Articles | Volume 15, issue 8
https://doi.org/10.5194/gmd-15-3433-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-3433-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
02 May 2022
Development and technical paper |
| 02 May 2022
| 02 May 2022
Efficient high-dimensional variational data assimilation with machine-learned reduced-order models
Romit Maulik
CORRESPONDING AUTHOR
240, Argonne National Laboratory, Lemont, IL 60439, USA
Vishwas Rao
240, Argonne National Laboratory, Lemont, IL 60439, USA
Jiali Wang
240, Argonne National Laboratory, Lemont, IL 60439, USA
Gianmarco Mengaldo
Department of Mechanical Engineering, National University of Singapore, Block EA, #07-08, 9 Engineering Drive 1, Singapore
Emil Constantinescu
240, Argonne National Laboratory, Lemont, IL 60439, USA
Bethany Lusch
240, Argonne National Laboratory, Lemont, IL 60439, USA
Prasanna Balaprakash
240, Argonne National Laboratory, Lemont, IL 60439, USA
Ian Foster
240, Argonne National Laboratory, Lemont, IL 60439, USA
Rao Kotamarthi
240, Argonne National Laboratory, Lemont, IL 60439, USA
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Cited
15 citations as recorded by crossref.
- Data assimilation with machine learning for dynamical systems: Modelling indoor ventilation C. Heaney et al. 10.1016/j.physa.2024.129783
- Multi-domain encoder–decoder neural networks for latent data assimilation in dynamical systems S. Cheng et al. 10.1016/j.cma.2024.117201
- Deep Learning for Ocean Forecasting: A Comprehensive Review of Methods, Applications, and Datasets R. Hao et al. 10.1109/TCYB.2025.3539990
- Quantifying uncertainty for deep learning based forecasting and flow-reconstruction using neural architecture search ensembles R. Maulik et al. 10.1016/j.physd.2023.133852
- Machine Learning With Data Assimilation and Uncertainty Quantification for Dynamical Systems: A Review S. Cheng et al. 10.1109/JAS.2023.123537
- Blending machine learning and sequential data assimilation over latent spaces for surrogate modeling of Boussinesq systems S. Akbari et al. 10.1016/j.physd.2023.133711
- Applying Machine Learning in Numerical Weather and Climate Modeling Systems V. Krasnopolsky 10.3390/cli12060078
- TorchDA: A Python package for performing data assimilation with deep learning forward and transformation functions S. Cheng et al. 10.1016/j.cpc.2024.109359
- Data-driven stochastic spectral modeling for coarsening of the two-dimensional Euler equations on the sphere S. Ephrati et al. 10.1063/5.0156942
- Artificial intelligence and numerical weather prediction models: A technical survey M. Waqas et al. 10.1016/j.nhres.2024.11.004
- Accurate initial field estimation for weather forecasting with a variational constrained neural network W. Wang et al. 10.1038/s41612-024-00776-1
- Leveraging interpolation models and error bounds for verifiable scientific machine learning T. Chang et al. 10.1016/j.jcp.2025.113726
- Artificial Intelligence and Its Application in Numerical Weather Prediction S. Soldatenko 10.3103/S1068373924040010
- Equation‐Free Surrogate Modeling of Geophysical Flows at the Intersection of Machine Learning and Data Assimilation S. Pawar & O. San 10.1029/2022MS003170
- Efficient high-dimensional variational data assimilation with machine-learned reduced-order models R. Maulik et al. 10.5194/gmd-15-3433-2022
13 citations as recorded by crossref.
- Data assimilation with machine learning for dynamical systems: Modelling indoor ventilation C. Heaney et al. 10.1016/j.physa.2024.129783
- Multi-domain encoder–decoder neural networks for latent data assimilation in dynamical systems S. Cheng et al. 10.1016/j.cma.2024.117201
- Deep Learning for Ocean Forecasting: A Comprehensive Review of Methods, Applications, and Datasets R. Hao et al. 10.1109/TCYB.2025.3539990
- Quantifying uncertainty for deep learning based forecasting and flow-reconstruction using neural architecture search ensembles R. Maulik et al. 10.1016/j.physd.2023.133852
- Machine Learning With Data Assimilation and Uncertainty Quantification for Dynamical Systems: A Review S. Cheng et al. 10.1109/JAS.2023.123537
- Blending machine learning and sequential data assimilation over latent spaces for surrogate modeling of Boussinesq systems S. Akbari et al. 10.1016/j.physd.2023.133711
- Applying Machine Learning in Numerical Weather and Climate Modeling Systems V. Krasnopolsky 10.3390/cli12060078
- TorchDA: A Python package for performing data assimilation with deep learning forward and transformation functions S. Cheng et al. 10.1016/j.cpc.2024.109359
- Data-driven stochastic spectral modeling for coarsening of the two-dimensional Euler equations on the sphere S. Ephrati et al. 10.1063/5.0156942
- Artificial intelligence and numerical weather prediction models: A technical survey M. Waqas et al. 10.1016/j.nhres.2024.11.004
- Accurate initial field estimation for weather forecasting with a variational constrained neural network W. Wang et al. 10.1038/s41612-024-00776-1
- Leveraging interpolation models and error bounds for verifiable scientific machine learning T. Chang et al. 10.1016/j.jcp.2025.113726
- Artificial Intelligence and Its Application in Numerical Weather Prediction S. Soldatenko 10.3103/S1068373924040010
2 citations as recorded by crossref.
Latest update: 29 Jul 2025
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Short summary
In numerical weather prediction, data assimilation is frequently utilized to enhance the accuracy of forecasts from equation-based models. In this work we use a machine learning framework that approximates a complex dynamical system given by the geopotential height. Instead of using an equation-based model, we utilize this machine-learned alternative to dramatically accelerate both the forecast and the assimilation of data, thereby reducing need for large computational resources.
In numerical weather prediction, data assimilation is frequently utilized to enhance the...
