A Spatiotemporal Multiscale Deep Learning Model for Subseasonal Prediction of Arctic Sea Ice-Reference-Cited by-同舟云学术

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A Spatiotemporal Multiscale Deep Learning Model for Subseasonal Prediction of Arctic Sea Ice

Published:2024 Issue: Volume:62 Page:1-22
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Zheng Qingyu¹^ORCID,Wang Ru¹,Han Guijun¹^ORCID,Li Wei¹^ORCID,Wang Xuan¹,Shao Qi²^ORCID,Wu Xiaobo¹^ORCID,Cao Lige¹,Zhou Gongfu¹,Hu Song¹^ORCID

Affiliation:

1. Tianjin Key Laboratory for Marine Environmental Research and Service, School of Marine Science and Technology, Tianjin University, Tianjin, China

2. Fujian Key Laboratory on Conservation and Sustainable Utilization of Marine Biodiversity, Minjiang University, Fuzhou, China

Funder

National Natural Science Foundation

National Key Research and Development Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/36/10354519/10402090.pdf?arnumber=10402090

Reference61 articles.

1. Observationally-constrained projections of an ice-free Arctic even under a low emission scenario

2. Arctic system on trajectory to new, seasonally ice-free state

3. Projected changes in sea ice and the navigability of the Arctic Passages under global warming of 2 ℃ and 3 ℃

4. Assimilating SMOS sea ice thickness into a coupled ice-ocean model using a local SEIK filter

5. Sea Ice Properties in High‐Resolution Sea Ice Models

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1. SICFormer: A 3D-Swin Transformer for Sea Ice Concentration Prediction;Journal of Marine Science and Engineering;2024-08-17

2. Incorporating physical constraints in a deep learning framework for short-term daily prediction of sea ice concentration;Applied Ocean Research;2024-07

3. Retrieval of sea ice thickness from FY-3E data using Random Forest method;Advances in Space Research;2024-07

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