ENAC Seminar Series by Prof. P. Gentine
Event details
| Date | 31.01.2019 |
| Hour | 09:00 › 10:00 |
| Speaker | Prof. P. Gentine |
| Location | |
| Category | Conferences - Seminars |
9:00 – 10:00 – Prof. Pierre Gentine
Associate Professor, Columbia University, USA
Multiscale land-atmosphere interactions
We are witnessing profound changes in our observational capacity of the biosphere and atmosphere with new generations of remote sensing platforms. Simultaneously, advances in our computational capacity have allowed us to better resolve multiple scales of the atmosphere from molecular scale to deep convective clouds. Yet, fully exploiting those massive datasets remains a major challenge. In this talk, I will show how machine learning, causality discovery and physical approaches can be combined to address some important questions related to the global carbon and water cycles and climate. I will in particular show how these techniques can be used to better quantify global environmental changes especially in cold and tropical regions as well as how machine learning can be used to break the so-called cloud convection deadlock, which is at the core of the uncertainties in our climate predictions.
Associate Professor, Columbia University, USA
Multiscale land-atmosphere interactions
We are witnessing profound changes in our observational capacity of the biosphere and atmosphere with new generations of remote sensing platforms. Simultaneously, advances in our computational capacity have allowed us to better resolve multiple scales of the atmosphere from molecular scale to deep convective clouds. Yet, fully exploiting those massive datasets remains a major challenge. In this talk, I will show how machine learning, causality discovery and physical approaches can be combined to address some important questions related to the global carbon and water cycles and climate. I will in particular show how these techniques can be used to better quantify global environmental changes especially in cold and tropical regions as well as how machine learning can be used to break the so-called cloud convection deadlock, which is at the core of the uncertainties in our climate predictions.
Practical information
- General public
- Free
Organizer
- ENAC
Contact
- Cristina Perez