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This page is part of the Climate Change AI Wiki, which aims provide resources at the intersection of climate change and machine learning.
Hybrid modelling, by incorporating physical-constraints into data-driven ML or deep learning models is a promising field of leveraging the large amounts of data available from observational products, while making use of physical constraints present in the climate system, to ensure robust projections and extrapolating well outside of the training data. The output from physics-driven GCM climate models can be used for a "perfect model test" of the ML models, before the ML model is applied to make projections based on the observations.
Background Readings[edit | edit source]
Conferences, Journals, and Professional Organizations[edit | edit source]
Libraries and Tools[edit | edit source]
Data[edit | edit source]
Future Directions[edit | edit source]
Relevant Groups and Organizations[edit | edit source]
References[edit | edit source]
- Reichstein, Markus; Camps-Valls, Gustau; Stevens, Bjorn; Jung, Martin; Denzler, Joachim; Carvalhais, Nuno; Prabhat (2019-02). "Deep learning and process understanding for data-driven Earth system science". Nature. 566 (7743): 195–204. doi:10.1038/s41586-019-0912-1. ISSN 1476-4687. Check date values in:
- Zhao, Wen Li; Gentine, Pierre; Reichstein, Markus; Zhang, Yao; Zhou, Sha; Wen, Yeqiang; Lin, Changjie; Li, Xi; Qiu, Guo Yu (2019). "Physics-Constrained Machine Learning of Evapotranspiration". Geophysical Research Letters. 46 (24): 14496–14507. doi:10.1029/2019GL085291. ISSN 1944-8007.
- Schlund, Manuel; Eyring, Veronika; Camps‐Valls, Gustau; Friedlingstein, Pierre; Gentine, Pierre; Reichstein, Markus (2020). "Constraining Uncertainty in Projected Gross Primary Production With Machine Learning". Journal of Geophysical Research: Biogeosciences. 125 (11): e2019JG005619. doi:10.1029/2019JG005619. ISSN 2169-8961.