Approximate Bayesian Inference for Reconstructing Velocities of Migrating Birds from Weather Radar

Authors

  • Daniel Sheldon University of Massachusetts Amherst
  • Andrew Farnsworth Cornell Lab of Ornithology
  • Jed Irvine Oregon State University
  • Benjamin Van Doren Cornell University
  • Kevin Webb Cornell Lab of Ornithology
  • Thomas Dietterich Oregon State University
  • Steve Kelling Cornell Lab of Ornithology

DOI:

https://doi.org/10.1609/aaai.v27i1.8486

Keywords:

Bird Migration, Bayesian Inference, BirdCast, WSR-88D, Doppler Radar, Velocity Profiling

Abstract

Archived data from the WSR-88D network of weather radars in the US hold 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 present an approximate Bayesian inference algorithm to reconstruct the velocity fields of birds migrating in the vicinity of a radar station. This is part of a larger project to quantify bird migration at large scales using weather radar data.

AAAI 2013 Proceedings Cover

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Published

2013-06-29

How to Cite

Sheldon, D., Farnsworth, A., Irvine, J., Van Doren, B., Webb, K., Dietterich, T., & Kelling, S. (2013). Approximate Bayesian Inference for Reconstructing Velocities of Migrating Birds from Weather Radar. Proceedings of the AAAI Conference on Artificial Intelligence, 27(1), 1334-1340. https://doi.org/10.1609/aaai.v27i1.8486

Issue

Vol. 27 No. 1 (2013): Twenty-Seventh AAAI Conference on Artificial Intelligence

Section

Computational Sustainability and Artificial Intelligence