Scalable machine learning for global weather prediction at high spatial and temporal resolutions

Professor John Taylor1

1Csiro, Canberra, Australia

Society has benefited enormously from the continuous advancement in numerical weather prediction that has occurred over many decades. These advances have been driven by a combination of outstanding scientific, computational and technological breakthroughs.

Here we demonstrate that data driven methods are now positioned to contribute to the next wave of major advances in atmospheric science. We show that data driven models can predict important meteorological quantities of interest to society such as global high resolution precipitation fields (0.25 degrees) and can deliver accurate forecasts of the future state of the atmosphere without prior knowledge of the laws of physics and chemistry. We also show how these data driven methods can be scaled to run on super-computers with up to 1024 modern graphics processing units (GPU) and beyond resulting in rapid training of data driven models, thus supporting a cycle of rapid research and innovation.

Taken together, these two results illustrate the significant potential of data driven methods to advance atmospheric science and operational weather forecasting.


Biography:

Prof. John Taylor is currently Research Group Leader in CSIRO Data61, and Chief Computational Scientist at the Defence Science and Technology Group and Honorary Professor, College of Engineering & Computer Science at ANU. He leads complex, multi-site, large scale interdisciplinary teams of research scientists, computational scientists, computer scientists and software engineers that are delivering high quality strategic science.

Date

Oct 14 2021
Expired!

Time

2:20 pm - 2:40 pm

Local Time

  • Timezone: America/New_York
  • Date: Oct 13 2021
  • Time: 11:20 pm - 11:40 pm