Articles | Volume 16, issue 16
https://doi.org/10.5194/gmd-16-4867-2023
https://doi.org/10.5194/gmd-16-4867-2023
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

Related authors

South Asia ammonia emission inversion through assimilating IASI observations
Ji Xia, Yi Zhou, Li Fang, Yingfei Qi, Dehao Li, Hong Liao, and Jianbing Jin
EGUsphere, https://doi.org/10.5194/egusphere-2024-3938,https://doi.org/10.5194/egusphere-2024-3938, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Observational operator for fair model evaluation with ground NO2 measurements
Li Fang, Jianbing Jin, Arjo Segers, Ke Li, Ji Xia, Wei Han, Baojie Li, Hai Xiang Lin, Lei Zhu, Song Liu, and Hong Liao
Geosci. Model Dev., 17, 8267–8282, https://doi.org/10.5194/gmd-17-8267-2024,https://doi.org/10.5194/gmd-17-8267-2024, 2024
Short summary
Valid time shifting ensemble Kalman filter (VTS-EnKF) for dust storm forecasting
Mijie Pang, Jianbing Jin, Arjo Segers, Huiya Jiang, Wei Han, Batjargal Buyantogtokh, Ji Xia, Li Fang, Jiandong Li, Hai Xiang Lin, and Hong Liao
Geosci. Model Dev., 17, 8223–8242, https://doi.org/10.5194/gmd-17-8223-2024,https://doi.org/10.5194/gmd-17-8223-2024, 2024
Short summary
Development of a regional feature selection-based machine learning system (RFSML v1.0) for air pollution forecasting over China
Li Fang, Jianbing Jin, Arjo Segers, Hai Xiang Lin, Mijie Pang, Cong Xiao, Tuo Deng, and Hong Liao
Geosci. Model Dev., 15, 7791–7807, https://doi.org/10.5194/gmd-15-7791-2022,https://doi.org/10.5194/gmd-15-7791-2022, 2022
Short summary
Inverse modeling of the 2021 spring super dust storms in East Asia
Jianbing Jin, Mijie Pang, Arjo Segers, Wei Han, Li Fang, Baojie Li, Haochuan Feng, Hai Xiang Lin, and Hong Liao
Atmos. Chem. Phys., 22, 6393–6410, https://doi.org/10.5194/acp-22-6393-2022,https://doi.org/10.5194/acp-22-6393-2022, 2022
Short summary

Related subject area

Atmospheric sciences
Exploring a high-level programming model for the NWP domain using ECMWF microphysics schemes
Stefano Ubbiali, Christian Kühnlein, Christoph Schär, Linda Schlemmer, Thomas C. Schulthess, Michael Staneker, and Heini Wernli
Geosci. Model Dev., 18, 529–546, https://doi.org/10.5194/gmd-18-529-2025,https://doi.org/10.5194/gmd-18-529-2025, 2025
Short summary
Quantifying uncertainties in satellite NO2 superobservations for data assimilation and model evaluation
Pieter Rijsdijk, Henk Eskes, Arlene Dingemans, K. Folkert Boersma, Takashi Sekiya, Kazuyuki Miyazaki, and Sander Houweling
Geosci. Model Dev., 18, 483–509, https://doi.org/10.5194/gmd-18-483-2025,https://doi.org/10.5194/gmd-18-483-2025, 2025
Short summary
ML-AMPSIT: Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool
Dario Di Santo, Cenlin He, Fei Chen, and Lorenzo Giovannini
Geosci. Model Dev., 18, 433–459, https://doi.org/10.5194/gmd-18-433-2025,https://doi.org/10.5194/gmd-18-433-2025, 2025
Short summary
Coupling the urban canopy model TEB (SURFEXv9.0) with the radiation model SPARTACUS-Urbanv0.6.1 for more realistic urban radiative exchange calculation
Robert Schoetter, Robin James Hogan, Cyril Caliot, and Valéry Masson
Geosci. Model Dev., 18, 405–431, https://doi.org/10.5194/gmd-18-405-2025,https://doi.org/10.5194/gmd-18-405-2025, 2025
Short summary
Forecasting contrail climate forcing for flight planning and air traffic management applications: the CocipGrid model in pycontrails 0.51.0
Zebediah Engberg, Roger Teoh, Tristan Abbott, Thomas Dean, Marc E. J. Stettler, and Marc L. Shapiro
Geosci. Model Dev., 18, 253–286, https://doi.org/10.5194/gmd-18-253-2025,https://doi.org/10.5194/gmd-18-253-2025, 2025
Short summary

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, https://doi.org/10.1021/acs.est.1c05578, 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, https://doi.org/10.1016/j.scitotenv.2022.160928, 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, https://doi.org/10.1016/j.atmosenv.2020.117909, 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, https://doi.org/10.1093/nsr/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.
Share