NANSEN ENVIRONMENTAL AND REMOTE SENSING CENTER (NO)
SuperIce aims to develop a simulator of high-resolution sea ice thickness in the Arctic to address critical questions related to sea ice predictability at seasonal timescales and its role in the Earth’s climate system.
Current satellite-based observations of sea ice thickness provide valuable data but are limited by their spatial resolution. High-resolution information is crucial for accurate predictions and understanding small-scale features such as ice leads and thin ice, which significantly impact seasonal forecasting and heat flux calculations.
To overcome these limitations, this project proposes a multi-step approach. First, a physically based sea ice model, neXtSIM, is employed to generate high-resolution synthetic sea ice thickness datasets. These synthetic datasets are then filtered to mimic the resolution of satellite products. An AI-based diffusion model is trained to super-resolve the low-resolution SIT data. Finally, the AI-based model is applied to real Earth observation (EO) data, and its results are validated against high-resolution satellite data.
The project aims to achieve several objectives: