OmniPMNet: Bridging discrete and gridded PM10 forecasts via omni-query neural processes
OmniPM-Net is a novel fusion model combining graph neural network station forecasts with chemical transport model gridded forecasts using a Convolutional Conditional Neural Process framework. Evaluated on 1,618 stations across China in 2024, it matches the best GNN accuracy while reducing CAMS MAE by 30% and providing continuous spatial fields. It excels during dust storms and high-concentration events.
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[Submitted on 12 Jun 2026]
Title:OmniPMNet: Bridging discrete and gridded PM10 forecasts via omni-query neural processes
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Abstract:Forecasting particulate matter (PM10) requires both station-scale accuracy and continuous spatial fields, especially during severe dust storms. Chemical transport models (CTMs) provide gridded forecasts but retain local biases, whereas graph neural networks (GNNs) track monitoring sites well at short lead times but do not produce gridded outputs. Here we present OmniPM-Net, a Convolutional Conditional Neural Process (ConvCNP)-based fusion model that reconciles these two forecast types within a shared spatial representation. A terrain-aware Gaussian set convolution lifts irregular GNN station forecasts onto a regular grid, where a multi-scale Spatial Source Attention (SSA) module blends them with Copernicus Atmosphere Monitoring Service (CAMS) forecasts; a shared omni-query readout then decodes this representation into consistent PM10 predictions at either stations or grid cells over a 108 h horizon. Evaluated across 1,618 air-quality monitoring stations throughout China over the full year of 2024, OmniPM-Net matches the station-level accuracy of the stronger GNN baseline (mean absolute error 21.14 versus 22.00 ug/m3) and reduces the CAMS mean absolute error by 30%, while simultaneously delivering the gridded fields that the discrete GNN cannot. Its clearest gains are in the high-concentration tail, where the 90th-percentile MAE falls by 9% relative to the GNN and 25% relative to CAMS, and during dust episodes, where it improves categorical detection skill while tracking the evolving spatial trajectory.
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Atmospheric and Oceanic Physics (physics.ao-ph)
Cite as: arXiv:2607.11896 [cs.LG]
(or arXiv:2607.11896v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.11896
arXiv-issued DOI via DataCite
Submission history
From: Shuangshuang He [view email] [v1] Fri, 12 Jun 2026 12:09:49 UTC (7,527 KB)
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