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+## Impact of super resolution SIT data for seasonal sea ice predictions
+### *Yiguo Wang*
+
+Accurate seasonal prediction of Sea Ice Extent (SIE) is critical for understanding Arctic climate dynamics and holds significant societal and environmental implications. 
+The Norwegian Climate Prediction Model (NorCPM) has previously demonstrated skill in predicting pan-Arctic SIE up to 12 months ahead, primarily using ocean data and coarse-resolution Sea Ice Concentration (SIC) and Sea Ice Thickness (SIT) data. 
+However, its capacity for regional SIE prediction remains limited to a few months, depending on season and region.
+
+This study aims to improve seasonal SIE prediction by incorporating high-resolution SIC and SIT data into NorCPM. 
+The high-resolution sea ice data are classed to SIC data in each category. NorCPM assimilates the category SIC data.
+We assess the performance of this approach by conducting retrospective ensemble prediction for 2023. 
+The experiment is initialized in March and run for up to 13 months. 
+The prediction will be validated against observations and compared to the standard version of prediction using coarse-resolution sea ice data.
+
+[back to the Workshop page](https://nansencenter.github.io/superice-nersc/workshop/)