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

Related authors

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. Discuss., https://doi.org/10.5194/gmd-2024-56,https://doi.org/10.5194/gmd-2024-56, 2024
Preprint under review for GMD
Short summary
Spatially Extensive Long-term Quality-assured Land-atmosphere Interactions Dataset over the Tibetan Plateau
Yaoming Ma, Zhipeng Xie, Yingying Chen, Shaoming Liu, Tao Che, Ziwei Xu, Lunyu Shang, Xiaobo He, Xianhong Meng, Weiqiang Ma, Baiqing Xu, Huabiao Zhao, Junbo Wang, Guangjian Wu, and Xin Li
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-9,https://doi.org/10.5194/essd-2024-9, 2024
Revised manuscript accepted for ESSD
Short summary
Quality evaluation for measurements of wind field and turbulent fluxes from a UAV-based eddy covariance system
Yibo Sun, Bilige Sude, Xingwen Lin, Bing Geng, Bo Liu, Shengnan Ji, Junping Jing, Zhiping Zhu, Ziwei Xu, Shaomin Liu, and Zhanjun Quan
Atmos. Meas. Tech., 16, 5659–5679, https://doi.org/10.5194/amt-16-5659-2023,https://doi.org/10.5194/amt-16-5659-2023, 2023
Short summary
Investigation of the climatology of low-level jets over North America in a high-resolution WRF simulation
Xiao Ma, Yanping Li, Zhenhua Li, and Fei Huo
EGUsphere, https://doi.org/10.5194/egusphere-2023-2342,https://doi.org/10.5194/egusphere-2023-2342, 2023
Short summary
A dataset of energy, water vapor, and carbon exchange observations in oasis–desert areas from 2012 to 2021 in a typical endorheic basin
Shaomin Liu, Ziwei Xu, Tao Che, Xin Li, Tongren Xu, Zhiguo Ren, Yang Zhang, Junlei Tan, Lisheng Song, Ji Zhou, Zhongli Zhu, Xiaofan Yang, Rui Liu, and Yanfei Ma
Earth Syst. Sci. Data, 15, 4959–4981, https://doi.org/10.5194/essd-15-4959-2023,https://doi.org/10.5194/essd-15-4959-2023, 2023
Short summary

Related subject area

Subject: Ecohydrology | Techniques and Approaches: Modelling approaches
Machine learning and global vegetation: random forests for downscaling and gap filling
Barry van Jaarsveld, Sandra M. Hauswirth, and Niko Wanders
Hydrol. Earth Syst. Sci., 28, 2357–2374, https://doi.org/10.5194/hess-28-2357-2024,https://doi.org/10.5194/hess-28-2357-2024, 2024
Short summary
Unraveling phenological and stomatal responses to flash drought and implications for water and carbon budgets
Nicholas K. Corak, Jason A. Otkin, Trent W. Ford, and Lauren E. L. Lowman
Hydrol. Earth Syst. Sci., 28, 1827–1851, https://doi.org/10.5194/hess-28-1827-2024,https://doi.org/10.5194/hess-28-1827-2024, 2024
Short summary
Bias-blind and bias-aware assimilation of leaf area index into the Noah-MP land surface model over Europe
Samuel Scherrer, Gabriëlle De Lannoy, Zdenko Heyvaert, Michel Bechtold, Clement Albergel, Tarek S. El-Madany, and Wouter Dorigo
Hydrol. Earth Syst. Sci., 27, 4087–4114, https://doi.org/10.5194/hess-27-4087-2023,https://doi.org/10.5194/hess-27-4087-2023, 2023
Short summary
Regional patterns and drivers of water flows along environmental, functional and stand structure gradients in Spanish forests
Jesús Sánchez-Dávila, Miquel De Cáceres, Jordi Vayreda, and Javier Retana
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-255,https://doi.org/10.5194/hess-2023-255, 2023
Revised manuscript accepted for HESS
Short summary
Technical note: Seamless extraction and analysis of river networks in R
Luca Carraro
Hydrol. Earth Syst. Sci., 27, 3733–3742, https://doi.org/10.5194/hess-27-3733-2023,https://doi.org/10.5194/hess-27-3733-2023, 2023
Short summary

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. 
Download
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.