Reference:Reference-026517: Difference between revisions
From CSDMS
Created page with "{{CSDMS reference template cargo |DOIentry=10.5194/gmd-16-199-2023 }} {{RefsInt1 |FeatureRef=No |PublicationClusterID=0 |MS_PublicationClusterID=0 |Semantic_ID=699f93fbb6018be7a16336c08ff66579af2bdcc1 |PublicationWhatKindOf=a module application description |PublicationHPCCYesno=No |PublicationNrofModels=a single module }} {{RefsInt2 |PublicationMultipleModelsCargo=WRF }} {{RefsInt3}}" |
m Text replacement - "None" to "" |
||
(4 intermediate revisions by 2 users not shown) | |||
Line 1: | Line 1: | ||
{{ | {{BibEntryCargo | ||
| | |Firstname=Zhong, Xiaohui; Ma, Zhijian; Yao, Yichen; Xu, Lifei; Wu, Yuan; Wang, Zhibin; | ||
|BibType=journalArticle | |||
|Title=WRF–ML v1.0: a bridge between WRF v4.3 and machine learning parameterizations and its application to atmospheric radiative transfer | |||
|Year=2023-01-06 | |||
|Journal=Geoscientific Model Development | |||
|Booktitle= | |||
|Volume=16 | |||
|Pages=199–209 | |||
|URL=https://gmd.copernicus.org/articles/16/199/2023/ | |||
|DOI=10.5194/gmd-16-199-2023 | |||
|Note=Auto downloaded ref at: 2024-09-05 | |||
}} | }} | ||
{{RefsInt1 | {{RefsInt1 | ||
Line 6: | Line 16: | ||
|PublicationClusterID=0 | |PublicationClusterID=0 | ||
|MS_PublicationClusterID=0 | |MS_PublicationClusterID=0 | ||
|Semantic_ID=699f93fbb6018be7a16336c08ff66579af2bdcc1 | |Semantic_ID=699f93fbb6018be7a16336c08ff66579af2bdcc1 |CorpusID=255736130 | ||
|PublicationWhatKindOf=a module application description | |PublicationWhatKindOf=a module application description | ||
|PublicationNrofModels=a single module | |||
|PublicationHPCCYesno=No | |PublicationHPCCYesno=No | ||
}} | }} | ||
{{RefsInt2 | {{RefsInt2 | ||
|PublicationMultipleModelsCargo=WRF | |PublicationMultipleModelsCargo=WRF | ||
}} | }} | ||
{{RefsInt3}} | {{RefsInt3 | ||
|PublicationAnimationsCargo= | |||
}} |
Latest revision as of 14:57, 12 November 2024
Author(s) | Zhong, Xiaohui; Ma, Zhijian; Yao, Yichen; Xu, Lifei; Wu, Yuan; Wang, Zhibin; |
BibType | journalArticle |
Title | WRF–ML v1.0: a bridge between WRF v4.3 and machine learning parameterizations and its application to atmospheric radiative transfer |
Editors | |
Year | 2023-01-06 |
Journal | Geoscientific Model Development |
Booktitle | |
Volume | 16 |
Pages | 199–209 |
URL | https://gmd.copernicus.org/articles/16/199/2023/ |
DOI | 10.5194/gmd-16-199-2023 |
ISBN | |
Note | Auto downloaded ref at: 2024-09-05
|
|
|
Feature reference | No |
PublicationClusterID | 0 |
MS_PublicationClusterID | 0 |
Semantic_ID | 699f93fbb6018be7a16336c08ff66579af2bdcc1 |
Nr of citations | 13 |
Sort of publication | a module application description |
Sort of model publication | a single module |
Is the CSDMS HPC used | No |
If HPC is used, for what project was it?: | |
Associated simulation movie if any: |
|
Model(s) discussed: | WRF
|