AirCast-SR: A Foundation Model for Kilometer-Scale Atmospheric Super-Resolution via Latent Consistency Diffusion
AirCast-SR is a foundation model that downscales global AI weather forecasts from 0.25-degree (~28 km) resolution to 1 km horizontal resolution at hourly intervals. It uses a three-dimensional U-Net within a Latent Consistency Model diffusion framework, trained on data over the contiguous United States. The model achieves near-zero bias and preserves fine-scale atmospheric structures, validated across multiple seasons and demonstrated zero-shot transferability to India and Germany without retraining.
Article intelligence
Key points
- AirCast-SR downscales global AI weather forecasts from ~28 km to 1 km resolution at hourly steps.
- It employs a Latent Consistency Model diffusion with a 3D U-Net architecture.
- The model shows near-zero bias and preserves fine-scale structures in the 10–100 km range.
- Zero-shot global transferability is demonstrated over India and Germany without fine-tuning.
Why it matters
This matters because airCast-SR downscales global AI weather forecasts from ~28 km to 1 km resolution at hourly steps.
Technical impact
May affect model selection, inference cost, product capability, and evaluation benchmarks.
[2605.26130] AirCast-SR: A Foundation Model for Kilometer-Scale Atmospheric Super-Resolution via Latent Consistency Diffusion
[Submitted on 20 May 2026]
Title:AirCast-SR: A Foundation Model for Kilometer-Scale Atmospheric Super-Resolution via Latent Consistency Diffusion
View a PDF of the paper titled AirCast-SR: A Foundation Model for Kilometer-Scale Atmospheric Super-Resolution via Latent Consistency Diffusion, by Somnath Luitel and 12 other authors
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Abstract:Operational weather prediction at kilometer scales remains computationally prohibitive for traditional numerical weather prediction (NWP) models, limiting forecast access for applications in energy, agriculture, and disaster management that require fine-grained spatiotemporal detail. Here we introduce AirCast-SR, a foundation model for atmospheric super-resolution that downscales global AI weather forecasts from 0.25 degree (~28 km) to 1 km horizontal resolution at hourly temporal resolution, producing 67-hour forecasts of eight coupled surface variables simultaneously. EarthMind-SR employs a three-dimensional U-Net conditioned within a Latent Consistency Model (LCM) diffusion framework, trained on patch-based samples over the contiguous United States (CONUS) using GraphCast forecasts as input and NOAA's Analysis of Record for Calibration (AORC) as the target. The model achieves near-zero bias across all variables and lead times, and its radial power spectral density analysis demonstrates preservation of fine-scale atmospheric structure at wavelengths of 10 km to 100 km where coarser models lose spectral power. We validate EarthMind-SR across three CONUS case studies spanning winter, summer, and spring seasons, and demonstrate zero-shot global transferability over India and Germany using independent surface station observations without any retraining or fine-tuning. As an open-weights foundation model, EarthMind-SR establishes a new paradigm for kilometer-scale AI weather prediction and provides a platform for regional fine-tuning, distillation, and downstream applications in climate services and hazard forecasting.
Comments: Somnath Luitel and Manmeet Singh are equal-contribution co-first authors, with Manmeet Singh (this http [email protected]) as corresponding author
Subjects:
Machine Learning (cs.LG); Atmospheric and Oceanic Physics (physics.ao-ph)
Cite as: arXiv:2605.26130 [cs.LG]
(or arXiv:2605.26130v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2605.26130
arXiv-issued DOI via DataCite
Submission history
From: Manmeet Singh [view email] [v1] Wed, 20 May 2026 07:29:00 UTC (35,453 KB)
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