Improving WRF-Chem PM2.5 predictions by combining data assimilation and deep-learning-based bias correction

In numerical model simulations, data assimilation (DA) on the initial conditions and bias correction (BC) of model outputs have been proven to be promising approaches to improving PM2.5 (particulate matter with an aerodynamic equivalent diameter of ≤ 2.5 μm) predictions. This study compared the opti...

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Hlavní autoři:	Xingxing Ma, Hongnian Liu, Zhen Peng
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	Elsevier 2025-01-01
Edice:	Environment International
Témata:	WRF-Chem Bias correction Data assimilation PM2.5 concentrations Optimization
On-line přístup:	http://www.sciencedirect.com/science/article/pii/S0160412024007864
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