机器学习技术的进步可为海冰应用的各个阶段提供帮助

IF 6.9 1区地球科学 Q1 METEOROLOGY & ATMOSPHERIC SCIENCES

Bulletin of the American Meteorological Society Pub Date : 2024-02-01 DOI:10.1175/bams-d-23-0332.1

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, Sukyoung Yun

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引用次数: 0

摘要

"美国气象学会于 2024 年 2 月 1 日发表了《机器学习技术的进步可为海冰应用的各个阶段提供帮助》一文。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Advances in machine learning techniques can assist across a variety of stages in sea ice applications

"Advances in machine learning techniques can assist across a variety of stages in sea ice applications" published on 01 Feb 2024 by American Meteorological Society.

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来源期刊

Bulletin of the American Meteorological Society 地学-气象与大气科学

CiteScore

9.80

自引率

6.20%

发文量

231

审稿时长

6-12 weeks

期刊介绍： The Bulletin of the American Meteorological Society (BAMS) is the flagship magazine of AMS and publishes articles of interest and significance for the weather, water, and climate community as well as news, editorials, and reviews for AMS members.