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Machine Learning / Artificial Intelligence for S2S prediction
There is currently a lot excitement in the weather and climate communities to explore the potential of data driven approaches based on Artificial Intelligence/Machine Learning/Deep learning for S2S prediction through, for instance, improved parameterization, improved calibration and multi-model calibration, extreme event attribution, verification... The publicly available S2S database which contains a considerable amount of data (re-forecasts and real-time forecasts from 11 operational centres) represents an ideal testbed for these data-driven methods. The SubX database provides another such opportunity (http://cola.gmu.edu/subx/index.html).
Potential applications :
1. Improved data assimilation (e.g. better quality control of observations)
2. Improved parameterization (e.g. radiative schemes)
3. Improved post-processing (model calibration, bias-correction, multi-ensemble combination...)
4. Predictability diagnostics (e.g. teleconnections)
5. S2S event attribution (e.g. origins of extreme events)
6. Empirical forecasts
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Researches on AI/ML for S2S prediction
| 1. Scripps Institute of Oceanography The project explores the potential for modern machine learning tools to improve seasonal prediction skill of precipitation over the Western US. Modern machine learning approaches are 'data hungry' while observations are data limited (relatively short in length for the purposes of seasonal forecasting). To circumvent this issue, we train a variety of machine learning tools on perturbed initial condition climate model ensembles that span several thousands of years, then use these 'learnt' teleconnections to make seasonal predictions. We are testing a hierarchy of machine learning approaches from simple to complex: simple logistic regression, LASSO, Random Forests, Gradient Boosted decision trees, and convolutional neural networks. This project is a collaboration between researchers at Scripps CW3E and JPL, and funded by the California Department of Water Resources. (From Dr. Peter Gibson) |
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