A Data-Driven Deep Learning Model for Weekly Sea Ice Concentration Prediction of the Pan-Arctic During the Melting Season

• Published in: IEEE Transactions on Geoscience and Remote Sensing
• Main Authors: Ren, Yibin, Li, Xiaofeng, Zhang, Wenhao
• Format: Report
• Language: English
• Published: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC 2022
• Subjects: Deep fully convolutional networks (FCNs); recursively predicting; satellite-derived sea ice concentration (SIC); SIC prediction; temporal-spatial attention; Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology; Electrical & Electronic; CONVOLUTIONAL LONG; VARIABILITY; FORECASTS; Arctic; Nash; Sutcliffe; National Snow and Ice Data Center; Sea ice
• Online Access: http://ir.qdio.ac.cn/handle/337002/179153; https://doi.org/10.1109/TGRS.2022.3177600

This study proposes a purely data-driven model for the weekly prediction of daily sea ice concentration (SIC) of the pan-Arctic (90 N, 45 N, 180 E, 180 W) during the melting season. The model, SICNet, adopts an encoder-decoder framework with fully convolutional networks (FCNs) and can predict the SI...