ML4Land: Using Earth’s observation data, Climate reanalysis & Machine Learning to detect Earth’s heating patterns
Skin temperature has been pivotal in identifying the heating and land-use patterns of Earth. The project aims to learn a mapping from model simulations (using ERA5) to satellite observations of skin temperature. Various works have shown how Machine Learning based models can efficiently recognize and learn useful patterns from complex datasets. We thus aim to use Machine Learning algorithms to learn the mapping between ERA5 variables and satellite observations of maximal skin temperature. These solutions will provide predictions at higher resolutions and offer valuable insights into the relationships between skin temperature and various ERA5 variables.
Follow the developments on GitHub
Mentors
- Gianpaolo Balsamo
- Joe Mc Norton
- Gabriele Arduini
- Margarita Choulga
- Souhail Boussetta
- Nils Wedi
- Peter Düben
Participants
Het Shah
Avishree Khare