Fusion of ice thickness from passive microwave data and ice ocean model for improved estimation
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Date
2015-05-01
Authors
Janjua, Harveen
Journal Title
Journal ISSN
Volume Title
Publisher
University of Waterloo
Abstract
The ice cover in the perennial region of the Arctic Circle has reduced signi cantly in
recent years. Various models are available to predict the spatial and temporal evolution
of the ice cover.Predictions from these models can be improved by incorporating satellite
observations by the technique of data assimilation.
In this thesis, ice thickness observations from Advanced Microwave Sensing Radiometer
Earth (AMSR-E) and Moderate Resolution Imaging Spectro-Radiometer (MODIS) remote
sensors 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 spatial
correlations are modelled using a di usion operator. The observation error covariance matrix
is diagonal and the observation operator is saturated to a threshold of 0.2 m for ice
thickness observations from AMSR-E because ice thickness is negatively correlated to the
polarization ratio for thin ice up to 0.2 m only. It is observed that when more observations
are available the analysis from data fusion is closer to ice charts produced by Canadian Ice
Services (CIS).
One possible application of the system developed is in areas where ships need to be
safely routed through ice infested water. This thesis presents a small example that tries
to nd the path for a ship through thick ice. The impact of fusing perturbed observations
with ice thickness data inferred from a satellite image on the ice thickness traversed and
distance travelled is investigated.
Description
Keywords
Data assimilation, sea ice thickness, background error covariance, observation operator, ship routing