Physics-Constrained Deep Learning Postprocessing of Temperature and HumiditySource: Artificial Intelligence for the Earth Systems:;2023:;volume( 002 ):;issue: 004DOI: 10.1175/AIES-D-22-0089.1Publisher: American Meteorological Society
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| contributor author | Zanetta, Francesco | |
| contributor author | Nerini, Daniele | |
| contributor author | Beucler, Tom | |
| contributor author | Liniger, Mark A. | |
| date accessioned | 2024-12-24T15:12:18Z | |
| date available | 2024-12-24T15:12:18Z | |
| date copyright | 01 Oct. 2023 | |
| date issued | 2023 | |
| identifier other | aies-AIES-D-22-0089.1.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4301982 | |
| language | English | |
| publisher | American Meteorological Society | |
| title | Physics-Constrained Deep Learning Postprocessing of Temperature and Humidity | |
| type | Journal Paper | |
| journal volume | 2 | |
| journal issue | 4 | |
| journal title | Artificial Intelligence for the Earth Systems | |
| identifier doi | 10.1175/AIES-D-22-0089.1 | |
| journal lastpage | e220089 | |
| tree | Artificial Intelligence for the Earth Systems:;2023:;volume( 002 ):;issue: 004 | |
| contenttype | Fulltext |