PACT introduces a peak-aware cross-attention graph transformer that emulates station-level storm surges more accurately than prior graph neural network baselines while running in seconds after training.
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2026 2verdicts
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SwAIther-Precip uses lead-time-conditioned U-Net bias correction followed by diffusion-based super-resolution to downscale AIFS forecasts, achieving 48% CRPS reduction and ~4 km effective resolution up to 5 days lead time.
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PACT: Peak-Aware Cross-Attention Graph Transformers for Efficient Storm-Surge Emulation
PACT introduces a peak-aware cross-attention graph transformer that emulates station-level storm surges more accurately than prior graph neural network baselines while running in seconds after training.
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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 super-resolution to downscale AIFS forecasts, achieving 48% CRPS reduction and ~4 km effective resolution up to 5 days lead time.