Assimilation of ice and water observations from SAR imagery to improve estimates of sea ice concentration

In this paper the assimilation of binary observations calculated from synthetic aperture radar (SAR) images of sea ice is investigated. Ice and water observations are obtained from a set of SAR images by thresholding ice and water probabilities calculated using a supervised maximum likelihood estimator (MLE). These ice and water observations are then assimilated in combination with ice concentration from passive microwave imagery for the purpose of estimating sea ice concentration. Due to the fact that the observations are binary, consisting of zeros and ones, while the state vector is a continuous variable (ice concentration), the forward model used to map the state vector to the observation space requires special consideration. Both linear and nonlinear forward models were investigated. In both cases, the assimilation of SAR data was able to produce ice concentration analyses in closer agreement with image analysis charts than when assimilating passive microwave data only. When both passive microwave and SAR data are assimilated, the bias between the ice concentration ∗K. Andrea Scott and Zahra Ashouri are with the Department of Systems Design Engineering at the University of Waterloo, Waterloo, Canada (email: [email protected]) †M. Buehner is with the Data Assimilation and Satellite Meteorology Research Section at Environment Canada, Dorval, Canada ‡Lynn Pogson and Tom Carrieres are with Marine and Ice Services, Environment Canada, Ottawa, Canada.