Can a Machine Learning–Enabled Numerical Model Help Extend Effective Forecast Range through Consistently Trained Subgrid-Scale Models?Source: Artificial Intelligence for the Earth Systems:;2023:;volume( 002 ):;issue: 001DOI: 10.1175/AIES-D-22-0050.1Publisher: American Meteorological Society
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| contributor author | Qu, Yongquan | |
| contributor author | Shi, Xiaoming | |
| date accessioned | 2023-08-15T10:38:54Z | |
| date available | 2023-08-15T10:38:54Z | |
| date copyright | 01 Jan. 2023 | |
| date issued | 2023 | |
| identifier other | AIES-D-22-0050.1.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4290727 | |
| language | English | |
| publisher | American Meteorological Society | |
| title | Can a Machine Learning–Enabled Numerical Model Help Extend Effective Forecast Range through Consistently Trained Subgrid-Scale Models? | |
| type | Journal Paper | |
| journal volume | 2 | |
| journal issue | 1 | |
| journal title | Artificial Intelligence for the Earth Systems | |
| identifier doi | 10.1175/AIES-D-22-0050.1 | |
| page | e220050 | |
| tree | Artificial Intelligence for the Earth Systems:;2023:;volume( 002 ):;issue: 001 | |
| contenttype | Fulltext |