efficient interpolation of non-stationary satellite altimetry data, using scalable Gaussian Process (GP) techniques. We showcase the library, GPSat, by data from CryoSat-2, Sentinel-3A, and Sentinel-3B radar altimeters, to generate complete maps daily 50 km2-gridded Arctic sea ice freeboard. Relative previous GP scheme, find that GPSat offers 504× computational speedup, with less than 4 mm difference on derived freeboards, average. then demonstrate scalability through freeboard at 5 km2 grid resolution, Sea-Level Anomalies (SLA) resolution altimeter footprint. Validation novel high product shows strong agreement airborne linear correlation 0.66. Footprint-level SLA also improvements in predictive skill over regression, which is standard approach used processing. suggest could overcome bottlenecks faced many altimetry-based routines. This turn lead improved observational estimates ocean topography thickness, further critical understanding variability short spatio-temporal scales.

Language: Английский

Seasonal forecasting of Pan-Arctic sea ice with state space model DOI Creative Commons
W. Wang,

Weidong Yang,

Lei Wang

et al.

npj Climate and Atmospheric Science, Journal Year: 2025, Volume and Issue: 8(1)

Published: May 7, 2025

Language: Английский

Citations

0
Scalable interpolation of satellite altimetry data with probabilistic machine learning DOI Creative Commons
William K. Gregory,

Ronald MacEachern,

So Takao

et al.

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: Aug. 28, 2024

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

Language: Английский

Citations

2
Scalable interpolation of satellite altimetry data with probabilistic machine learning DOI Creative Commons
William K. Gregory,

Ronald MacEachern,

So Takao

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: April 8, 2024

Abstract In this work, we present a new open-source Python programming library for performing efficient interpolation of non-stationary satellite altimetry data, using scalable Gaussian Process (GP) techniques. We showcase the library, GPSat, by data from CryoSat-2, Sentinel-3A, and Sentinel-3B radar altimeters, to generate complete maps daily 50 km2-gridded Arctic sea ice freeboard. Relative previous GP scheme, find that GPSat offers 504× computational speedup, with less than 4 mm difference on derived freeboards, average. then demonstrate scalability through freeboard at 5 km2 grid resolution, Sea-Level Anomalies (SLA) resolution altimeter footprint. Validation novel high product shows strong agreement airborne linear correlation 0.66. Footprint-level SLA also improvements in predictive skill over regression, which is standard approach used processing. suggest could overcome bottlenecks faced many altimetry-based routines. This turn lead improved observational estimates ocean topography thickness, further critical understanding variability short spatio-temporal scales.

Language: Английский

Citations

1