Vote for your favorite mineral in #MinCup25! - Kosmochlor vs. Azurite
It's a battle of green vs blue as rare but vibrant chromium-bearing kosmochlor up against the deep blue copper alteration mineral azurite.
Log InRegister
Quick Links : The Mindat ManualThe Rock H. Currier Digital LibraryMindat Newsletter [Free Download]
Home PageAbout MindatThe Mindat ManualHistory of MindatCopyright StatusWho We AreContact UsAdvertise on Mindat
Donate to MindatCorporate SponsorshipSponsor a PageSponsored PagesMindat AdvertisersAdvertise on Mindat
Learning CenterWhat is a mineral?The most common minerals on earthInformation for EducatorsMindat ArticlesThe ElementsThe Rock H. Currier Digital LibraryGeologic Time
Minerals by PropertiesMinerals by ChemistryAdvanced Locality SearchRandom MineralRandom LocalitySearch by minIDLocalities Near MeSearch ArticlesSearch GlossaryMore Search Options
Search For:
Mineral Name:
Locality Name:
Keyword(s):
 
The Mindat ManualAdd a New PhotoRate PhotosLocality Edit ReportCoordinate Completion ReportAdd Glossary Item
Mining CompaniesStatisticsUsersMineral MuseumsClubs & OrganizationsMineral Shows & EventsThe Mindat DirectoryDevice SettingsThe Mineral Quiz
Photo SearchPhoto GalleriesSearch by ColorNew Photos TodayNew Photos YesterdayMembers' Photo GalleriesPast Photo of the Day GalleryPhotography

Wang, Yunhe, Yuan, Xiaojun, Ren, Yibin, Bushuk, Mitchell, Shu, Qi, Li, Cuihua, Li, Xiaofeng (2023) Subseasonal Prediction of Regional Antarctic Sea Ice by a Deep Learning Model. Geophysical Research Letters, 50 (17) doi:10.1029/2023gl104347

Advanced
   -   Only viewable:
Reference TypeJournal (article/letter/editorial)
TitleSubseasonal Prediction of Regional Antarctic Sea Ice by a Deep Learning Model
JournalGeophysical Research Letters
AuthorsWang, YunheAuthor
Yuan, XiaojunAuthor
Ren, YibinAuthor
Bushuk, MitchellAuthor
Shu, QiAuthor
Li, CuihuaAuthor
Li, XiaofengAuthor
Year2023 (September 16)Volume50
Issue17
PublisherAmerican Geophysical Union (AGU)
DOIdoi:10.1029/2023gl104347Search in ResearchGate
Generate Citation Formats
Mindat Ref. ID16679085Long-form Identifiermindat:1:5:16679085:6
GUID0
Full ReferenceWang, Yunhe, Yuan, Xiaojun, Ren, Yibin, Bushuk, Mitchell, Shu, Qi, Li, Cuihua, Li, Xiaofeng (2023) Subseasonal Prediction of Regional Antarctic Sea Ice by a Deep Learning Model. Geophysical Research Letters, 50 (17) doi:10.1029/2023gl104347
Plain TextWang, Yunhe, Yuan, Xiaojun, Ren, Yibin, Bushuk, Mitchell, Shu, Qi, Li, Cuihua, Li, Xiaofeng (2023) Subseasonal Prediction of Regional Antarctic Sea Ice by a Deep Learning Model. Geophysical Research Letters, 50 (17) doi:10.1029/2023gl104347
In(2023, September) Geophysical Research Letters Vol. 50 (17) American Geophysical Union (AGU)

See Also

These are possibly similar items as determined by title/reference text matching only.

