Improving surface melt estimation over the Antarctic Ice Sheet using deep learning: a proof of concept over the Larsen Ice Shelf

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. --- (1) MLP_...

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Main Authors: Zhongyang Hu, Peter Kuipers Munneke, Stef Lhermitte, Maaike Izeboud, Michiel van den Broeke
Format: Other/Unknown Material
Language:unknown
Published: Zenodo 2021
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Online Access:https://doi.org/10.5194/tc-2021-102
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spelling ftzenodo:oai:zenodo.org:5764242 2024-09-15T17:43:43+00:00 Improving surface melt estimation over the Antarctic Ice Sheet using deep learning: a proof of concept over the Larsen Ice Shelf Zhongyang Hu Peter Kuipers Munneke Stef Lhermitte Maaike Izeboud Michiel van den Broeke 2021-12-07 https://doi.org/10.5194/tc-2021-102 unknown Zenodo https://zenodo.org/communities/protect-slr https://doi.org/10.5194/tc-2021-102 oai:zenodo.org:5764242 info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode Antarctic Surface Melt Deep Learning info:eu-repo/semantics/other 2021 ftzenodo https://doi.org/10.5194/tc-2021-102 2024-07-25T08:30:36Z 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. --- (1) MLP_model_surface_melt_corr.h5 is the developed MLP model used for correcting RACMO2 surface melt. (2) RACMO2_surface_melt_corr_MLP_AWS14.xlsx 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. (3) RACMO2_surface_melt_corr_MLP_AWS17.xlsx The same as point 2 but for AWS 17 (4) RACMO2_surface_melt_corr_MLP_AWS18.xlsx 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. --- Related data set: 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 ... Other/Unknown Material Antarc* Antarctic Antarctica Ice Sheet Ice Shelf Larsen Ice Shelf Zenodo
institution Open Polar
collection Zenodo
op_collection_id ftzenodo
language unknown
topic Antarctic
Surface Melt
Deep Learning
spellingShingle Antarctic
Surface Melt
Deep Learning
Zhongyang Hu
Peter Kuipers Munneke
Stef Lhermitte
Maaike Izeboud
Michiel van den Broeke
Improving surface melt estimation over the Antarctic Ice Sheet using deep learning: a proof of concept over the Larsen Ice Shelf
topic_facet Antarctic
Surface Melt
Deep Learning
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. --- (1) MLP_model_surface_melt_corr.h5 is the developed MLP model used for correcting RACMO2 surface melt. (2) RACMO2_surface_melt_corr_MLP_AWS14.xlsx 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. (3) RACMO2_surface_melt_corr_MLP_AWS17.xlsx The same as point 2 but for AWS 17 (4) RACMO2_surface_melt_corr_MLP_AWS18.xlsx 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. --- Related data set: 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 ...
format Other/Unknown Material
author Zhongyang Hu
Peter Kuipers Munneke
Stef Lhermitte
Maaike Izeboud
Michiel van den Broeke
author_facet Zhongyang Hu
Peter Kuipers Munneke
Stef Lhermitte
Maaike Izeboud
Michiel van den Broeke
author_sort Zhongyang Hu
title Improving surface melt estimation over the Antarctic Ice Sheet using deep learning: a proof of concept over the Larsen Ice Shelf
title_short Improving surface melt estimation over the Antarctic Ice Sheet using deep learning: a proof of concept over the Larsen Ice Shelf
title_full Improving surface melt estimation over the Antarctic Ice Sheet using deep learning: a proof of concept over the Larsen Ice Shelf
title_fullStr Improving surface melt estimation over the Antarctic Ice Sheet using deep learning: a proof of concept over the Larsen Ice Shelf
title_full_unstemmed Improving surface melt estimation over the Antarctic Ice Sheet using deep learning: a proof of concept over the Larsen Ice Shelf
title_sort improving surface melt estimation over the antarctic ice sheet using deep learning: a proof of concept over the larsen ice shelf
publisher Zenodo
publishDate 2021
url https://doi.org/10.5194/tc-2021-102
genre Antarc*
Antarctic
Antarctica
Ice Sheet
Ice Shelf
Larsen Ice Shelf
genre_facet Antarc*
Antarctic
Antarctica
Ice Sheet
Ice Shelf
Larsen Ice Shelf
op_relation https://zenodo.org/communities/protect-slr
https://doi.org/10.5194/tc-2021-102
oai:zenodo.org:5764242
op_rights info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
op_doi https://doi.org/10.5194/tc-2021-102
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