| dc.contributor.author | John L. Cintineo | |
| dc.contributor.author | Michael J. Pavolonis | |
| dc.contributor.author | Justin M. Sieglaff | |
| dc.date.accessioned | 2023-04-12T18:40:51Z | |
| dc.date.available | 2023-04-12T18:40:51Z | |
| dc.date.copyright | 2022/07/01 | |
| dc.date.issued | 2022 | |
| dc.identifier.other | WAF-D-22-0019.1.pdf | |
| dc.identifier.uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4290061 | |
| dc.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 | |
| dc.publisher | American Meteorological Society | |
| dc.title | ProbSevere LightningCast: A Deep-Learning Model for Satellite-Based Lightning Nowcasting | |
| dc.type | Journal Paper | |
| dc.journal.volume | 37 | |
| dc.journal.issue | 7 | |
| dc.journal.title | Weather and Forecasting | |
| dc.identifier.doi | 10.1175/WAF-D-22-0019.1 | |
| dc.journal.fristpage | 1239 | |
| dc.journal.lastpage | 1257 | |
| dc.page | 1239–1257 | |
| dc.tree | Weather and Forecasting:;2022:;volume( 037 ):;issue: 007 | |
| dc.contenttype | Fulltext | |
