Summary
As the climate crisis progresses, and we see an increase in extreme temperatures, the importance of accuracy in regional weather forecasting significantly increases. These regional models, or limited area models (LAMs), run at the highest feasible resolution to well resolve fine grain features in the model. Due to the global nature of the atmosphere, LAMs are coupled to a global forecast model, which due to the larger size must run at a coarser resolution and does not see the fine grain structures. This project will increase the accuracy of this coupling between LAM and global model. Specifically, the core focus is to utilise deep learning to recover accurate fine grain structures from a coarse global model to be incorporated as boundary data to the LAM.
The philosophy followed is that if one wants to couple two models it is paramount to preserve the physical structures between the two models. One may think of such structures as conserved quantities here. In addition to utilising this philosophy to optimise the coupling between models in the traditional (deterministic) sense, new technologies in structure preserving deep learning will be developed. These aim to resolve the fine grain features to be qualitatively consistent with a global model ran at high resolution.
This is an interesting problem from a mathematical perspective as it applies expertise from numerical analysis and geometric numerical integration to develop the field of machine learning.
This project has been designed to be in line with the UK Met Office atmospheric models and is of high research interest to them.
The philosophy followed is that if one wants to couple two models it is paramount to preserve the physical structures between the two models. One may think of such structures as conserved quantities here. In addition to utilising this philosophy to optimise the coupling between models in the traditional (deterministic) sense, new technologies in structure preserving deep learning will be developed. These aim to resolve the fine grain features to be qualitatively consistent with a global model ran at high resolution.
This is an interesting problem from a mathematical perspective as it applies expertise from numerical analysis and geometric numerical integration to develop the field of machine learning.
This project has been designed to be in line with the UK Met Office atmospheric models and is of high research interest to them.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101108679 |
| Start date: | 01-09-2023 |
| End date: | 31-08-2025 |
| Total budget - Public funding: | - 210 911,00 Euro |
Cordis data
Original description
As the climate crisis progresses, and we see an increase in extreme temperatures, the importance of accuracy in regional weather forecasting significantly increases. These regional models, or limited area models (LAMs), run at the highest feasible resolution to well resolve fine grain features in the model. Due to the global nature of the atmosphere, LAMs are coupled to a global forecast model, which due to the larger size must run at a coarser resolution and does not see the fine grain structures. This project will increase the accuracy of this coupling between LAM and global model. Specifically, the core focus is to utilise deep learning to recover accurate fine grain structures from a coarse global model to be incorporated as boundary data to the LAM.The philosophy followed is that if one wants to couple two models it is paramount to preserve the physical structures between the two models. One may think of such structures as conserved quantities here. In addition to utilising this philosophy to optimise the coupling between models in the traditional (deterministic) sense, new technologies in structure preserving deep learning will be developed. These aim to resolve the fine grain features to be qualitatively consistent with a global model ran at high resolution.
This is an interesting problem from a mathematical perspective as it applies expertise from numerical analysis and geometric numerical integration to develop the field of machine learning.
This project has been designed to be in line with the UK Met Office atmospheric models and is of high research interest to them.
Status
SIGNEDCall topic
HORIZON-MSCA-2022-PF-01-01Update Date
31-07-2023
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