Articles | Volume 28, issue 1
https://doi.org/10.5194/npg-28-111-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-28-111-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 Feb 2021
Research article |
| 09 Feb 2021
| 09 Feb 2021
Training a convolutional neural network to conserve mass in data assimilation
Yvonne Ruckstuhl
CORRESPONDING AUTHOR
Meteorological Institute Munich, Ludwig-Maximilians-Universität München, Munich, Germany
Tijana Janjić
Meteorological Institute Munich, Ludwig-Maximilians-Universität München, Munich, Germany
Stephan Rasp
ClimateAi, San Francisco, CA, USA
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Cited
19 citations as recorded by crossref.
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- Equation‐Free Surrogate Modeling of Geophysical Flows at the Intersection of Machine Learning and Data Assimilation S. Pawar & O. San 10.1029/2022MS003170
- Convergence of forecast distributions in a 100,000‐member idealised convective‐scale ensemble K. Tempest et al. 10.1002/qj.4410
- Combining Stochastic Parameterized Reduced‐Order Models With Machine Learning for Data Assimilation and Uncertainty Quantification With Partial Observations C. Mou et al. 10.1029/2022MS003597
- The effectiveness of machine learning methods in the nonlinear coupled data assimilation Z. Xuan et al. 10.1186/s40562-024-00347-5
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19 citations as recorded by crossref.
- Emotion Recognition and Regulation in English Teaching Based on Emotion Computing Technology J. Wang 10.1142/S0129156425401378
- Ensemble Kalman filter based data assimilation for tropical waves in the MJO skeleton model T. Gleiter et al. 10.1002/qj.4245
- Equation‐Free Surrogate Modeling of Geophysical Flows at the Intersection of Machine Learning and Data Assimilation S. Pawar & O. San 10.1029/2022MS003170
- Convergence of forecast distributions in a 100,000‐member idealised convective‐scale ensemble K. Tempest et al. 10.1002/qj.4410
- Combining Stochastic Parameterized Reduced‐Order Models With Machine Learning for Data Assimilation and Uncertainty Quantification With Partial Observations C. Mou et al. 10.1029/2022MS003597
- The effectiveness of machine learning methods in the nonlinear coupled data assimilation Z. Xuan et al. 10.1186/s40562-024-00347-5
- Towards neural Earth system modelling by integrating artificial intelligence in Earth system science C. Irrgang et al. 10.1038/s42256-021-00374-3
- Statistical variational data assimilation A. Benaceur & B. Verfürth 10.1016/j.cma.2024.117402
- Assessing the Physical Realism of Deep Learning Hydrologic Model Projections Under Climate Change S. Wi & S. Steinschneider 10.1029/2022WR032123
- Power line fault diagnosis based on convolutional neural networks L. Ning & D. Pei 10.1016/j.heliyon.2024.e29021
- Combining data assimilation and machine learning to estimate parameters of a convective‐scale model S. Legler & T. Janjić 10.1002/qj.4235
- Optimal sensor placement for ensemble-based data assimilation using gradient-weighted class activation mapping Z. Xu et al. 10.1016/j.jcp.2024.113224
- Super-Resolving Ocean Dynamics from Space with Computer Vision Algorithms B. Buongiorno Nardelli et al. 10.3390/rs14051159
- Integrating Recurrent Neural Networks With Data Assimilation for Scalable Data‐Driven State Estimation S. Penny et al. 10.1029/2021MS002843
- Weakly Constrained LETKF for Estimation of Hydrometeor Variables in Convective‐Scale Data Assimilation T. Janjić & Y. Zeng 10.1029/2021GL094962
- Advancements and Challenges in Deep Learning-Driven Marine Data Assimilation: A Comprehensive Review Y. Ma et al. 10.61186/crpase.9.4.2876
- Robot control system based on deep learning and RPA Y. Ren et al. 10.3233/JIFS-233056
- Modeling and control strategy of small unmanned helicopter rotation based on deep learning H. Xia 10.1016/j.sasc.2024.200146
- Using neural networks to improve simulations in the gray zone R. Kriegmair et al. 10.5194/npg-29-171-2022
Latest update: 16 Sep 2025
Short summary
The assimilation of observations using standard algorithms can lead to a violation of physical laws (e.g. mass conservation), which is shown to have a detrimental impact on the system's forecast. We use a neural network (NN) to correct this mass violation, using training data generated from expensive algorithms that can constrain such physical properties. We found that, in an idealized set-up, the NN can match the performance of these expensive algorithms at negligible computational costs.
The assimilation of observations using standard algorithms can lead to a violation of physical...
