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|---|
| _creationDate | Datetime | 2024-09-04 8:47:26 PM |
| _modificationDate | Datetime | 2024-11-12 5:52:41 PM |
| _pageID | Integer | 110,602 |
| _pageName | Page | Reference:Reference-025333 |
| _pageTitle | String | Reference-025333
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| Field | Field type | Allowed values | Value |
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| Firstname | String | | Ghazvinian, Mohammadvaghef; Delle Monache, Luca; Afzali Gorooh, Vesta; Steinhoff, Daniel; Sengupta, Agniv; Hu, Weiming; Simpson, Matthew; Weihs, Rachel; Papadopoulos, Caroline; Mulrooney, Patrick; Kawzenuk, Brian; Mascioli, Nora; Ralph, Fred Martin; |
| BibType | String | journalArticle · report · bookSection · webpage · conferencePaper · MISC · thesis · blogpost · preprint | journalArticle |
| Title | String | | Deep Learning of a 200-Member Ensemble with a Limited Historical Training to Improve the Prediction of Extreme Precipitation Events |
| Editors | String | | |
| Year | Date | | July 2024 |
| Journal | String | | Monthly Weather Review |
| Booktitle | String | | |
| Volume | String | | 152 |
| Pages | String | | 1587–1605 |
| URL | String | | https://journals.ametsoc.org/view/journals/mwre/152/7/MWR-D-23-0277.1.xml |
| DOI | String | | 10.1175/MWR-D-23-0277.1 |
| ISBN | String | | |
| Note | String | | Auto downloaded ref at: 2024-09-04 |
1 row is stored for this page
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| PublicationMultipleModelsCargo | String | WRF |
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| CSDMSGroups | String | Terrestrial Working Group · Coastal Working Group · Marine Working Group · Education and Knowledge Transfer (EKT) Working Group · Cyberinformatics and Numerics Working Group · Hydrology Focus Research Group · Chesapeake Focus Research Group · Critical Zone Focus Research Group · Human Dimensions Focus Research Group · Geodynamics Focus Research Group · Ecosystem Dynamics Focus Research Group · Coastal Vulnerability Initiative · Continental Margin Initiative · Artificial Intelligence & Machine Learning Initiative · Modeling Platform Interoperability Initiative · River Network Modeling Initiative | |
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| PublicationClusterID | String | | 0 |
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| Semantic_ID | String | | 3a5b8fdc9833fbbdef0aa066f043024569d80742 |
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