SwAIther-Precip uses lead-time-conditioned U-Net bias correction followed by diffusion-based generative downscaling to reduce CRPS by 48% and achieve ~4 km effective resolution from 0.25° AIFS forecasts.
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5 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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Q-SRDRN multi-quantile network with pinball loss and per-quantile heads detects extreme precipitation events up to 18 times more effectively than deterministic baselines while preserving augmentation benefits for the median.
A physics-constrained consistency model downscales Greenland SMB and surface temperature by a factor of 32 while preserving coarse-scale sums and outperforming interpolation on test metrics.
A conditional diffusion model downscales global atmospheric forecasts from 100 km to 30 km resolution while improving probabilistic skill, matching power spectra, and preserving physical relationships.
A multi-task Patch-cGAN with lightning-derived spatial loss weighting improves post-processed forecasts of intense precipitation and lightning occurrence over the Korean Peninsula in summer 2025.
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Physics-constrained generative machine learning-based high-resolution downscaling of Greenland's surface mass balance and surface temperature
A physics-constrained consistency model downscales Greenland SMB and surface temperature by a factor of 32 while preserving coarse-scale sums and outperforming interpolation on test metrics.