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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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-01-01
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| Series: | Environment International |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0160412024007864 |
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