TRIE benchmarks stochastic PDE surrogates on two chaotic SPDEs, finding generative models best match long-term statistics and uncertainty while latent versions cut inference time by 12x.
Diffcast: A unified framework via residual diffusion for precipitation nowcasting
3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3verdicts
UNVERDICTED 3representative citing papers
ReMatch corrects train-test residual distribution mismatch in probabilistic downscaling via optimal transport in low-dimensional PCA space, reducing under-dispersion and improving SSR and CRPS on HRRR-ERA5 wind data.
MeteoLogist improves nowcasting by processing radar data through physics-tailored encoders, temporal-phase alignment, and cross-field spatial aggregation to capture asynchronous and fragmented meteorological drivers, yielding +9.7% CSI40 gain and 37.67% gain in storm-developing stage on 3D-NEXRAD da
citing papers explorer
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TRIE: An Evaluation Framework for Stochastic PDE Surrogates
TRIE benchmarks stochastic PDE surrogates on two chaotic SPDEs, finding generative models best match long-term statistics and uncertainty while latent versions cut inference time by 12x.
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Mind the Residual Gap: Probabilistic Downscaling under Real-World Bias
ReMatch corrects train-test residual distribution mismatch in probabilistic downscaling via optimal transport in low-dimensional PCA space, reducing under-dispersion and improving SSR and CRPS on HRRR-ERA5 wind data.
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Seeing Inside the Storm: Improving Nowcasting by Integrating Meteorological Drivers
MeteoLogist improves nowcasting by processing radar data through physics-tailored encoders, temporal-phase alignment, and cross-field spatial aggregation to capture asynchronous and fragmented meteorological drivers, yielding +9.7% CSI40 gain and 37.67% gain in storm-developing stage on 3D-NEXRAD da