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 (
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- LinkedLinked via arxiv author · 85%Guorun Wang →
“Air Quality Downscaling with Station-Guided Pseudo-Supervision”
- LinkedLinked via arxiv author · 85%Simone Foti →
“Air Quality Downscaling with Station-Guided Pseudo-Supervision”
- LinkedLinked via arxiv author · 85%Andreas D. Demou →
“Air Quality Downscaling with Station-Guided Pseudo-Supervision”
- LinkedLinked via arxiv author · 85%Leonidas Kotoulas →
“Air Quality Downscaling with Station-Guided Pseudo-Supervision”
- LinkedLinked via arxiv author · 85%Theodoros Christoudias →
“Air Quality Downscaling with Station-Guided Pseudo-Supervision”
- LinkedLinked via arxiv author · 85%Alexandros Koliousis →
“Air Quality Downscaling with Station-Guided Pseudo-Supervision”
- LinkedLinked via arxiv author · 85%Mihalis Nicolaou →
“Air Quality Downscaling with Station-Guided Pseudo-Supervision”
- LinkedLinked via arxiv author · 85%Stefanos Zafeiriou →
“Air Quality Downscaling with Station-Guided Pseudo-Supervision”