Articles | Volume 18, issue 9
https://doi.org/10.5194/tc-18-3933-2024
https://doi.org/10.5194/tc-18-3933-2024
Research article
 | 
04 Sep 2024
Research article |  | 04 Sep 2024

AWI-ICENet1: a convolutional neural network retracker for ice altimetry

Veit Helm, Alireza Dehghanpour, Ronny Hänsch, Erik Loebel, Martin Horwath, and Angelika Humbert

Data sets

Convolutional neural network training dataset and results of AWI-ICENet1 retracker Veit Helm https://doi.org/10.1594/PANGAEA.964596

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Short summary
We present a new approach (AWI-ICENet1), based on a deep convolutional neural network, for analysing satellite radar altimeter measurements to accurately determine the surface height of ice sheets. Surface height estimates obtained with AWI-ICENet1 (along with related products, such as ice sheet height change and volume change) show improved and unbiased results compared to other products. This is important for the long-term monitoring of ice sheet mass loss and its impact on sea level rise.