Extracting 3D Radar Features to Improve Quantitative Precipitation Estimation in Complex Terrain Based on Deep Learning Neural NetworksSource: Weather and Forecasting:;2023:;volume( 038 ):;issue: 002::page 273DOI: 10.1175/WAF-D-22-0034.1Publisher: American Meteorological Society
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| contributor author | Cheng, Yung-Yun | |
| contributor author | Chang, Chia-Tung | |
| contributor author | Chen, Buo-Fu | |
| contributor author | Kuo, Hung-Chi | |
| contributor author | Lee, Cheng-Shang | |
| date accessioned | 2024-12-24T13:49:27Z | |
| date available | 2024-12-24T13:49:27Z | |
| date copyright | 01 Feb. 2023 | |
| date issued | 2023 | |
| identifier other | wefo-WAF-D-22-0034.1.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4299673 | |
| language | English | |
| publisher | American Meteorological Society | |
| title | Extracting 3D Radar Features to Improve Quantitative Precipitation Estimation in Complex Terrain Based on Deep Learning Neural Networks | |
| type | Journal Paper | |
| journal volume | 38 | |
| journal issue | 2 | |
| journal title | Weather and Forecasting | |
| identifier doi | 10.1175/WAF-D-22-0034.1 | |
| journal fristpage | 273 | |
| journal lastpage | 289 | |
| tree | Weather and Forecasting:;2023:;volume( 038 ):;issue: 002 | |
| contenttype | Fulltext |