Wang, Yunhe, Yuan, Xiaojun, Bi, Haibo, Ren, Yibin, Liang, Yu, Li, Cuihua, Li, Xiaofeng (2023) Understanding Arctic Sea Ice Thickness Predictability by a Markov Model. Journal of Climate, 36 (15) 4879-4897 doi:10.1175/jcli-d-22-0525.1
Ren, Yibin; Li, Xiaofeng; Wang, Yunhe (2025) SICNetseason V1.0: a transformer-based deep learning model for seasonal Arctic sea ice prediction by incorporating sea ice thickness data. Geoscientific Model Development, 18 (9). doi:10.5194/gmd-18-2665-2025
Zeng, Jingwen, Yang, Qinghua, Li, Xuewei, Yuan, Xiaojun, Bushuk, Mitchell, Chen, Dake (2023) Reducing the Spring Barrier in Predicting Summer Arctic Sea Ice Concentration. Geophysical Research Letters, 50 (8) doi:10.1029/2022gl102115
Wang, Yunhe, Yuan, Xiaojun, Bi, Haibo, Bushuk, Mitchell, Liang, Yu, Li, Cuihua, Huang, Haijun (2022) Reassessing seasonal sea ice predictability of the Pacific-Arctic sector using a Markov model. The Cryosphere, 16 (3) 1141-1156 doi:10.5194/tc-16-1141-2022
Yuan, Xiaojun, Li, Cuihua (2008) Climate modes in southern high latitudes and their impacts on Antarctic sea ice. Journal of Geophysical Research, 113. doi:10.1029/2006jc004067
Ren, Yibin, Li, Xiaofeng (2023) Predicting the Daily Sea Ice Concentration on a Subseasonal Scale of the Pan-Arctic During the Melting Season by a Deep Learning Model. IEEE Transactions on Geoscience and Remote Sensing, 61. 1-15 doi:10.1109/tgrs.2023.3279089
Yuan, Xiaojun, Chen, Dake, Li, Cuihua, Wang, Lei, Wang, Wanqiu (2016) Arctic Sea Ice Seasonal Prediction by a Linear Markov Model. Journal of Climate, 29 (22). 8151-8173 doi:10.1175/jcli-d-15-0858.1
Li, Xia, Tan, Zhihong, Zheng, Youtong, Bushuk, Mitchell, Donner, Leo J. (2023) Open Water in Sea Ice Causes High Bias in Polar Low‐Level Clouds in GFDL CM4. Geophysical Research Letters, 50 (24) doi:10.1029/2023gl106322
Wang, Lei, Yuan, Xiaojun, Li, Cuihua (2019) Subseasonal forecast of Arctic sea ice concentration via statistical approaches. Climate Dynamics, 52 (7). 4953-4971 doi:10.1007/s00382-018-4426-6
Ren, Yibin, Li, Xiaofeng, Zhang, Wenhao (2022) A Data-Driven Deep Learning Model for Weekly Sea Ice Concentration Prediction of the Pan-Arctic During the Melting Season. IEEE Transactions on Geoscience and Remote Sensing, 60. 1-19 doi:10.1109/tgrs.2022.3177600
Wang, Yunhe, Yuan, Xiaojun, Cane, Mark A (2022) Coupled mode of cloud, atmospheric circulation, and sea ice controlled by wave-3 pattern in Antarctic winter. Environmental Research Letters, 17 (4) 44053pp. doi:10.1088/1748-9326/ac5272
 
and/or  
Mindat.org is an outreach project of the Hudson Institute of Mineralogy, a 501(c)(3) not-for-profit organization.
Copyright © mindat.org and the Hudson Institute of Mineralogy 1993-2025, except where stated. Most political location boundaries are © OpenStreetMap contributors. Mindat.org relies on the contributions of thousands of members and supporters. Founded in 2000 by Jolyon Ralph.
To cite: Ralph, J., Von Bargen, D., Martynov, P., Zhang, J., Que, X., Prabhu, A., Morrison, S. M., Li, W., Chen, W., & Ma, X. (2025). Mindat.org: The open access mineralogy database to accelerate data-intensive geoscience research. American Mineralogist, 110(6), 833–844. doi:10.2138/am-2024-9486.
Privacy Policy - Terms & Conditions - Contact Us / DMCA issues - Report a bug/vulnerability Current server date and time: September 9, 2025 04:41:45
Go to top of page