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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Zenodo
2024
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Online Access: | https://dx.doi.org/10.5281/zenodo.13218448 https://zenodo.org/doi/10.5281/zenodo.13218448 |
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ftdatacite:10.5281/zenodo.13218448 2024-09-30T14:30:58+00:00 Datasets for "Scalable interpolation of satellite altimetry data with probabilistic machine learning" ... Gregory, William MacEachern, Ronald Takao, So Lawrence, Isobel R. Nab, Carmen Deisenroth, Marc Peter Tsamados, Michel 2024 https://dx.doi.org/10.5281/zenodo.13218448 https://zenodo.org/doi/10.5281/zenodo.13218448 unknown Zenodo https://dx.doi.org/10.5281/zenodo.13218449 Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 Dataset dataset 2024 ftdatacite https://doi.org/10.5281/zenodo.1321844810.5281/zenodo.13218449 2024-09-02T07:59:01Z 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'. ... Dataset Arctic DataCite Arctic |
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unknown |
description |
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'. ... |
format |
Dataset |
author |
Gregory, William MacEachern, Ronald Takao, So Lawrence, Isobel R. Nab, Carmen Deisenroth, Marc Peter Tsamados, Michel |
spellingShingle |
Gregory, William MacEachern, Ronald Takao, So Lawrence, Isobel R. Nab, Carmen Deisenroth, Marc Peter Tsamados, Michel Datasets for "Scalable interpolation of satellite altimetry data with probabilistic machine learning" ... |
author_facet |
Gregory, William MacEachern, Ronald Takao, So Lawrence, Isobel R. Nab, Carmen Deisenroth, Marc Peter Tsamados, Michel |
author_sort |
Gregory, William |
title |
Datasets for "Scalable interpolation of satellite altimetry data with probabilistic machine learning" ... |
title_short |
Datasets for "Scalable interpolation of satellite altimetry data with probabilistic machine learning" ... |
title_full |
Datasets for "Scalable interpolation of satellite altimetry data with probabilistic machine learning" ... |
title_fullStr |
Datasets for "Scalable interpolation of satellite altimetry data with probabilistic machine learning" ... |
title_full_unstemmed |
Datasets for "Scalable interpolation of satellite altimetry data with probabilistic machine learning" ... |
title_sort |
datasets for "scalable interpolation of satellite altimetry data with probabilistic machine learning" ... |
publisher |
Zenodo |
publishDate |
2024 |
url |
https://dx.doi.org/10.5281/zenodo.13218448 https://zenodo.org/doi/10.5281/zenodo.13218448 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_relation |
https://dx.doi.org/10.5281/zenodo.13218449 |
op_rights |
Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 |
op_doi |
https://doi.org/10.5281/zenodo.1321844810.5281/zenodo.13218449 |
_version_ |
1811635680748503040 |