Air Quality Downscaling with Station-Guided Pseudo-Supervision
Super-resolving coarse atmospheric fields to local PM$_{2.5}$ variations is uniquely challenged by a mismatch in spatial support: while pixels represent regional averages, ground-truth observations are discrete, unaligned samples of a continuous spatial signal. To bridge this gap, we present a station-guided framework for high-resolution PM$_{2.5}$ downscaling over Europe. Taking coarse CAMS atmospheric composition fields alongside heterogeneous side information (i.e., human activity, land cover, elevation, satellite aerosol observations, and wind fields) our framework jointly super-resolves (
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- Linked via arxiv authorGuorun Wang →
Air Quality Downscaling with Station-Guided Pseudo-Supervision
- Linked via arxiv authorSimone Foti →
Air Quality Downscaling with Station-Guided Pseudo-Supervision
- Linked via arxiv authorAndreas D. Demou →
Air Quality Downscaling with Station-Guided Pseudo-Supervision
- Linked via arxiv authorLeonidas Kotoulas →
Air Quality Downscaling with Station-Guided Pseudo-Supervision
- Linked via arxiv authorTheodoros Christoudias →
Air Quality Downscaling with Station-Guided Pseudo-Supervision
- Linked via arxiv authorAlexandros Koliousis →
Air Quality Downscaling with Station-Guided Pseudo-Supervision
- Linked via arxiv authorMihalis Nicolaou →
Air Quality Downscaling with Station-Guided Pseudo-Supervision
- Linked via arxiv authorStefanos Zafeiriou →
Air Quality Downscaling with Station-Guided Pseudo-Supervision