Using Machine Learning to Estimate Nonorographic Gravity Wave Characteristics at Source LevelsSource: Journal of the Atmospheric Sciences:;2023:;volume( 080 ):;issue: 002::page 419DOI: 10.1175/JAS-D-22-0021.1Publisher: American Meteorological Society
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| contributor author | Amiramjadi, Mozhgan | |
| contributor author | Plougonven, Riwal | |
| contributor author | Mohebalhojeh, Ali R. | |
| contributor author | Mirzaei, Mohammad | |
| date accessioned | 2024-12-24T14:21:02Z | |
| date available | 2024-12-24T14:21:02Z | |
| date copyright | 01 Feb. 2023 | |
| date issued | 2023 | |
| identifier other | atsc-JAS-D-22-0021.1.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4300592 | |
| language | English | |
| publisher | American Meteorological Society | |
| title | Using Machine Learning to Estimate Nonorographic Gravity Wave Characteristics at Source Levels | |
| type | Journal Paper | |
| journal volume | 80 | |
| journal issue | 2 | |
| journal title | Journal of the Atmospheric Sciences | |
| identifier doi | 10.1175/JAS-D-22-0021.1 | |
| journal fristpage | 419 | |
| journal lastpage | 440 | |
| tree | Journal of the Atmospheric Sciences:;2023:;volume( 080 ):;issue: 002 | |
| contenttype | Fulltext |