Advances in Machine Learning Techniques Can Assist Across a Variety of Stages in Sea Ice Applications
Clare Eayrs , Won Sang Lee, Emilia Jin, Jean-François Lemieux, François Massonnet, Martin Vancoppenolle, Lorenzo Zampieri, Luke G. Bennetts, Ed Blockley, Eui-Seok Chung, Alexander D. Fraser, Yoo-geun Ham, Jungho Im, Baek-min Kim, Beong-Hoon Kim, Jinsuk Kim, Joo-Hong Kim, Seong-Joong Kim, Seung Hee Kim, Anton Korosov, Choon-Ki Lee, Donghyuck Lee, Hyun-Ju Lee, Jeong-Gil Lee, Jiyeon Lee, Jisung Na, In-woo Park, Jikang Park, Xianwei Wang, Shiming Xu, and Sukyoung Yun
Forty-two researchers from South Korea, China, Australia, Canada, Belgium, France, Italy, and Norway were invited to discuss recent advances in sea ice modeling in order to situate future domestic research at the cutting edge of ongoing sea ice research in the international arena during a three-day (24–26 October 2023) workshop in Gwangmyeong, South Korea. Sea ice modeling is an emerging research topic in South Korea responding to 1) an operational need to understand sea ice variability at daily to seasonal time scales to support field campaigns and facilitate access to polar infrastructure and 2) a scientific need to understand sea ice variability at seasonal to longer time scales in terms of cryospheric change and future projections. Presentations from 21 invited speakers set the stage for four discussion sessions that were centered around four overriding themes:
1) emerging model technologies;
2) the role of artificial intelligence (AI) in sea ice applications;
3) model/observation integration;
4) future research needs and opportunities.
PDF: P2024_11