ENSO prediction using Deep Learning #17
Replies: 5 comments 6 replies
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Hello! This project seems like something I'd like to work on! I have some ML experience in the climate science field, mainly methods like logistic regression, trees, and ANNs but would love to get more into deep learning. I am also interested exploring explainable/interpretable AI. |
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I'm also interested in working on this project! In particular, the idea of identifying relevance predictors seems helpful in terms of understanding the dynamics and "why/how" ENSO affects climate as opposed to solely the "what". I'd love to discuss this further after the Git tutorial. |
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I'm also interested in working on this project! I want to learn about ENSO prediction. |
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I'm interested in this project! I'm excited about using machine learning to learn about the effects of climate change on ocean |
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This project stood out to me the most! Looking forward to apply deep learning to climate forecasts. |
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Title
ENSO prediction using Deep Learning
Summary
We propose a project to develop a framework for ENSO prediction using a range of machine learning approaches and benchmark their skill against other methods. Identify observables that are most relevant for ENSO predictability at a particular timescale.
Personnel
Georgy Manucharyan and Scott Martin, University of Washington, School of Oceanography
Specific Tasks
To begin with, we will try using the large ensemble of climate model simulations https://www.cesm.ucar.edu/projects/community-projects/MMLEA/. Then we will proceed with transfer learning using reanalysis data, like ERA5. Then benchmark against other techniques. Finally, identify relevant predictors using relevance propagation techniques.
Reading
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