Frequency-Compensated Network for Daily Arctic Sea Ice Concentration Prediction
This work addresses the critical need for improved sea ice concentration prediction in the Arctic, which impacts global ecosystem health and navigation safety, representing an incremental advancement with a novel method for a known bottleneck.
The paper tackles the problem of accurately forecasting daily Arctic sea ice concentration by addressing challenges in capturing long-term feature dependencies and preserving high-frequency details, resulting in a Frequency-Compensated Network (FCNet) that achieves refined prediction of edges and details as verified through experiments on a satellite-derived dataset.
Accurately forecasting sea ice concentration (SIC) in the Arctic is critical to global ecosystem health and navigation safety. However, current methods still is confronted with two challenges: 1) these methods rarely explore the long-term feature dependencies in the frequency domain. 2) they can hardly preserve the high-frequency details, and the changes in the marginal area of the sea ice cannot be accurately captured. To this end, we present a Frequency-Compensated Network (FCNet) for Arctic SIC prediction on a daily basis. In particular, we design a dual-branch network, including branches for frequency feature extraction and convolutional feature extraction. For frequency feature extraction, we design an adaptive frequency filter block, which integrates trainable layers with Fourier-based filters. By adding frequency features, the FCNet can achieve refined prediction of edges and details. For convolutional feature extraction, we propose a high-frequency enhancement block to separate high and low-frequency information. Moreover, high-frequency features are enhanced via channel-wise attention, and temporal attention unit is employed for low-frequency feature extraction to capture long-range sea ice changes. Extensive experiments are conducted on a satellite-derived daily SIC dataset, and the results verify the effectiveness of the proposed FCNet. Our codes and data will be made public available at: https://github.com/oucailab/FCNet .