Articles | Volume 16, issue 16
Development and technical paper
29 Aug 2023
Development and technical paper |  | 29 Aug 2023

A gridded air quality forecast through fusing site-available machine learning predictions from RFSML v1.0 and chemical transport model results from GEOS-Chem v13.1.0 using the ensemble Kalman filter

Li Fang, Jianbing Jin, Arjo Segers, Hong Liao, Ke Li, Bufan Xu, Wei Han, Mijie Pang, and Hai Xiang Lin

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Cited articles

Bartier, P. M. and Keller, C. P.: Multivariate interpolation to incorporate thematic surface data using inverse distance weighting (IDW), Comput. Geosci., 22, 795–799, 1996. a
Bi, J., Knowland, K. E., Keller, C. A., and Liu, Y.: Combining Machine Learning and Numerical Simulation for High-Resolution PM2.5 Concentration Forecast, Environ. Sci. Technol., 56, 1544–1556,, 2022. a, b
Chen, B., Wang, Y., Huang, J., Zhao, L., Chen, R., Song, Z., and Hu, J.: Estimation of near-surface ozone concentration and analysis of main weather situation in China based on machine learning model and Himawari-8 TOAR data, Sci. Total Environ., 864, 160928,, 2023. a
Cheng, F.-Y., Feng, C.-Y., Yang, Z.-M., Hsu, C.-H., Chan, K.-W., Lee, C.-Y., and Chang, S.-C.: Evaluation of real-time PM2.5 forecasts with the WRF-CMAQ modeling system and weather-pattern-dependent bias-adjusted PM2.5 forecasts in Taiwan, Atmos. Environ., 244, 117909,, 2021a. a
Cheng, J., Tong, D., Zhang, Q., Liu, Y., Lei, Y., Yan, G., Yan, L., Yu, S., Cui, R. Y., Clarke, L., Geng, G., Zheng, B., Zhang, X., Davis, S. J., and He, K.: Pathways of China's PM2.5 air quality 2015–2060 in the context of carbon neutrality, Nat. Sci. Rev., 8, nwab078,, 2021b. a
Short summary
Machine learning models have gained great popularity in air quality prediction. However, they are only available at air quality monitoring stations. In contrast, chemical transport models (CTM) provide predictions that are continuous in the 3D field. Owing to complex error sources, they are typically biased. In this study, we proposed a gridded prediction with high accuracy by fusing predictions from our regional feature selection machine learning prediction (RFSML v1.0) and a CTM prediction.