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Title: Inferring Latent Velocities from Weather Radar Data using Gaussian Processes
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.  more » « less
Award ID(s):
1661259
PAR ID:
10092572
Author(s) / Creator(s):
;
Date Published:
Journal Name:
Advances in neural information processing systems
Volume:
31
ISSN:
1049-5258
Page Range / eLocation ID:
8984–8993
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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