Deep Learning Models Ratify ENSO's Substantial Impact on Antarctic Sea Ice Subseasonal Predictability: supplemental data ...
cor_spatial38c.hdf contains SIPNet skill information, comprising four subsets: 'mid', 'nino', 'nina', and 'all', representing model skill under neutral conditions, El Niño, La Niña, and the entire time range, respectively.acc_per_spatial38.hdf is similar to co...
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ftdatacite:10.6084/m9.figshare.24572866.v2 2024-03-31T07:49:21+00:00 Deep Learning Models Ratify ENSO's Substantial Impact on Antarctic Sea Ice Subseasonal Predictability: supplemental data ... Wang, Yunhe 2024 https://dx.doi.org/10.6084/m9.figshare.24572866.v2 https://figshare.com/articles/dataset/AI_Model_Affirms_ENSO_s_Boost_to_Subseasonal_Predictability_of_Antarctic_Sea_Ice_supplemental_data/24572866/2 unknown figshare https://dx.doi.org/10.6084/m9.figshare.24572866 Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 Physical oceanography dataset Dataset 2024 ftdatacite https://doi.org/10.6084/m9.figshare.24572866.v210.6084/m9.figshare.24572866 2024-03-04T13:36:33Z cor_spatial38c.hdf contains SIPNet skill information, comprising four subsets: 'mid', 'nino', 'nina', and 'all', representing model skill under neutral conditions, El Niño, La Niña, and the entire time range, respectively.acc_per_spatial38.hdf is similar to cor_spatial38c but represents the model skill for anomaly persistence.cor_spatial38_linear.hdf, like cor_spatial38c, represents the model skill but specifically for the linear SIPNet model.sic_stddev.hdf contains sea ice variability information with three subsets: 'nino', 'mid', and 'nina', denoting sea ice variability under El Niño, neutral conditions, and La Niña, respectively.t2m_composite38.hdf: Surface air temperature composite dataset, containing composites for four seasons.sst_composite38.hdf: Similar to t2m_composite38, but for sea surface temperature.mslp_composite38.hdf: Similar to t2m_composite38, but for sea level pressure.obser_sic_composite.hdf: Similar to t2m_composite38, but for observed sea ice concentration.predi_sic_composite.hdf: ... Dataset Antarc* Antarctic Sea ice DataCite Metadata Store (German National Library of Science and Technology) Antarctic |
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Open Polar |
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DataCite Metadata Store (German National Library of Science and Technology) |
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ftdatacite |
language |
unknown |
topic |
Physical oceanography |
spellingShingle |
Physical oceanography Wang, Yunhe Deep Learning Models Ratify ENSO's Substantial Impact on Antarctic Sea Ice Subseasonal Predictability: supplemental data ... |
topic_facet |
Physical oceanography |
description |
cor_spatial38c.hdf contains SIPNet skill information, comprising four subsets: 'mid', 'nino', 'nina', and 'all', representing model skill under neutral conditions, El Niño, La Niña, and the entire time range, respectively.acc_per_spatial38.hdf is similar to cor_spatial38c but represents the model skill for anomaly persistence.cor_spatial38_linear.hdf, like cor_spatial38c, represents the model skill but specifically for the linear SIPNet model.sic_stddev.hdf contains sea ice variability information with three subsets: 'nino', 'mid', and 'nina', denoting sea ice variability under El Niño, neutral conditions, and La Niña, respectively.t2m_composite38.hdf: Surface air temperature composite dataset, containing composites for four seasons.sst_composite38.hdf: Similar to t2m_composite38, but for sea surface temperature.mslp_composite38.hdf: Similar to t2m_composite38, but for sea level pressure.obser_sic_composite.hdf: Similar to t2m_composite38, but for observed sea ice concentration.predi_sic_composite.hdf: ... |
format |
Dataset |
author |
Wang, Yunhe |
author_facet |
Wang, Yunhe |
author_sort |
Wang, Yunhe |
title |
Deep Learning Models Ratify ENSO's Substantial Impact on Antarctic Sea Ice Subseasonal Predictability: supplemental data ... |
title_short |
Deep Learning Models Ratify ENSO's Substantial Impact on Antarctic Sea Ice Subseasonal Predictability: supplemental data ... |
title_full |
Deep Learning Models Ratify ENSO's Substantial Impact on Antarctic Sea Ice Subseasonal Predictability: supplemental data ... |
title_fullStr |
Deep Learning Models Ratify ENSO's Substantial Impact on Antarctic Sea Ice Subseasonal Predictability: supplemental data ... |
title_full_unstemmed |
Deep Learning Models Ratify ENSO's Substantial Impact on Antarctic Sea Ice Subseasonal Predictability: supplemental data ... |
title_sort |
deep learning models ratify enso's substantial impact on antarctic sea ice subseasonal predictability: supplemental data ... |
publisher |
figshare |
publishDate |
2024 |
url |
https://dx.doi.org/10.6084/m9.figshare.24572866.v2 https://figshare.com/articles/dataset/AI_Model_Affirms_ENSO_s_Boost_to_Subseasonal_Predictability_of_Antarctic_Sea_Ice_supplemental_data/24572866/2 |
geographic |
Antarctic |
geographic_facet |
Antarctic |
genre |
Antarc* Antarctic Sea ice |
genre_facet |
Antarc* Antarctic Sea ice |
op_relation |
https://dx.doi.org/10.6084/m9.figshare.24572866 |
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/10.6084/m9.figshare.24572866.v210.6084/m9.figshare.24572866 |
_version_ |
1795038588991176704 |