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Structure preserving limited area weather modelling

CORDIS · observation · Publication date unknown

n 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 th

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recordType

award

status

SIGNED

region

EU

value

210911.04

unit

EUR