pan-Arctic region 60-day forcast data and remote sensing ground truth

output mat: 60-day SR-SICFormer forecasting datalabel mat: 60-day remote sensing ground truthnan_mat: land-sea maskmaks: lat-lon maskThis study proposes a data-driven SR-SICFormer model combined with a ConIce loss function, aiming to address these issues mentioned above. SR-SICFormer is a novel Transformer-based model that combines image super-resolution reconstruction with spatiotemporal feature learning, enabling high-resolution daily SIC predictions for the next 15 days to 2 months. The ConIce loss function incorporates SIC conservation, correcting SIE and internal concentration predictions by integrating physical constraints from thermodynamic and dynamic sea ice processes. Compared to existing models, this approach performs excellently in fine-scale, long-term, extended-range daily SIC prediction. Furthermore, for the 60-day forecast task during the melting season, SR-SICFormer continues to perform exceptionally well.