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
24 citations as recorded by crossref.
- Data assimilation with machine learning for dynamical systems: Modelling indoor ventilation C. Heaney et al.
- Deep Learning for Ocean Forecasting: A Comprehensive Review of Methods, Applications, and Datasets R. Hao et al.
- Data assimilation with extremum Monte Carlo methods K. Moussa & S. Koopman
- A hybrid two-level MCMC framework to accelerate posterior mean estimation with deep learning surrogates for bayesian inverse problems J. Yang et al.
- Machine Learning With Data Assimilation and Uncertainty Quantification for Dynamical Systems: A Review S. Cheng et al.
- AI-Empowered Latent Four-dimensional Variational Data Assimilation for River Discharge Forecasting K. Wang et al.
- Data-driven stochastic spectral modeling for coarsening of the two-dimensional Euler equations on the sphere S. Ephrati et al.
- Accurate initial field estimation for weather forecasting with a variational constrained neural network W. Wang et al.
- Leveraging interpolation models and error bounds for verifiable scientific machine learning T. Chang et al.
- Long-Time Accuracy of Ensemble Kalman Filters for Chaotic Dynamical Systems and Machine-Learned Dynamical Systems D. Sanz-Alonso & N. Waniorek
- Artificial Intelligence and Its Application in Numerical Weather Prediction S. Soldatenko
- 资料同化中的人工智能技术：新兴方法、关键挑战与未来展望 悟. 王 et al.
- Data Assimilation in Hydrological Models: Methods, Challenges and Emerging Trends X. Yuan et al.
- Multi-domain encoder–decoder neural networks for latent data assimilation in dynamical systems S. Cheng et al.
- Quantifying uncertainty for deep learning based forecasting and flow-reconstruction using neural architecture search ensembles R. Maulik et al.
- Blending machine learning and sequential data assimilation over latent spaces for surrogate modeling of Boussinesq systems S. Akbari et al.
- Applying Machine Learning in Numerical Weather and Climate Modeling Systems V. Krasnopolsky
- Data assimilation in machine-learned reduced-order model of chaotic earthquake sequences H. Kaveh et al.
- Artificial Intelligence techniques in data assimilation: Emerging approaches, key challenges, and future prospects W. Wang et al.
- TorchDA: A Python package for performing data assimilation with deep learning forward and transformation functions S. Cheng et al.
- Artificial intelligence and numerical weather prediction models: A technical survey M. Waqas et al.
- Physically consistent global atmospheric data assimilation with machine learning in latent space H. Fan et al.
- Generative Lagrangian data assimilation for ocean dynamics under extreme sparsity N. Asefi et al.
- A deep learning model for ocean surface latent heat flux based on transformer and data assimilation Y. Liu et al.
24 citations as recorded by crossref.
- Data assimilation with machine learning for dynamical systems: Modelling indoor ventilation C. Heaney et al.
- Deep Learning for Ocean Forecasting: A Comprehensive Review of Methods, Applications, and Datasets R. Hao et al.
- Data assimilation with extremum Monte Carlo methods K. Moussa & S. Koopman
- A hybrid two-level MCMC framework to accelerate posterior mean estimation with deep learning surrogates for bayesian inverse problems J. Yang et al.
- Machine Learning With Data Assimilation and Uncertainty Quantification for Dynamical Systems: A Review S. Cheng et al.
- AI-Empowered Latent Four-dimensional Variational Data Assimilation for River Discharge Forecasting K. Wang et al.
- Data-driven stochastic spectral modeling for coarsening of the two-dimensional Euler equations on the sphere S. Ephrati et al.
- Accurate initial field estimation for weather forecasting with a variational constrained neural network W. Wang et al.
- Leveraging interpolation models and error bounds for verifiable scientific machine learning T. Chang et al.
- Long-Time Accuracy of Ensemble Kalman Filters for Chaotic Dynamical Systems and Machine-Learned Dynamical Systems D. Sanz-Alonso & N. Waniorek
- Artificial Intelligence and Its Application in Numerical Weather Prediction S. Soldatenko
- 资料同化中的人工智能技术：新兴方法、关键挑战与未来展望 悟. 王 et al.
- Data Assimilation in Hydrological Models: Methods, Challenges and Emerging Trends X. Yuan et al.
- Multi-domain encoder–decoder neural networks for latent data assimilation in dynamical systems S. Cheng et al.
- Quantifying uncertainty for deep learning based forecasting and flow-reconstruction using neural architecture search ensembles R. Maulik et al.
- Blending machine learning and sequential data assimilation over latent spaces for surrogate modeling of Boussinesq systems S. Akbari et al.
- Applying Machine Learning in Numerical Weather and Climate Modeling Systems V. Krasnopolsky
- Data assimilation in machine-learned reduced-order model of chaotic earthquake sequences H. Kaveh et al.
- Artificial Intelligence techniques in data assimilation: Emerging approaches, key challenges, and future prospects W. Wang et al.
- TorchDA: A Python package for performing data assimilation with deep learning forward and transformation functions S. Cheng et al.
- Artificial intelligence and numerical weather prediction models: A technical survey M. Waqas et al.
- Physically consistent global atmospheric data assimilation with machine learning in latent space H. Fan et al.
- Generative Lagrangian data assimilation for ocean dynamics under extreme sparsity N. Asefi et al.
- A deep learning model for ocean surface latent heat flux based on transformer and data assimilation Y. Liu et al.
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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...
