Data Science Working Group
The US CLIVAR Working Group on Emerging Data Science Tools for Climate Variability and Predictability was formed in September 2019. The purpose of the Working Group is to help foster the understanding, adoption, and further development of modern data science tools for the analysis of large-to-massive climate data sets. It brings together experts in Earth science, statisticians, and computer scientists with the specific goal of fostering collaboration across these disciplinary boundaries to achieve US CLIVAR's scientific objectives.
The main objectives of the Working Group are:
- Identify best practices for interpretability and predictive capacity.
- Gather, prepare, and encourage use of benchmark training data sets.
- Provide timely perspectives on emerging data sciences tools.
| Amy Braverman (co-chair) | California Institute of Technology/NASA JPL |
| Bo Li (co-chair) | University of Illinois |
| Ryan Abernathey | Columbia University |
| Noah Brenowitz | Vulcan Inc. |
| Tom Beucler | University of Lausanne, Switzerland |
| Emily Kang | University of Cincinnati |
| Vladimir Krasnopolsky | NOAA EMC |
| Paul Loikith | Portland State University |
| Mike Pritchard | University of California, Irvine |
| Bruno Sansó | University of California, Santa Cruz |
| Richard Smith | University of North Carolina |
| Ying Sun | Cornell University |
| Laure Zanna | New York University |