Articles | Volume 15, issue 5
https://doi.org/10.5194/gmd-15-2221-2022
© 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-15-2221-2022
© 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
| 
16 Mar 2022
Development and technical paper |
| 16 Mar 2022
| 16 Mar 2022
Towards physics-inspired data-driven weather forecasting: integrating data assimilation with a deep spatial-transformer-based U-NET in a case study with ERA5
Ashesh Chattopadhyay
Department of Mechanical Engineering, Rice University, Houston, TX, USA
Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Mustafa Mustafa
Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Department of Mechanical Engineering, Rice University, Houston, TX, USA
Department of Earth, Environmental and Planetary Sciences, Rice University, Houston, TX, USA
Eviatar Bach
Department of Atmospheric and Oceanic Science and Institute for Physical Science and Technology, University of Maryland, College Park, USA
Geosciences Department and Laboratoire de Météorologie Dynamique (CNRS and IPSL), École Normale Supérieure and PSL University, Paris, France
Karthik Kashinath
Lawrence Berkeley National Laboratory, Berkeley, CA, USA
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Cited
28 citations as recorded by crossref.
- Deep Learning-Enhanced Ensemble-Based Data Assimilation for High-Dimensional Nonlinear Dynamical Systems A. Chattopadhyay et al. 10.2139/ssrn.4142015
- Learning spatiotemporal chaos using next-generation reservoir computing W. Barbosa & D. Gauthier 10.1063/5.0098707
- Statistical Deep Learning for Spatial and Spatiotemporal Data C. Wikle & A. Zammit-Mangion 10.1146/annurev-statistics-033021-112628
- Analog ensemble data assimilation in a quasigeostrophic coupled model I. Grooms et al. 10.1002/qj.4446
- Assessing the Feasibility of an NWP Satellite Data Assimilation System Entirely Based on AI Techniques E. Maddy et al. 10.1109/JSTARS.2024.3397078
- Deep learning-enhanced ensemble-based data assimilation for high-dimensional nonlinear dynamical systems A. Chattopadhyay et al. 10.1016/j.jcp.2023.111918
- Long-term stability and generalization of observationally-constrained stochastic data-driven models for geophysical turbulence A. Chattopadhyay et al. 10.1017/eds.2022.30
- Accelerating regional weather forecasting by super-resolution and data-driven methods A. Mikhaylov et al. 10.1515/jiip-2023-0078
- Accurate initial field estimation for weather forecasting with a variational constrained neural network W. Wang et al. 10.1038/s41612-024-00776-1
- Spatio‐Temporal Super‐Resolution Data Assimilation (SRDA) Utilizing Deep Neural Networks With Domain Generalization Y. Yasuda & R. Onishi 10.1029/2023MS003658
- Key factors for quantitative precipitation nowcasting using ground weather radar data based on deep learning D. Han et al. 10.5194/gmd-16-5895-2023
- Advancing neural network-based data assimilation for large-scale spatiotemporal systems with sparse observations S. Cai et al. 10.1063/5.0228384
- A Space-Time Partial Differential Equation Based Physics-Guided Neural Network for Sea Surface Temperature Prediction T. Yuan et al. 10.3390/rs15143498
- Equation‐Free Surrogate Modeling of Geophysical Flows at the Intersection of Machine Learning and Data Assimilation S. Pawar & O. San 10.1029/2022MS003170
- A Multi‐Model Ensemble Kalman Filter for Data Assimilation and Forecasting E. Bach & M. Ghil 10.1029/2022MS003123
- A Novel Transformer Network With Shifted Window Cross-Attention for Spatiotemporal Weather Forecasting A. Bojesomo et al. 10.1109/JSTARS.2023.3323729
- Data driven pathway analysis and forecast of global warming and sea level rise J. Song et al. 10.1038/s41598-023-30789-4
- Industrial Heat Source-Related PM2.5 Concentration Estimates and Analysis Using New Three-Stage Model in the Beijing–Tianjin–Hebei Region Y. Zeng et al. 10.3390/atmos15010131
- Stabilizing machine learning prediction of dynamics: Novel noise-inspired regularization tested with reservoir computing A. Wikner et al. 10.1016/j.neunet.2023.10.054
- Machine learning for the physics of climate A. Bracco et al. 10.1038/s42254-024-00776-3
- 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
- Precipitation nowcasting using transformer-based generative models and transfer learning for improved disaster preparedness M. Piran et al. 10.1016/j.jag.2024.103962
- A Four‐Dimensional Variational Constrained Neural Network‐Based Data Assimilation Method W. Wang et al. 10.1029/2023MS003687
- 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
- Proper Weather Forecasting Internet of Things Sensor Framework with Machine Learning A. Turukmane & S. Pande 10.4108/eetiot.5382
- Updates on Model Hierarchies for Understanding and Simulating the Climate System: A Focus on Data‐Informed Methods and Climate Change Impacts L. Mansfield et al. 10.1029/2023MS003715
- Towards physics-inspired data-driven weather forecasting: integrating data assimilation with a deep spatial-transformer-based U-NET in a case study with ERA5 A. Chattopadhyay et al. 10.5194/gmd-15-2221-2022
- BAMCAFE: A Bayesian machine learning advanced forecast ensemble method for complex turbulent systems with partial observations N. Chen & Y. Li 10.1063/5.0062028
22 citations as recorded by crossref.
