Turnitin
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Heavy-tailed diffusion models
Diffusion models achieve state-of-the-art generation quality across many applications, but
their ability to capture rare or extreme events in heavy-tailed distributions remains unclear. In …
their ability to capture rare or extreme events in heavy-tailed distributions remains unclear. In …
A generative diffusion model for probabilistic ensembles of precipitation maps conditioned on multisensor satellite observations
A generative diffusion model is used to produce probabilistic ensembles of precipitation
intensity maps at the 1-hour 5-km resolution. The generation is conditioned on infrared and …
intensity maps at the 1-hour 5-km resolution. The generation is conditioned on infrared and …
Kolmogorov Arnold Neural Interpolator for Downscaling and Correcting Meteorological Fields from In-Situ Observations
Obtaining accurate weather forecasts at station locations is a critical challenge due to
systematic biases arising from the mismatch between multi-scale, continuous atmospheric …
systematic biases arising from the mismatch between multi-scale, continuous atmospheric …