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. |
Year | Venue | Keywords |
---|---|---|
2018 | ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 31 (NIPS 2018) | small number,gaussian processes,gaussian process,bird migration,weather radars,spatial resolution |
Field | DocType | Volume |
Kernel (linear algebra),Weather radar,Laplace transform,Inference,Computer science,Algorithm,Aliasing,Fast Fourier transform,Gaussian process,Artificial intelligence,Image resolution,Machine learning | Conference | 31 |
ISSN | Citations | PageRank |
1049-5258 | 0 | 0.34 |
References | Authors | |
0 | 2 |
Name | Order | Citations | PageRank |
---|---|---|---|
Rico Angell | 1 | 10 | 1.79 |
Sheldon, Daniel R. | 2 | 180 | 23.15 |