The article “Improving surface melt estimation over the Antarctic Ice Sheet using deep learning: a proof of concept over the Larsen Ice Shelf” was published as a PROTECT publication.
Read here the main results:
- Accurately estimating surface melt volume of the Antarctic Ice Sheet (AIS) is challenging and has hitherto relied on climate modelling, or on observations from satellite remote sensing. Each of these methods has its limitations, especially in regions with high surface melt.
- Here we develop a framework correcting the model-observation mismatch of surface melt in the AIS with a deep learning model, which utilizes inputs from the physically based model, the regional atmospheric climate model version 2.3p2, and remote sensing albedo observations.
- As a result, our study demonstrates the opportunity to improve surface melt simulations using deep learning combined with satellite albedo observations.
Link to the paper: https://tc.copernicus.org/articles/15/5639/2021/
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