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Published on: October 27, 2016
Scalable interpolation of satellite altimetry data with probabilistic machine learning
William Gregory1, Ronald MacEachern2,3, So Takao2
1Atmospheric and Oceanic Sciences Program, Princeton University, Princeton, NJ, USA. wg4031@princeton.edu.
GPSat, a new Python library, speeds up satellite altimetry data interpolation 504x for Arctic sea ice freeboard mapping. It improves understanding of ocean and sea ice changes at fine scales.
Area of Science:
- Geosciences
- Oceanography
- Cryosphere Science
Background:
- Satellite altimetry is crucial for monitoring sea ice and sea level.
- Existing interpolation methods face computational challenges for high-resolution data.
- Non-stationary data requires advanced interpolation techniques.
Purpose of the Study:
- Introduce GPSat, an open-source Python library for efficient satellite altimetry data interpolation.
- Generate high-resolution maps of Arctic sea ice radar freeboard and Sea-Level Anomalies (SLA).
- Address computational bottlenecks in altimetry data processing.
Main Methods:
- Utilize scalable Gaussian process techniques for interpolation.
- Develop and apply the GPSat library for processing non-stationary satellite altimetry data.
- Generate daily 50 km-gridded Arctic sea ice radar freeboard maps.
Main Results:
- GPSat achieved a 504x computational speedup compared to previous methods for freeboard interpolation.
- Derived freeboards showed an average difference of less than 4 mm.
- Demonstrated scalability with 5 km freeboard interpolation and footprint-level SLA interpolation.
- Interpolated 5 km radar freeboards correlated strongly with airborne data (r=0.66).
- Footprint-level SLA interpolation showed improved predictive skill over linear regression.
Conclusions:
- GPSat offers a significant computational advantage for satellite altimetry data interpolation.
- The library enables more efficient generation of high-resolution sea ice and sea level products.
- GPSat can advance the understanding of short-term ocean and sea ice variability.
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