Project: Machine learning of clouds
The objective is to use new large cloud-resolving simulations to try and better represent cloud processes in coarse-resolution climate models (~100km in horizontal resolution). Those simulations are global (spanning the entire globe) at 2km resolution and 30-minute output. The data will be hosted on google cloud platform (Pangeo) (the data size is about 50TB). We will in particular evaluate the impact of using Constitutional Neural Network (in time and space) and the capacity for out of sample prediction.
This is an UNPAID research project.
- Professor Pierre Gentine
- Department/School: Earth and Env. Eng/SEAS
- Location: Morningside Campus
- Earliest starting date: 03/01/2019
- End date: 05/31/2019
- Number of hours per week of research expected during Spring 2019: ~6
- Skill sets: Machine Learning
- Student eligibility (as of Spring 2019): freshman, sophomore, junior, senior, master’s
- International students on F1 or J1 visa: eligible