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• W2891108049 abstract "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." @default.
• W2891108049 created "2018-09-27" @default.
• W2891108049 creator A5047594082 @default.
• W2891108049 creator A5061155671 @default.
• W2891108049 date "2018-01-01" @default.
• W2891108049 modified "2023-10-18" @default.
• W2891108049 title "Inferring Latent Velocities from Weather Radar Data using Gaussian Processes" @default.
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• W2891108049 hasPublicationYear "2018" @default.
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