Machine Learning Roles in Earth System Modeling

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Event Details
  • Date/Time:
    • Thursday February 4, 2021 - Friday February 5, 2021
      11:00 am - 11:59 am
  • Location: Virtual seminar
  • Phone:
  • URL: BlueJeans
  • Email:
  • Fee(s):
    Free
  • Extras:
Contact

Dr. Alex Robel

Summaries

Summary Sentence: A seminar by Dr. David John Gagne, School of Earth and Atmospheric Sciences

Full Summary: No summary paragraph submitted.

Media
  • David John Gagne David John Gagne
    (image/jpeg)

The School of Earth and Atmospheric Sciences Presents Dr. David John Gagne, National Center for Atmospheric Research

Machine Learning Roles in Earth System Modeling

Machine learning has the potential to improve many aspects of Earth System modeling by accelerating computationally intensive process models with emulation and by bridging modeling gaps where no clear physical equation can be derived. However, many challenges remain in ensuring that machine learning models can perform robustly and integrate seamlessly within Earth System models. 
 
The NCAR Analytics and Integrative Machine Learning group is currently working on machine learning challenges for ocean mixed layer depth retrievals, bin microphysics emulation, and atmospheric chemistry modeling. We will discuss our successes as well as challenges in ensuring robust online performance and incorporating emulators within existing simulations.

Additional Information

In Campus Calendar
Yes
Groups

EAS

Invited Audience
Faculty/Staff, Postdoc, Graduate students, Undergraduate students
Categories
Seminar/Lecture/Colloquium
Keywords
EAS Seminar
Status
  • Created By: nlawson3
  • Workflow Status: Published
  • Created On: Dec 10, 2020 - 8:49am
  • Last Updated: Jan 25, 2021 - 12:29pm