ITTC Project

Model-Based Data Inversion to Estimate Accumulation Rate of Polar Ice Sheets

Project Award Date: 09-01-2003


Global climate change and its impact on the polar ice sheets and associated sea level rise have been subjects of much debate over the last few years. A key to predicting the effects of global climate change on polar ice sheets is the accurate determination of their mass balance. Snow accumulation rate is an important parameter in determining the ice sheet mass balance. Currently it is derived from sparsely distributed ice cores and pits with large errors particularly in the ice sheet margins. It is difficult to conduct field operations in these regions, and thus remote sensing methods are necessary to provide better spatial and temporal coverage in these areas. The University of Kansas has developed a wideband radar system (600-900 MHz) that maps near-surface internal layers to estimate the accumulation rate. The wideband radar mapped internal layers to a depth of 300 m with a resolution of 0.5 m during field trials over the Greenland ice sheet in May 2001 and May 2002.

Researchers will now develop numerically efficient model-based signal processing algorithms to invert the collected remote sensing data. They will estimate the density and thickness profiles of the near-surface internal layers by model-based data inversion, and use them to compute the snow accumulation rate.


Faculty Investigator(s): Sivaprasad Gogineni (PI)

Student Investigator(s): Vijaya Ramasami

Project Sponsors

Primary Sponsor(s): NASA-Goddard

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