AirCast-SR is a latent consistency diffusion model that super-resolves GraphCast forecasts to 1 km hourly resolution over eight surface variables with near-zero bias and preserved fine-scale spectral power.
Residual corrective diffusion mod- eling for km-scale atmospheric downscaling,
3 Pith papers cite this work. Polarity classification is still indexing.
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Flow matching produces better spatial structure than diffusion models for convective precipitation downscaling but underestimates heavy rainfall amounts.
citing papers explorer
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AirCast-SR: A Foundation Model for Kilometer-Scale Atmospheric Super-Resolution via Latent Consistency Diffusion
AirCast-SR is a latent consistency diffusion model that super-resolves GraphCast forecasts to 1 km hourly resolution over eight surface variables with near-zero bias and preserved fine-scale spectral power.
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Flow Matching for Convective-Scale Precipitation Downscaling
Flow matching produces better spatial structure than diffusion models for convective precipitation downscaling but underestimates heavy rainfall amounts.
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