Data underlying the publication: Physically-informed super-resolution downscaling of Antarctic surface melt ...

• Main Authors: de Roda Husman, Sophie, Hu, Zhongyang, van Tiggelen, Maurice, Dell, Rebecca, Bolibar, Jordi, Lhermitte, Stef, Wouters, Bert, Kuipers Munneke, Peter
• Format: Dataset
• Language: English
• Published: 4TU.ResearchData 2024
• Subjects: Artificial Intelligence and Image Processing; FOS Computer and information sciences; Other Environmental Sciences; FOS Earth and related environmental sciences; Physical Geography and Environmental Geoscience; Climate and Climate Change; Atmosphere and Weather; Earth Sciences; Information and Computing Sciences; Environmental Sciences; Environment; Antarctica; Surface melt; Deep learning; Super-resolution; Downscaling; High-resolution; Ice shelves; Physically-informed; Time 2001-2019; Antarc*; Antarctic; Ice Shelves
• Online Access: https://dx.doi.org/10.4121/87607637-09d9-4147-8125-c85eec901a88.v1; https://data.4tu.nl/datasets/87607637-09d9-4147-8125-c85eec901a88/1

The dataset provided in this repository corresponds to the original data used in the publication by De Roda Husman et al. (2024) titled "Physically-informed super-resolution downscaling of Antarctic surface melt" (DOI to be announced!). The dataset, named SUPREME, contains a record of surface melt (in meters water equivalent per year) on an Antarctic-wide scale. It offers surface melt data at a high spatial resolution of 5.5 kilometers for each melt year (1 July - 30 June), covering the period from 2001 to 2019. Our method downscales Antarctic surface melt from the regional climate model Regional Atmospheric Climate Model (RACMO, v2.3p2), employing a physically-informed super-resolution architecture. The super-resolution model integrates physical information derived from remote sensing data, specifically surface albedo and elevation. ...