Articles | Volume 27, issue 7
https://doi.org/10.5194/hess-27-1583-2023
https://doi.org/10.5194/hess-27-1583-2023
Research article
 | 
17 Apr 2023
Research article |  | 17 Apr 2023

Improving regional climate simulations based on a hybrid data assimilation and machine learning method

Xinlei He, Yanping Li, Shaomin Liu, Tongren Xu, Fei Chen, Zhenhua Li, Zhe Zhang, Rui Liu, Lisheng Song, Ziwei Xu, Zhixing Peng, and Chen Zheng

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Cited articles

Ahmad, S. K., Kumar, S. V., Lahmers, T. M., Wang, S., Liu, P., Wrzesien, M. L., Bindlish, R., Getirana, A., Locke, K. A., Holmes, T. R., and Otkin, J. A.: Flash Drought Onset and Development Mechanisms Captured with Soil Moisture and Vegetation Data Assimilation, Water Resour. Res., 58, e2022WR032894, https://doi.org/10.1029/2022WR032894, 2022. 
Brajard, J., Carrassi, A., Bocquet, M., and Bertino, L.: Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: A case study with the Lorenz 96 model, J. Comput. Sci., 44, 101171, https://doi.org/10.1016/j.jocs.2020.101171, 2020. 
Buizza, C., Quilodrán Casas, C., Nadler, P., Mack, J., Marrone, S., Titus, Z., Le Cornec, C., Heylen, E., Dur, T., Baca Ruiz, L., Heaney, C., Díaz Lopez, J. A., Kumar, K. S. S., and Arcucci, R.: Data Learning: Integrating Data Assimilation and Machine Learning, J. Comput. Syst. Sci., 58, 101525, https://doi.org/10.1016/j.jocs.2021.101525, 2022. 
Campo, L., Castelli, F., Entekhabi, D., and Caparrini, F.: Land-atmosphere interactions in an high resolution atmospheric simulation coupled with a surface data assimilation scheme, Nat. Hazards Earth Syst. Sci., 9, 1613–1624, https://doi.org/10.5194/nhess-9-1613-2009, 2009. 
Cazes Boezio, G. and Ortelli, S.: Use of the WRF-DA 3D-Var Data Assimilation System to Obtain Wind Speed Estimates in Regular Grids from Measurements at Wind Farms in Uruguay, Data, 4, 142, https://doi.org/10.3390/data4040142, 2019. 
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Short summary
This study highlights the role of integrating vegetation and multi-source soil moisture observations in regional climate models via a hybrid data assimilation and machine learning method. In particular, we show that this approach can improve land surface fluxes, near-surface atmospheric conditions, and land–atmosphere interactions by implementing detailed land characterization information in basins with complex underlying surfaces.