Record Details
Reconstructing Velocities of Migrating Birds from Weather Radar – A Case Study in Computational Sustainability
ScholarsArchive at Oregon State University
| Field | Value |
|---|---|
| Title | Reconstructing Velocities of Migrating Birds from Weather Radar – A Case Study in Computational Sustainability |
| Names |
Farnsworth, Andrew
(creator) Sheldon, Daniel (creator) Geevarghese, Jeffrey (creator) Irvine, Jed (creator) Van Doren, Benjamin (creator) Webb, Kevin (creator) Dietterich, Thomas G. (creator) Kelling, Steve (creator) |
| Date Issued | 2014 (iso8601) |
| Note | This is an author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by the Association for the Advancement of Artificial Intelligence (AAAI) and can be found at: http://www.aaai.org/Magazine/magazine.php. |
| Abstract | Bird migration occurs at the largest of global scales, but monitoring such movements can be challenging. In the US there is an operational network of weather radars providing freely accessible data for monitoring meteorological phenomena in the atmosphere. Individual radars are sensitive enough to detect birds, and can provide insight into migratory behaviors of birds at scales that are not possible using other sensors. Archived data from the WSR-88D network of US weather radars hold valuable and detailed information about the continent-scale migratory movements of birds over the last 20 years. However, significant technical challenges must be overcome to understand this information and harness its potential for science and conservation. We describe recent work on an AI system to quantify bird migration using radar data, which is part of the larger BirdCast project to model and forecast bird migration at large scales using radar, weather, and citizen science data. |
| Genre | Article |
| Identifier | Farnsworth, A., Sheldon, D., Geevarghese, J., Irvine, J., Van Doren, B., Webb, K., . . . Kelling, S. (2014). Reconstructing velocities of migrating birds from weather radar - A case study in computational sustainability. AI Magazine, 35(2), 31-48. doi:10.1609/aimag.v35i2.2527 |
