1-km Antarctic net snow accumulation predictions
Overview We provide static predictions of net snow accumulation over the Antarctic Ice Sheet derived using amachine learning approach.Here, we train random forest models to predict variability in net accumulation using atmospheric variables and topographic characteristics as predictors at 1 km resol...
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ftzenodo:oai:zenodo.org:7105855 2024-09-15T17:46:37+00:00 1-km Antarctic net snow accumulation predictions Medley, Brooke Lenaerts, Jan T. M. Dattler, Marissa Dattler, Eric Wever, Nander 2022-09-22 https://doi.org/10.5281/zenodo.7105855 unknown Zenodo https://doi.org/10.5281/zenodo.7105854 https://doi.org/10.5281/zenodo.7105855 oai:zenodo.org:7105855 info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode Antarctica Snow Accumulation ICESat-2 MERRA-2 info:eu-repo/semantics/other 2022 ftzenodo https://doi.org/10.5281/zenodo.710585510.5281/zenodo.7105854 2024-07-25T18:58:54Z Overview We provide static predictions of net snow accumulation over the Antarctic Ice Sheet derived using amachine learning approach.Here, we train random forest models to predict variability in net accumulation using atmospheric variables and topographic characteristics as predictors at 1 km resolution. Observations of net snow accumulation from both in situ and airborne radar data provide the input observable targets needed to train the random forest models. The data file includes all the predictors and predictands, an independent stake transect used for evaluation, four random forest gridded accumulation anomalies and their uncertainties, and a combined accumulation anomalies and its uncertainty. For a thorough description of how the predictions were generated generated see Medley et al. (2022). Other/Unknown Material Antarc* Antarctic Antarctica Ice Sheet Zenodo |
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Open Polar |
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unknown |
topic |
Antarctica Snow Accumulation ICESat-2 MERRA-2 |
spellingShingle |
Antarctica Snow Accumulation ICESat-2 MERRA-2 Medley, Brooke Lenaerts, Jan T. M. Dattler, Marissa Dattler, Eric Wever, Nander 1-km Antarctic net snow accumulation predictions |
topic_facet |
Antarctica Snow Accumulation ICESat-2 MERRA-2 |
description |
Overview We provide static predictions of net snow accumulation over the Antarctic Ice Sheet derived using amachine learning approach.Here, we train random forest models to predict variability in net accumulation using atmospheric variables and topographic characteristics as predictors at 1 km resolution. Observations of net snow accumulation from both in situ and airborne radar data provide the input observable targets needed to train the random forest models. The data file includes all the predictors and predictands, an independent stake transect used for evaluation, four random forest gridded accumulation anomalies and their uncertainties, and a combined accumulation anomalies and its uncertainty. For a thorough description of how the predictions were generated generated see Medley et al. (2022). |
format |
Other/Unknown Material |
author |
Medley, Brooke Lenaerts, Jan T. M. Dattler, Marissa Dattler, Eric Wever, Nander |
author_facet |
Medley, Brooke Lenaerts, Jan T. M. Dattler, Marissa Dattler, Eric Wever, Nander |
author_sort |
Medley, Brooke |
title |
1-km Antarctic net snow accumulation predictions |
title_short |
1-km Antarctic net snow accumulation predictions |
title_full |
1-km Antarctic net snow accumulation predictions |
title_fullStr |
1-km Antarctic net snow accumulation predictions |
title_full_unstemmed |
1-km Antarctic net snow accumulation predictions |
title_sort |
1-km antarctic net snow accumulation predictions |
publisher |
Zenodo |
publishDate |
2022 |
url |
https://doi.org/10.5281/zenodo.7105855 |
genre |
Antarc* Antarctic Antarctica Ice Sheet |
genre_facet |
Antarc* Antarctic Antarctica Ice Sheet |
op_relation |
https://doi.org/10.5281/zenodo.7105854 https://doi.org/10.5281/zenodo.7105855 oai:zenodo.org:7105855 |
op_rights |
info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode |
op_doi |
https://doi.org/10.5281/zenodo.710585510.5281/zenodo.7105854 |
_version_ |
1810494892212748288 |