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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KUP-BI distills continuation-style knowledge from a train-only historical library to supply an approximate post-target proxy that is fused into forecasting backbones for improved performance on public datasets.
citing papers explorer
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SwAIther-Precip: Lead-Time-Aware Bias Correction Enables Kilometer-Scale Downscaling of Global AI Precipitation Forecasts over Switzerland
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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Beyond Extrapolation: Knowledge Utilization Paradigm with Bidirectional Inspiration for Time Series Forecasting
KUP-BI distills continuation-style knowledge from a train-only historical library to supply an approximate post-target proxy that is fused into forecasting backbones for improved performance on public datasets.