| contributor author | John L. Cintineo | |
| contributor author | Michael J. Pavolonis | |
| contributor author | Justin M. Sieglaff | |
| date accessioned | 2023-04-12T18:40:51Z | |
| date available | 2023-04-12T18:40:51Z | |
| date copyright | 2022/07/01 | |
| date issued | 2022 | |
| identifier other | WAF-D-22-0019.1.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4290061 | |
| description abstract | Lightning strikes pose a hazard to human life and property, and can be difficult to forecast in a timely manner. In this study, a satellite-based machine learning model was developed to provide objective, short-term, location-specific probabilistic guidance for next-hour lightning activity. Using a convolutional neural network architecture designed for semantic segmentation, the model was trained using | |
| publisher | American Meteorological Society | |
| title | ProbSevere LightningCast: A Deep-Learning Model for Satellite-Based Lightning Nowcasting | |
| type | Journal Paper | |
| journal volume | 37 | |
| journal issue | 7 | |
| journal title | Weather and Forecasting | |
| identifier doi | 10.1175/WAF-D-22-0019.1 | |
| journal fristpage | 1239 | |
| journal lastpage | 1257 | |
| page | 1239–1257 | |
| tree | Weather and Forecasting:;2022:;volume( 037 ):;issue: 007 | |
| contenttype | Fulltext | |
