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Poster
Inferring Latent Velocities from Weather Radar Data using Gaussian Processes
Rico Angell · Daniel Sheldon

Tue Dec 04 02:00 PM -- 04:00 PM (PST) @ Room 517 AB #113
in Tue Poster Session B »

Archived data from the US network of weather radars hold detailed information about bird migration over the last 25 years, including very high-resolution partial measurements of velocity. Historically, most of this spatial resolution is discarded and velocities are summarized at a very small number of locations due to modeling and algorithmic limitations. This paper presents a Gaussian process (GP) model to reconstruct high-resolution full velocity fields across the entire US. The GP faithfully models all aspects of the problem in a single joint framework, including spatially random velocities, partial velocity measurements, station-specific geometries, measurement noise, and an ambiguity known as aliasing. We develop fast inference algorithms based on the FFT; to do so, we employ a creative use of Laplace's method to sidestep the fact that the kernel of the joint process is non-stationary.

Keywords: Kernel Methods · Gaussian Processes · Sustainability
[ Paper] 

Author Information

Rico Angell (University of Massachusetts)
Daniel Sheldon (University of Massachusetts Amherst)

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