- Deep Learning-Enhanced Ensemble-Based Data Assimilation for High-Dimensional Nonlinear Dynamical Systems A. Chattopadhyay et al. 10.2139/ssrn.4142015
- Learning spatiotemporal chaos using next-generation reservoir computing W. Barbosa & D. Gauthier 10.1063/5.0098707
- Statistical Deep Learning for Spatial and Spatiotemporal Data C. Wikle & A. Zammit-Mangion 10.1146/annurev-statistics-033021-112628
- Analog ensemble data assimilation in a quasigeostrophic coupled model I. Grooms et al. 10.1002/qj.4446
- Assessing the Feasibility of an NWP Satellite Data Assimilation System Entirely Based on AI Techniques E. Maddy et al. 10.1109/JSTARS.2024.3397078
- Deep learning-enhanced ensemble-based data assimilation for high-dimensional nonlinear dynamical systems A. Chattopadhyay et al. 10.1016/j.jcp.2023.111918
- Long-term stability and generalization of observationally-constrained stochastic data-driven models for geophysical turbulence A. Chattopadhyay et al. 10.1017/eds.2022.30
- Accelerating regional weather forecasting by super-resolution and data-driven methods A. Mikhaylov et al. 10.1515/jiip-2023-0078
- Accurate initial field estimation for weather forecasting with a variational constrained neural network W. Wang et al. 10.1038/s41612-024-00776-1
- Spatio‐Temporal Super‐Resolution Data Assimilation (SRDA) Utilizing Deep Neural Networks With Domain Generalization Y. Yasuda & R. Onishi 10.1029/2023MS003658
- Key factors for quantitative precipitation nowcasting using ground weather radar data based on deep learning D. Han et al. 10.5194/gmd-16-5895-2023
- Advancing neural network-based data assimilation for large-scale spatiotemporal systems with sparse observations S. Cai et al. 10.1063/5.0228384
- A Space-Time Partial Differential Equation Based Physics-Guided Neural Network for Sea Surface Temperature Prediction T. Yuan et al. 10.3390/rs15143498
- Equation‐Free Surrogate Modeling of Geophysical Flows at the Intersection of Machine Learning and Data Assimilation S. Pawar & O. San 10.1029/2022MS003170
- A Multi‐Model Ensemble Kalman Filter for Data Assimilation and Forecasting E. Bach & M. Ghil 10.1029/2022MS003123
- A Novel Transformer Network With Shifted Window Cross-Attention for Spatiotemporal Weather Forecasting A. Bojesomo et al. 10.1109/JSTARS.2023.3323729
- Data driven pathway analysis and forecast of global warming and sea level rise J. Song et al. 10.1038/s41598-023-30789-4
- Industrial Heat Source-Related PM2.5 Concentration Estimates and Analysis Using New Three-Stage Model in the Beijing–Tianjin–Hebei Region Y. Zeng et al. 10.3390/atmos15010131
- Stabilizing machine learning prediction of dynamics: Novel noise-inspired regularization tested with reservoir computing A. Wikner et al. 10.1016/j.neunet.2023.10.054
- Machine learning for the physics of climate A. Bracco et al. 10.1038/s42254-024-00776-3
- 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
- Precipitation nowcasting using transformer-based generative models and transfer learning for improved disaster preparedness M. Piran et al. 10.1016/j.jag.2024.103962
6 citations as recorded by crossref.
- A Four‐Dimensional Variational Constrained Neural Network‐Based Data Assimilation Method W. Wang et al. 10.1029/2023MS003687
- 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
- Proper Weather Forecasting Internet of Things Sensor Framework with Machine Learning A. Turukmane & S. Pande 10.4108/eetiot.5382
- Updates on Model Hierarchies for Understanding and Simulating the Climate System: A Focus on Data‐Informed Methods and Climate Change Impacts L. Mansfield et al. 10.1029/2023MS003715
- Towards physics-inspired data-driven weather forecasting: integrating data assimilation with a deep spatial-transformer-based U-NET in a case study with ERA5 A. Chattopadhyay et al. 10.5194/gmd-15-2221-2022
- BAMCAFE: A Bayesian machine learning advanced forecast ensemble method for complex turbulent systems with partial observations N. Chen & Y. Li 10.1063/5.0062028
Latest update: 13 Dec 2024
Short summary
There is growing interest in data-driven weather forecasting, i.e., to predict the weather by using a deep neural network that learns from the evolution of past atmospheric patterns. Here, we propose three components to add to the current data-driven weather forecast models to improve their performance. These components involve a feature that incorporates physics into the neural network, a method to add data assimilation, and an algorithm to use several different time intervals in the forecast.
There is growing interest in data-driven weather forecasting, i.e., to predict the weather by...
