DOI: 10.5281/zenodo.5769661
title: Improving surface melt estimation over the Antarctic Ice Sheet using deep learning: a proof of concept over the Larsen Ice Shelf

description: Hu, Z., Kuipers Munneke, P., Lhermitte, S., Izeboud, M., and van den Broeke, M.: Improving Surface Melt Estimation over Antarctica Using Deep Learning: A Proof-of-Concept over the Larsen Ice Shelf, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2021-102, in review, 2021. ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- <strong>(1) MLP_model_surface_melt_corr.h5</strong> is the developed MLP model used for correcting RACMO2 surface melt. <strong>(2) RACMO2_surface_melt_corr_MLP_AWS14.xlsx </strong>corrected surface melt [mm w.e. per day] from RACMO2 at AWS 14 during austral summers 2001 - 2016. The model inputs are (1) the simulated albedo, (2) the albedo difference between the observed and simulated albedo, (3) air temperature at 2m, (4) incoming shortwave radiation, (5) downwelling longwave radiation, (6) simulated surface melt, (7) Boolean melt flag, (8) surface melt difference to the previous day, and (9) record date as day of the year. <strong>(3) RACMO2_surface_melt_corr_MLP_AWS17.xlsx </strong>The same as point 2 but for AWS 17 <strong>(4) RACMO2_surface_melt_corr_MLP_AWS18.xlsx </strong>The same as point 2 but for AWS 18 Note: Data 2-4 are corrected RACMO2 simulations of surface melt at the pixels in RACMO2 27 km grid corresponding to AWS 14, 17, and 18 locations. They are not AWS observations. ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- <strong>Related data set:</strong> MODIS/Terra Surface Reflectance Daily L2G Global 1 km and 500 m SIN Grid product is available via the Land Processes Distributed Active Archive Center (LP DAAC) (https://doi.org/10.5067/MODIS/MOD09GA.006, last access: 3 December 2021). MODIS/Terra+Aqua Albedo Daily L3 Global 500 m SIN Grid product is also available via LP DAAC (https://doi.org/10.5067/MODIS/MCD43A3.006, last access: 3 December 2021). Sentinel-1 images are provided by the European Space Agency (ESA) (https://sentinel.esa.int/web/sentinel/sentinel-data-access, last access: 3 December 2021). Automatic weather station observations from AWS 14, 17, and 18 are available via https://doi.pangaea.de/10.1594/PANGAEA.910473 (last access: 3 December 2021). RACMO2 simulations (https://www.projects.science.uu.nl/iceclimate/models/antarctica.php#2-1, last access: 3 December 2021) are provided by van Wessem et al. (2018) which are available on request to the original authors. ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- <strong>You should also refer to and cite the following paper:</strong> Hu, Z., Kuipers Munneke, P., Lhermitte, S., Izeboud, M., and van den Broeke, M.: Improving Surface Melt Estimation over Antarctica Using Deep Learning: A Proof-of-Concept over the Larsen Ice Shelf, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2021-102, in review, 2021.