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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Autores principales:	Xingxing Ma, Hongnian Liu, Zhen Peng
Formato:	Artigo
Lenguaje:	Inglês
Publicado:	Elsevier 2025-01-01
Colección:	Environment International
Materias:	WRF-Chem Bias correction Data assimilation PM2.5 concentrations Optimization
Acceso en línea:	http://www.sciencedirect.com/science/article/pii/S0160412024007864
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