Assimilation of satellite swaths versus daily means of sea ice concentration in a regional coupled ocean-sea ice model

Abstract. Operational forecasting systems routinely assimilate daily means of sea ice concentration (SIC) from microwave radiometers in order to improve the accuracy of the forecasts. However, the temporal and spatial averaging of the satellite individual swaths into daily means of SIC entails two main drawbacks: (i) the spatial resolution of the original product is blurred (specially critical on periods with strong sub-daily sea ice movement), and (ii) the sub-daily frequency of passive microwave observations in the Arctic is not used, providing less temporal resolution in the data assimilation (DA) analysis and therefore, in the forecast. Within the SIRANO (Sea Ice Retrievals and data Assimilation in NOrway) project, we investigate how challenge (i) and (ii) can be avoided by assimilating satellite individual swaths (Level-3 Uncollated) instead of daily means (Level-3) of SIC. To do so, we use a regional configuration of the Barents Sea (2.5 km grid) based on the Regional Ocean Modeling System (ROMS) and The Los Alamos Sea Ice Model (CICE) together with the Ensemble Kalman Filter (EnKF) as the DA system. The assimilation of individual swaths significantly improves the EnKF analysis of SIC compared to the assimilation of daily means; the Mean Absolute Difference (MAD) shows a 10 % improvement at the end of the assimilation period, and a 7 % improvement at the end of the 7-day forecast period. This improvement is caused by better exploitation of the information provided by the SIC swath data, in terms of both spatial and temporal variance, compared to the case when the swaths are combined to form a daily mean before assimilation.