The ice cover in the perennial region of the Arctic Circle has reduced signi cantly inrecent years. Various models are available to predict the spatial and temporal evolutionof the ice cover.Predictions from these models can be improved by incorporating satelliteobservations by the technique of data assimilation.In this thesis, ice thickness observations from Advanced Microwave Sensing RadiometerEarth (AMSR-E) and Moderate Resolution Imaging Spectro-Radiometer (MODIS) remotesensors are fused with that from an ice-ocean model using an optimal interpolation technique.It is assumed that the background error covariance matrix is static and the spatialcorrelations are modelled using a di usion operator. The observation error covariance matrixis diagonal and the observation operator is saturated to a threshold of 0.2 m for icethickness observations from AMSR-E because ice thickness is negatively correlated to thepolarization ratio for thin ice up to 0.2 m only. It is observed that when more observationsare available the analysis from data fusion is closer to ice charts produced by Canadian IceServices (CIS).One possible application of the system developed is in areas where ships need to besafely routed through ice infested water. This thesis presents a small example that triestond the path for a ship through thick ice. The impact of fusing perturbed observationswith ice thickness data inferred from a satellite image on the ice thickness traversed anddistance travelled is investigated.
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Fusion of ice thickness from passive microwave data and ice ocean model for improved estimation