USMILE is an active interdisciplinary team led by the four PIs with an extensive international network, engaged in vibrant AI networks like ELLIS and ESA PhiLab and in climate modelling initiatives like the Coupled Model Intercomparison Project (CMIP).
An interdisciplinary team of four researchers from the German Aerospace Center (DLR), the Max Planck Institute for Biogeochemistry, the University of Valencia, and Columbia University has been awarded a 2019 European Research Council (ERC) Synergy Grant to understand and model the Earth system with machine learning, one of the important approaches of artificial intelligence (AI). The prestigious award — 10 million euros over six years — will support the team’s groundbreaking work in rethinking the development and evaluation of Earth system models, which are the basis for understanding and projecting climate change.
ERC Synergy Grants are awarded to groups of two to four co-PIs who have complementary skills, knowledge and resources, and can jointly address research problems that could lead to breakthroughs not possible by the individual PIs working alone. The four PIs on the USMILE project all work at the intersection of Earth system and data science and have complementary backgrounds. They include:
- Veronika Eyring, head of the Earth System Model Evaluation and Analysis Department at the German Aerospace Center (DLR) Institute of Atmospheric Physics and Professor of Climate Modelling at the University of Bremen, has long-term experience in atmospheric physics and Earth system modelling. She is a pioneer in process-oriented Earth system model evaluation and analysis and also the founder of the Climate Informatics group at the DLR Institute of Data Science.
- Markus Reichstein, Director of the Biogeochemical Integration Department at the Max-Planck Institute for Biogeochemistry and Professor for Global Geoecology at the University of Jena, adds important expertise with terrestrial ecosystems and their response to climate variability and extremes. He has pioneered the application of machine learning methods to global carbon and water cycles.
- Pierre Gentine, Associate Professor of Earth and Environmental Engineering at Columbia University’s School of Engineering and Applied Science, contributes expertise in analysing atmospheric and land-surface processes using machine learning, as well as experience in high-resolution turbulence and cloud modelling. He has pioneered the use of machine learning for the parameterisation of deep convection in idealized simulations and uses machine learning for remote sensing retrievals and products.
- Gustau Camps-Valls, Professor in Electrical Engineering and head of the Image and Signal Processing group in the Image Processing Laboratory (IPL) at the University of Valencia brings broad expertise on the development of machine learning for remote sensing and geosciences. He has pushed the fields of kernel methods and deep learning for Earth observation data analysis, for classification, anomaly detection and biogeophysical parameter estimation.
The team is supported by the Computer Vision Group around Prof Denzler at the Friedrich Schiller University which contributes its many years of expertise in the development of machine learning techniques for anomaly and causality detection.
From the Earth and Solar System Research Partnership of the Max Planck Society (ESRP), the colleagues around Prof Stevens from MPI for Meteorology will participate in the project with high-resolution simulations.
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