A neural network learns non-stationary anisotropic correlations from gridded CTM outputs and transfers the structure via LatticeKrig basis functions to station data for refined fine-scale NO2 predictions with uncertainty.
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STARQ uses a SegFormer-based multi-scale transformer with Gaussian-kernel pseudo-label propagation from sparse OpenAQ stations to downscale CAMS PM2.5 forecasts from 0.4° to 0.01° (~1 km) across Europe, achieving MAE 5.87 and R² 0.24 on held-out stations.
citing papers explorer
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A Non-stationary, Amortized, Transfer Learning Approach for Modeling Italian Air Quality
A neural network learns non-stationary anisotropic correlations from gridded CTM outputs and transfers the structure via LatticeKrig basis functions to station data for refined fine-scale NO2 predictions with uncertainty.
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Air Quality Downscaling with Station-Guided Pseudo-Supervision
STARQ uses a SegFormer-based multi-scale transformer with Gaussian-kernel pseudo-label propagation from sparse OpenAQ stations to downscale CAMS PM2.5 forecasts from 0.4° to 0.01° (~1 km) across Europe, achieving MAE 5.87 and R² 0.24 on held-out stations.