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_...
Main Authors: | , , , , |
---|---|
Format: | Other/Unknown Material |
Language: | unknown |
Published: |
Zenodo
2021
|
Subjects: | |
Online Access: | https://doi.org/10.5194/tc-2021-102 |
id |
ftzenodo:oai:zenodo.org:5764242 |
---|---|
record_format |
openpolar |
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 |
_version_ |
1810490859469144064 |