Datasets for "Scalable interpolation of satellite altimetry data with probabilistic machine learning" ...

Elevation (radar freeboard and sea-level anomaly) fields from CryoSat-2, Sentinel-3A, and Sentinel-3B, over the period December 1st 2018 - April 30th 2019. These data were processed for the Arctic domain using the European Space Agency's Grid Processing on Demand (GPOD) service. Processing foll...

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Bibliographic Details
Main Authors: Gregory, William, MacEachern, Ronald, Takao, So, Lawrence, Isobel R., Nab, Carmen, Deisenroth, Marc Peter, Tsamados, Michel
Format: Dataset
Language:unknown
Published: Zenodo 2024
Subjects:
Arctic
Online Access:https://dx.doi.org/10.5281/zenodo.13218449
https://zenodo.org/doi/10.5281/zenodo.13218449
Description
Summary:Elevation (radar freeboard and sea-level anomaly) fields from CryoSat-2, Sentinel-3A, and Sentinel-3B, over the period December 1st 2018 - April 30th 2019. These data were processed for the Arctic domain using the European Space Agency's Grid Processing on Demand (GPOD) service. Processing follows the steps outlined in Lawrence et al., 2021 (https://doi.org/10.1016/j.asr.2019.10.011). These data are provided at along-track, 5 km and 50 km resolution, where gridded data follow the EASE grid definition (https://doi.org/10.3390/ijgi1010032). These data were used to develop the open-source Python programming library GPSat (https://github.com/CPOMUCL/GPSat), which uses local Gaussian Process models to perform scalable interpolation of non-stationary satellite altimetry data. The 'Source_data.xlsx' file contains the data corresponding to figures in the published Nature Communications article 'Scalable interpolation of satellite altimetry data with probabilistic machine learning'. ...

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